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Integrating media content analysis, reception analysis, and media effects studies

Ralf schmälzle.

1 Department of Communication, Michigan State University, East Lansing, MI, United States

Richard Huskey

2 Department of Communication, University of California, Davis, Davis, CA, United States

3 Cognitive Science Program, University of California, Davis, Davis, CA, United States

4 Center for Mind and Brain, University of California, Davis, Davis, CA, United States

Every day, the world of media is at our fingertips, whether it is watching movies, listening to the radio, or browsing online media. On average, people spend over 8 h per day consuming messages from the mass media, amounting to a total lifetime dose of more than 20 years in which conceptual content stimulates our brains. Effects from this flood of information range from short-term attention bursts (e.g., by breaking news features or viral ‘memes’) to life-long memories (e.g., of one’s favorite childhood movie), and from micro-level impacts on an individual’s memory, attitudes, and behaviors to macro-level effects on nations or generations. The modern study of media’s influence on society dates back to the 1940s. This body of mass communication scholarship has largely asked, “what is media’s effect on the individual?” Around the time of the cognitive revolution, media psychologists began to ask, “what cognitive processes are involved in media processing?” More recently, neuroimaging researchers started using real-life media as stimuli to examine perception and cognition under more natural conditions. Such research asks: “what can media tell us about brain function?” With some exceptions, these bodies of scholarship often talk past each other. An integration offers new insights into the neurocognitive mechanisms through which media affect single individuals and entire audiences. However, this endeavor faces the same challenges as all interdisciplinary approaches: Researchers with different backgrounds have different levels of expertise, goals, and foci. For instance, neuroimaging researchers label media stimuli as “naturalistic” although they are in many ways rather artificial. Similarly, media experts are typically unfamiliar with the brain. Neither media creators nor neuroscientifically oriented researchers approach media effects from a social scientific perspective, which is the domain of yet another species. In this article, we provide an overview of approaches and traditions to studying media, and we review the emerging literature that aims to connect these streams. We introduce an organizing scheme that connects the causal paths from media content → brain responses → media effects and discuss network control theory as a promising framework to integrate media content, reception, and effects analyses.

1. Introduction

Media messages permeate our lives; they stimulate rich neurocognitive responses and serve important, much-debated functions within modern information societies. On average, we spend about 8 h per day consuming media ( Twenge et al., 2019 ). Effects of exposure to media range from micro-level impacts on an individual’s memory, attitudes, and behaviors to macro-level effects on nations or generations ( Bryant and Oliver, 2008 ; Larzabal et al., 2017 ). In short, we live in a world where media content flows through our brains much like blood through our veins.

In recent years, researchers have begun to use theories and methods from neuroscience to examine the neural mechanisms of media effects ( Weber, 2013 ; Schmälzle and Grall, 2020a , b ; Schmälzle, 2022 ). This approach is motivated by the fact that the brain is the biological organ underlying all media effects, regardless of whether the study is about movies, narratives (books and audiobooks), or other media types. After all, if a message did not arrive in a recipient’s brain, it could not have any effect. This notion of the brain as the central processor of media content is undisputed. It is what motivates the use of neuroimaging to study brain responses to media in the hope of revealing the actual mechanisms that underlie media’s effects on perception, attention, comprehension, affect - or whatever the focal topic of a concrete neuroscientific investigation that uses media may be.

However, while the promise of neuroimaging in this area is generally recognized, the complexity of the enterprise cannot be underestimated. Media are a highly complex kind of ‘stimulus’, actually, they are a sequence of a multitude of individual stimuli. Moreover, media evoke multiplex brain responses. And finally, media result in a mosaic of consequences - from short-term to long-term effects and from individual to collective outcomes.

Given this complexity, it is no surprise that multiple disciplines exist at the nexus of media and the brain. Researchers in the fields of communication and media studies have largely focused on issues related to media content and the effects of exposure to such content ( Figure 1 , left; Riff et al., 2014 ; Neuendorf, 2017 ). By comparison, psychology and media psychology investigate the cognitive processes that subserve media processing and effects ( Figure 1 , middle; Weber et al., 2008 ; Lang and Ewoldsen, 2010 ). By comparison, the cognitive sciences and cognitive neurosciences primarily use media as a tool for studying cognition and the brain ( Figure 1 , right; Spiers and Maguire, 2007 ; Hasson and Honey, 2012 ; Sonkusare et al., 2019 ; Vanderwal et al., 2019 ).

Connecting siloed fields of media effects, media processes, and media neuroscience. Traditionally, these topics have been studied by different academic disciplines.

Of course, these broad generalizations mask substantial disciplinary and topical heterogeneity such that inquiry surrounding media and the brain is a bit reminiscent of people feeling an elephant in a dark room ( Figure 2 ): In this parable, each person brings their own experience and perspective to the endeavor of identifying the elephant, but each person is only able to feel just one small part of the large animal. In the same way, many different perspectives about media and the brain coexist - all valuable in and of themselves - but there is a lack of integration and a lot of confusion. In fact, early career researchers who consider working at the intersection of media and the brain will find themselves in a complex theoretical and methodological landscape that spans disciplines and even paradigms from the humanities, traditional STEM disciplines, and the social sciences. This state of affairs can make it difficult to see the proverbial elephant in the room, and one can almost ask oneself: If “naturalistic neuroimaging” or “movie fMRI” is the answer, what is the question (see Kosslyn, 1999 )?

An elephant in a dark room. In this classic parable, people investigate an elephant in a dark room. Each can only feel part of the elephant and cannot identify the whole. Misunderstanding ensues.

With this in mind, this article suggests a conceptual framework to integrate these disparate research streams of media effects, media processing, and media neuroscience. We begin by introducing and discussing each area and provide the logical division into content analysis , reception analysis , and effects analysis as an organizing scheme. Then, we suggest network control theory (NCT) as a framework with the potential for integrating these siloed traditions. We believe this framework can shed light on the elephant in the dark room and reveal causal mechanisms by which the content of media messages affects brain responses and how the resulting message effects in single individuals aggregate into media effects in large populations.

2. The arrow of causality: from media content to reception responses to media effects

So far, we have discussed how different areas of disciplinary inquiry are largely organized around levels of analysis (media effects on individuals and society, media processing within individuals, neural responses within individuals). As this section will show, a framework organized around levels of analysis does not cleanly map onto a causal path that begins with exposure to media content and ends with media effects. In this section, we give an overview of our conceptual model that starts with media as a stimulus (a brief text message, an audiovisual movie, a social-media video clip, an audiobook) containing conceptual content that is analyzed by the brain and results in what has traditionally been called media or message effects ( Figure 3 ).

The arrow of causality from media content to reception responses to media effects. The bottom left panel illustrates how content analysis quantifies content (e.g., for use as a design matrix), allowing to map out brain systems responding to specific content elements. These can then be linked to effects of media consumption. Of note, reception responses to an incoming (causal) media stimulus can also be modulated by receiver state, background knowledge, beliefs, and so forth.

2.1. Assaying the ingredients: media content analysis

Media are inherently content-rich and, therefore, complex. To demonstrate by selecting one possible example, consider movies. Among the most popular types of media, movies comprise multimodal content (images and soundtracks) that include a wealth of semantic and social-pragmatic dimensions that vary over time. The term movie emerged as a shortcut for moving images - essentially by stitching together photographs in rapid succession. For example, a typical Netflix HD movie streams about 3-7GB of data, containing over 100,000 individual frames, each containing many pixels. It becomes clear that if we consider the pixel-level information of any given movie, the information contained in a movie quickly reaches billions. These flickering pixels form the manifest content of the movie as it emerges from your TV screen.

Clearly, though, looking at movies as a multitude of pixels misses the point - just as it makes little sense to use a microscope to examine ink-saturated paper when reading a fiction book. Typically, when discussing movies, we mean their higher-order information, such as narrative and social-cognitive content. Clearly, we also do not remember the surface-level information (the pixels), but we recall and retell what happens to characters and the overall trajectory of a plot (like heroes and villains, or a rags-to-riches story, etc.; see Kintsch, 1998 for a similar argument about language comprehension).

Between the pixels as the lowest-level content features and the macro- or plot-level content features lie numerous intermediary-level features. For example, consider now the soundtrack of a movie (instead of the video track containing the pixels). At a lower level, a movie’s soundtrack is characterized by physical properties like its constituent amplitude, frequency content, etc. However, this all is embedded in a nested, hierarchical structure: Stretches of sound encode particular phonemes, which in turn represent words, words are nested in sentences, and a couple of sentences by one speaker are typically followed by a response from another speaker, reflecting a dialogue in a scene. The same case can be made for visual content (e.g., Hasson et al., 2008a ). Thus, it becomes clear that the content of a movie - a deceptively simple singular word - actually encompasses multiple content elements that can be organized along a hierarchy of abstraction (see Figures 3 , ​ ,4). 4 ). Which specific content element is of interest to researchers often depends on their home discipline - just like in the elephant in the dark room parable. Arguably, since movies are largely created and consumed to entertain, the most relevant level is the plot level. Still, it is clear that all lower levels (sounds, words, sentences, paragraphs or pixels, images, scenes) are necessary to convey the plot-level content of a movie (or a book or whatever the media format). 1

Linking hierarchies of content with matching reception mechanisms and integration with media effects. Content is a deceptively singular word, but it encompasses multiple layers - from manifest (e.g., pixels) to latent content (e.g., subtext, story morals). Understanding content as a network of information layers (left panel) allows for its comprehensive quantification, facilitating the identification of corresponding reception mechanisms. In a similar fashion, we can also integrate individual-specific neurocognitive processes during media reception to media effects and social levels.

One way of quantifying all this higher-order and often latent content (or subtext) is through a procedure known as content analysis ( Krippendorff, 2004 ; Riff et al., 2014 ; Neuendorf, 2017 ). Historically, content-analyzing movies and media more broadly was an arduous task. For example, the famous National Television Violence Study ( Federman, 1995 ) relied on manual labor from human coders to annotate over 10,000 h of content over a three-year period. Most content analyses are smaller in scale, but the effort required is still a key bottleneck ( Greenberg and Atkin, 1980 ; Masters et al., 1991 ; Hahn et al., 2017 ). Consequently, classical content analyses usually feature sparse sampling frames (e.g., the first 10 min of content from a sample of movies) that often incompletely describe the entire media corpus. Another, not unrelated problem, is that there is often substantial error in human annotations, which can be quite difficult or even impossible to resolve in some circumstances ( Weber et al., 2018 ).

Advances in computational analysis make this task much faster, scalable, feasible, and accurate. The catch, however, is that computational analyses are currently only able to quantify manifest lower- and mid-level features of the content-abstraction hierarchy. We assume that readers will be familiar with the explosion of research on natural language processing and computer vision. As of 2023, computers can automatically quantify many sound characteristics (such as energy and pitch) and even transcribe spoken content into writing ( Radford et al., 2022 ), and in the visual domain, they excel at quantifying image properties, recognizing objects, or even detecting actions in image sequences ( Rohrbach et al., 2017 ).

These advances can be leveraged to analyze media content in a rigorously quantitative fashion and at scale. For instance, researchers have used face-detection systems to detect characters in movies or natural language processing (NLP) techniques to identify characters from scripts, both of which can be used to create character networks (e.g., communities of characters that co-occur in the same scenes; Hopp et al., 2020 ; Baldwin and Schmälzle, 2022 ; Malik et al., 2022 ) or to create time-locked explanatory variables for neuroimaging analyses. Or researchers have used NLP techniques to study moral language in media ( Weber et al., 2018 ). Perhaps the most systematic yet still young approach in this area is the NeuroScout platform and the related pliers python package ( McNamara et al., 2017 ; De la Vega et al., 2022 ). NeuroScout provides easy access to machine-learning methods capable of automatically extracting hundreds of features that range from the very concrete (like root mean squared amplitude of the sound signal) to more abstract ones (like concept-level image tags from Google’s or Clarif.ai’s computer vision systems).

Overall, computational tools for extracting content features are immensely promising for studying content in a rigorously quantitative and automatic manner. However, we must acknowledge that even the most advanced machine-learning systems fail to achieve human-level understanding ( Marcus and Davis, 2019 ; McClelland et al., 2020 ). In fact, even though impressive progress is made in modeling so-called common sense knowledge, current systems still fall short in many regards when it comes to coding abstract categories of content, such as sarcasm or humor, or detecting sequential narrative information related to story schemata or scripts, or visual action depictions ( Vicol et al., 2018 ; Choi et al., 2021 ; Zellers et al., 2021 ). Taken together, automatic approaches can excel at quantifying lower- and mid-level properties of content, but they still face a barrier ( Karpathy, 2012 ; Mitchell, 2020 ) when it comes to analyzing higher-order media content.

Said differently, the content of a movie (or other media) can be analyzed very concretely and efficiently in terms of physical properties, such as brightness and contrast, and also for intermediate levels, like the presence of objects, such as guns and faces. At a yet more abstract level, however, the movie has an event structure (separated by cuts) and a plot that conveys the overall narrative. This type of abstract content is currently much harder to quantify, even with advanced machine learning and NLP techniques. Indeed, we often find ourselves resorting to psychological terms to describe content-level properties whose “ingredients” in content remain somewhat unclear, such as the ‘suspensefulnes’ of a movie to describe its potential to elicit suspense (see Cummins, 2000 ). However, it is clear that these content elements matter for a movie’s impact on viewers’ brain responses. 2

The upshot of all this is that if our goal is to understand the effects of content of the brain, then a diverse and rapidly improving toolkit for quantifying media content already exists for more concrete features, and we can rely on traditional human content annotations to quantify higher-level aspects of content that are still beyond the capabilities of computational tools. In the next section, we discuss how this quantified content is the key to deciphering the brain responses. 3

2.2. Reception analysis: how brains respond to media

It is clear that media content’s arrival in the brain sets forth a cascade of reactions ( Kepplinger, 1989 ; Bryant and Zillmann, 1990 ; Potter and Bolls, 2012 ; Schmälzle and Grall, 2020a ). Just like we started our analysis of movie content at the pixel level, we can begin our quest into the brain at what can be considered the neural counterpart of the pixel: an individual cell (rod/cone) in the receiver’s retina that gets stimulated by light and converts the televised movie’s signal into a neural impulse. Due to space limits, we cannot trace this signal’s neural itinerary in fine detail, but a rough sketch goes like this: From the retina, information travels along the optic nerve into the thalamus, gets relayed in the lateral geniculate nucleus, and arrives via the optic radiation in the primary visual cortex ( Mesulam, 1998 ; Chalupa and Werner, 2003 ; Fuster, 2003 ; Poeppel et al., 2020 ), and so forth. The seminal work by Hubel & Wiesel on receptive fields provides perhaps the most concrete examination of content-extractors (or feature detectors) in the brain; that is, neurocognitive mechanisms that match certain content elements, like oriented lines, edges, or motion ( Hubel and Wiesel, 1962 ).

However, just like with the analogy of trying to read a book with a microscope, studying movies as purely visual stimuli that activate the retina and V1-edge-detectors runs the risk of missing the point: We clearly do not watch movies simply to obtain visual stimulation, and we do not read or listen to books solely because we like letters and sounds, or processing any of the intermediary representations like objects, action sequences, or speech. Instead, we typically use media to engage their higher-level, albeit more difficult to quantify, content.

Few researchers would question the statement that “content is key” for understanding how media impact the brain. However, looking into the emerging literature on media and neuroscience, it is apparent that content is often simply ignored. In some ways, this is understandable. Modern neuroscience already requires extensive training in neuroanatomy, physiology, physics, statistics, engineering and signal processing, psychology, philosophy, programming, high-performance computing, and so on, such that there is little time left to also train in scholarship on complicated and sometimes even poorly specified content features that come with media stimuli (e.g., narratives, characters). Similarly, when using media as stimuli, it is not always so clear exactly what needs to be accounted for in either experimental design or statistical analysis. Should we account for luminance? Sound amplitude? The presence of faces? If so, how? The difficult answers and unappealing tradeoffs associated with these questions have spurred clever solutions optimized for designing around all of this complexity. Such approaches include calculating intersubject correlations (ISC; Hasson et al., 2004 , 2008b ), or borrowing other methods from resting-state fMRI, dynamic causal analyses (e.g., Granger causality or DCM methods), or introducing other advanced tools to decipher entangled brain responses ( Di and Biswal, 2020 ; Van Der Meer et al., 2020 ; Busch et al., 2022 ).

It is not our goal to criticize this research as it has already led to important new discoveries about the brain. Nevertheless, these approaches are largely content-blind. We argue that without an equal appreciation of the content, this endeavor will yield only limited insights (see Okdie et al., 2014 , for a parallel argument about media psychology). After all, it is clearly the content where the causal arrow originates that evokes the brain responses. Thus one should devote equal sophistication to content analysis as to reception analysis (i.e., analysis of neural or other types of data).

Not all neuroimaging analyses are content-blind, though. In fact, some go to great lengths to quantify or manipulate content. However, we claim that even these approaches are still limited when it comes to identifying the kinds of higher-level content elements that prompt conceptual and affective reactions to media and drive media selection and consumption behavior. For example, in studies of natural vision, movies are increasingly adopted as stimuli because they depict relatively natural scenes (except for things like cuts and blends; Hasson et al., 2008c ; Çukur et al., 2013 ). Such studies also tend to do a great job quantifying aspects of content that are relevant to their area of study, like meticulously annotating visual content properties such as contrast, individual objects, and so forth, or manipulating content via scrambling ( Hasson et al., 2008c ; Çukur et al., 2013 ; Huth et al., 2016b ; Wen et al., 2017 ). Studies like these make great use of movies as an experimental stimulus, and they can serve as role models for how content analysis can inform reception analysis. These studies represent the kind of work that examines carefully one specific part of the proverbial elephant (e.g., visual processing). As such, they are extremely valuable for understanding vision. However, although vision clearly is central to movie viewing and the entertainment experiences it produces, vision alone is only one piece of a larger mosaic of movie-evoked brain responses. Moreover, to the extent that higher-level content properties (such as suspense fluctuations in a movie) impact attention, it is probably the case that the measurements might be biased (e.g., Van Berkum et al., 2009 ; Gantman and Van Bavel, 2014 ; Schmälzle and Grall, 2020a , b ).

Much like the visual neurosciences have begun to adopt media as a more naturalistic alternative to traditional stimuli, neurolinguistics has also begun to embrace media (like stories, audiobooks, and movies with dialogue). In the early days of neuroimaging, language studies were notoriously artificial single-word studies (e.g., using sparse sampling event-related designs). The trend towards more naturalistic neuroimaging prompted an upsurge of studies using natural, running speech as stimuli - often taken from audiobooks and similar story-based media formats. Like their counterparts in the visual domain, neurolinguistics studies do a great job at annotating word-level linguistic properties, such as word length, frequency, syntactic role, or even basic semantic aspects (e.g., GloVe or Word2Vec embeddings) and relating these to the stimulus-evoked brain activity in a forward-inference manner ( Lerner et al., 2011 ; Huth et al., 2016a ; Broderick et al., 2018 ). As this trend advanced, the stimulus characteristics that were coded became more nuanced; for instance, it has been demonstrated that decoding results become better if one uses sentence-based embeddings as opposed to word-level-only embeddings. However, the key point is that these neurolinguistic studies also struggle to consider content elements that go beyond the linguistic level ( McClelland et al., 2020 ; Arana et al., 2023 ). However, just like reading a book with a microscope, we claim that we do not consume stories because they provide linguistic stimulation. Rather, it is the supralinguistic content and the responses this evokes that are critical: stories entertain, satisfy social needs, pique our curiosity, and so forth. 4

A still small but growing number of studies attempt to link higher-level media content, which influences post-perceptual processes like attention, semantic comprehension, and particularly affective and social-cognitive responses, to brain responses ( Hasson et al., 2008b ; Yeshurun et al., 2017 ; Richardson et al., 2018 ; Tikka et al., 2018 ; Nguyen et al., 2019 ; Schmälzle and Grall, 2020a , b ; Baek and Parkinson, 2022 ; Grady et al., 2022 ). For example, it is well known that movies are highly social in content and that their social and affective content is key to why we engage with them in the first place. In fact, movies are bursting with depictions of social interactions, including love, aggression, betrayal, etc. - and viewers take an intense interest in the fate of characters ( Bryant and Zillmann, 1990 ; Oatley, 2002 ; Tannenbaum, 2014 ). Because of this, movies and other fiction-based media are almost ideal tools for studying social cognitive processes like empathy, perspective-taking, trait inferences, and so on ( Vorderer, 1996 ; Klimmt et al., 2006 ). These characteristics of movies are increasingly recognized by neuroimagers interested in the neural basis of such processes ( Salmi et al., 2013 ; Byrge et al., 2015 ; Richardson et al., 2018 ; Nguyen et al., 2019 ; Broom et al., 2021 ; Chang et al., 2021 ), even beyond human neuroimaging ( Mantini et al., 2012 ; Sliwa and Freiwald, 2017 ).

Similarly, these social-cognitive responses to movies are intimately interwoven with affective reactions. For instance, viewer affect reliably tracks character victories and failures, good fortune and suffering, trials and tribulations such that audiences experience strong participatory responses (e.g., goosebumps during the hero’s victory at the end, crying during ‘all is lost’ moments when it seems that the hero is doomed to failure). In fact, it has been said that Hollywood is - at its core - a giant experimental psychology lab specializing in creating emotional stimuli that can effectively affect mass audiences. Likewise, Alfred Hitchcock, the famous master of suspense, described his profession as “based on an exact science of audience reactions” ( Hasson et al., 2008a ). Because of this capacity, entire genres of movies are devoted to catering to certain segments of the affect spectrum, and a few neuroimaging studies have explored such phenomena. For instance, suspense movies take audiences on an emotional rollercoaster that blends future-oriented cognitions like hope and anxiety ( Bezdek et al., 2017 ; Schmälzle and Grall, 2020b ). Action movies can stimulate intense bursts of arousal ( Hermans et al., 2011 ; Kautonen et al., 2018 ). Comedy tickles our funny bone ( Sawahata et al., 2013 ; Amir et al., 2015 ; Jääskeläinen et al., 2016 ; Schmälzle et al., 2022 ), drama/tragedy deals with human responses to suffering ( Raz et al., 2014 , 2016 ). And, while often hushed up, pornography is certainly quite powerful in stimulating experiences ( Prause et al., 2015 ; Schmälzle et al., 2017 ; Chen et al., 2020 ; Grubbs and Kraus, 2021 ).

In sum, it is clear that media feature a host of content that can elicit and precisely steer social-cognitive and affective processes. In fact, due to this capacity, media are very promising to study the neural basis of these phenomena in a way that is more appropriate to their nature than, say, event-related studies of single words, affective images, and so forth ( Hasson and Honey, 2012 ; Saarimäki, 2021 ).

The challenge, then, is to quantify the social and affective content characteristics to be able to unlock its mechanism of action via neuroimaging. The studies presented above are in an advantageous position because the content properties that we care about are relatively well understood and can be coded straightforwardly (as done in the NeuroScout system or via the Matlab vision toolbox or some natural language processing toolbox). By contrast, when the research focus is on social-cognitive and affective phenomena, the task of coding the conceptual content is considerably more difficult, 5 although some clever ways exist to attempt to parametrize these more challenging factors ( Heider and Simmel, 1944 ; Meyer et al., 2019 ; Nguyen et al., 2019 ). But it is clear that if we ignore higher-level content altogether, then we cannot expect to meaningfully relate brain responses to their elicitors - at least not beyond relatively simple sensory-perceptual brain responses, and if top-down attention comes into play, even these will get affected. This is the problem with ‘content-blind’ neuroimaging.

2.3. Media effects: how media influence individuals and large-scale populations

The last link in the causal chain from content to reception is the question of how exposure to media changes memories, attitudes, or behaviors. The term media effects refers to these psychological or behavioral outcomes of stimulation with media. Of note, the term media effects is used to refer to individual-level as well as population effects ( Bryant and Oliver, 2008 ). The latter clearly depend on the former, but in practice, they tend to be studied by different research communities who focus either on micro- (intraindividual) or macro (social) levels of analysis.

The origin of the field can be traced back to social scientific research in the 1920s and 30s, which is the era when the first distant mass media (radio, TV) emerged. Historically, the field has swung back and forth between periods in which researchers postulated relatively strong media effects and those of weaker effects. For example, in the period between 1920 and 1950, much research attention centered on the putatively strong influence of propaganda ( Hovland and Lumsdaine, 2017 ). Modern efforts showcase that media effects tend to be smaller in nature and more contextually dependent ( Lang, 2013 ; Rains et al., 2018 ). Nevertheless, and despite substantial evidence to the contrary, today’s pressing topics like radicalization, fake news, deep fakes, and the influence of social media are often cast in overly simplistic terms and assume overly powerful effects. Neuroimagers looking to use media as stimuli should recognize that, contrary to common perceptions, media effects tend to be quite small in practice.

The list of media effects and media effects theories is too long to discuss here. Still, a partial list of interesting phenomena and theories includes, e.g., the third-person effect - the belief that media influence others more than oneself ( Perloff, 2002 ). Readers are likely familiar with the famous Bobo Doll Study that helped give rise to Social-Cognitive or Social Learning Theory ( Bandura, 1977 ). Central to this theory is the notion of observational learning and role models - both of which can occur during media consumption - and therefore Social Cognitive Theory is widely used to explain social media effects ( Bandura, 1994 ). Similarly, Affective Disposition Theory ( Zillmann and Cantor, 1972 ; Raney, 2004 ) links characters and plot elements to affective audience responses. There are, of course, many other interesting effects and theories of media influence to highlight, but for the sake of space, we refer readers to key reference works ( Zillmann and Vorderer, 2000 ; Bryant and Oliver, 2008 ; Littlejohn and Foss, 2009 ; Nabi and Oliver, 2009 ; Dill, 2013 ; DeFleur, 2016 ).

In essence, any result of media stimulation could be considered as a media effect, whether it is short-term memory (e.g., recalling last night’s news), long-term memory (e.g., remembering a childhood TV show), a change in attitude, a belief (e.g., being more open to immigration after watching a refugee drama), or behavior (e.g., donating money to charity after viewing an ad). These effects are often linked to their elicitors in content, but how the brain mediates between content and effects has traditionally been ignored. Instead, because neuroimaging measures were unavailable until recently, researchers had to rely on self-report methods that were usually taken after the media consumption ended ( Lang, 2014 ).

Critically, media effects are not only studied in single individuals but often with an eye toward aggregate audiences. The field most closely associated with this perspective is mass communication. In brief, mass communication describes a one-to-many mode of communication in which the same message is sent out to multiple recipients. For instance, early mass media were newspapers where the same article would be read by all readers. Radio marked another milestone, then most notably followed by Television. And, although social media has now upended the traditional “one-to-many” model of mass communication, providing a many-to-many mode of communication instead, it is still true that a single social media message can be sent out to a large audience, and the brains of audience members would then still respond to the same message ( Schmälzle and Grall, 2020a , b ; Gong et al., 2022 ).

Given the important effects media can have on the masses and public opinion ( Lippmann, 1922 ; Noelle-Neumann, 1991 ), it is clearly of interest to examine how reception responses relate to such large-scale media effects. In other words, might media-evoked brain responses allow researchers to predict subsequent effects? Indeed, several emerging neuroimaging studies (and a large body of non-neuroimaging studies from the social sciences more broadly) have begun to examine this question. For instance, Hasson et al. showed that brain imaging data captured during viewing could predict memory, a very concrete and clear-cut media effect ( Hasson et al., 2008a ). Falk et al. showed that brain responses to health messages could predict message-consistent behavior change at later points ( Falk et al., 2010 ), and several other articles examine effects related to persuasion, broadly defined, or engagement with and sharing of messages in social networks ( Weber et al., 2015a ; Baek et al., 2017 ; Huskey et al., 2017 ; Coronel et al., 2021 ). These studies point to the potential of using brain imaging data to predict individual-level outcomes, that is, how to link reception responses captured in individuals to the ensuing media effects.

Another intriguing twist for using brain imaging data is to predict collective outcomes. By that, we mean that it is possible to record the brain’s responses during reception from a smaller test audience and link them to aggregate outcomes in larger groups ( Berkman and Falk, 2013 ). For example, in the neuroeconomics literature, researchers have predicted the cultural popularity of music from brain responses ( Berns and Moore, 2012 ). Similarly, Dmochowski et al. (2012) , used brain responses to SuperBowl commercials to predict online engagement (tweet volume; Dmochowski et al., 2014 ), and Falk et al. used brain responses to health messages to predict campaign success (call volume to an anti-smoking quitline; Falk et al., 2012 ).

The broader reasoning behind these efforts, which connect the brain responses of single individuals or small groups to large-scale population-level media effects, is based on the one-to-many mass communication logic: A message is sent out and processed by multiple individuals comprising an audience. If a given test audience is representative of a larger population, their brain responses can serve as a potential predictor of aggregate outcomes. That this works is just as logical as it is logical to use self-reports from samples to forecast larger outcomes ( Knutson and Genevsky, 2018 ). At present, this approach has been used only in a few studies. Still, given the desirability of movies and media as stimuli, we can expect that many others will follow: After all, movies often even galvanize culturally shared, long-lasting collective memories (e.g., the famous shower scene in Hitchcock’s Psycho), suggesting that these effects have a shared basis in the brains of people who saw the specific footage (see, e.g., Kauttonen et al., 2018 for a neuroimaging study of key-frames). The same logic can also be applied to study how movie content produces any kind of convergent audience response, from collective suspense and fear during a horror movie to collective laughter during comedy ( Schmälzle, 2022 ; Schmälzle et al., 2022 ).

Taken together, media effects are clearly consequential, of enormous interest to social scientists, and one of the most attractive areas that neuroscience researchers would like to seize. Especially the widespread ability of digital data (e.g., time-locked comments during movies and shows, social network metrics; Dmochowski et al., 2014 ; O’Donnell and Falk, 2015 ; Ni and Coupé, 2023 ) increases, there are unprecedented opportunities to link neural data to media effects. However, doing so in a meaningful way will - again - require keeping an eye on the content that starts the logical sequence from media content to brain responses to media effects. Said differently, we can only hope to explain media effects if we trace them back to the preceding brain responses and these, in turn to their elicitors in content.

To summarize, the previous section presented content analysis (2.1), reception analysis (2.2), and effects studies (2.3), arguing that these domains stand in a logical relationship with each other. And in each of these sections, we have pointed to the ways researchers have typically engaged in linking media, neural responses, and effects. These projects, while groundbreaking in their own right, often only investigate a subset of the causal chain from media content to reception responses to media effects. In what follows, we introduce Network Control Theory (NCT, Liu et al., 2011 ) as an integrative analytical framework that is well-suited to help further integrate these domains.

3. Network control theory: examining how media bring brains into specific states

In this article (and the special issue in which it appears), the brain takes center stage as the organ of media reception; that is, the site of action where complex content sets forth the activities that ultimately produce media effects. However, it is clear then that quantifying content is only half the battle - the other half deciphering the brain’s reactions to it. This, in turn, requires a general theory of brain function to motivate an analytical framework for studying content-brain relationships. Our model of brain function is based on current cognitive neuroscience research that views the brain as a complex, hierarchical network ( Mesulam, 1998 ; Fuster, 2003 ). 6 Entry-points into the network and its lower-level nodes (the eye, retina, optic nerve, LGN, and V1+; or the ear, cochlea, auditory nerve, olivary colliculi, and A1+) are relatively localized, and they correspond rather directly to specific lower-level content features (e.g., Hubel & Wiesel-type feature detectors). Subsequent layers of neural processing, however, tend to be more distributed, which calls for more multivariate analysis methods.

Over the past decade, network-based multivariate methods have been applied to neuroimaging data, and several large-scale brain networks have been identified (e.g., Medaglia et al., 2015 ). However, much of this work has been based on data captured in the so-called resting state, i.e., with participants only lying in the scanner. While this work has led to substantial and important insights, it is clear that the unconstrained nature of the resting state task is a limiting factor. By contrast, movies and media more broadly are ideal candidates to advance this research: They provide a rich and relevant stimulus for participants and one that is controlled insofar as it provides exactly the same input for everyone. Moreover, media can steer neurocognitive responses related to perception, attention, memory, and emotion, and it is this property that makes them ideally suited for studying cognitive neuroscience but also relevant for social science research trying to understand their mechanisms of influence. With this in mind, we will next introduce a mathematical framework - Network Control Theory - that uses external control forces (here: a movie and its content) to steer networked systems (here: the brains of audiences exposed to the movie).

3.1. What is network control theory?

Network control theory is a branch of control theory in engineering and a subfield of the larger network sciences ( Gu et al., 2015 ). It deals specifically with the question of how networked systems can be controlled. What does it mean to control a network? Simply put, network control theory is a computational model that specifies if and how interventions, and their corresponding energetic costs, drive complex systems between different topological organizations with different energetic requirements ( Muldoon et al., 2016 ; Tang and Bassett, 2018 ; Kim and Bassett, 2020 ; Lydon-Staley et al., 2020 ). More specifically, a given network topology requires energy costs to maintain. 7 Networks can shift between different topological organizations, each with a different energetic requirement and these topological shifts can have their own energetic requirements, as well (see Figure 5 ).

Network Control Theory. Left panel: Movie events are the causal forces that push the brain (or brains of entire audiences) into different states. For instance, the sudden reveal of a betrayal will likely engage theory-of-mind processes associated with social-cognitive brain networks. The depiction of a potential shooter approaching an innocent victim will engage affective systems. In this particular example, brightness and threat could be used as a time varying energetic source to use in a control theoretic analysis. The idea being, that each is analogous to an energetic source that should impact specific nodes (visual cortex, PFC, respectively) differently, and have different cascading impacts on time-varying network topology. Middle panel: A snapshot of network states in a single individual. As the individual views the movie, their brain responds to the time-varying content. Visual changes will prompt visual networks to come online and reconfigure (the example focuses only on brightness, but other visual features could be modeled, such as the presence or number of faces, contrast, objects, etc.). In parallel, higher-level content elements (though conveyed via the concrete sensory-perceptual features) prompt changes in networks related to executive control, salience processing, etc. Right panel, top: Example of an energy landscape in which valleys represent equilibrium states. By application of control energy, the brain can be pushed to leave one state and settle down in another. Note that the actual landscape has a higher dimensionality. Right panel, bottom: Example of a multivariate brain activity trajectory from actual movie-viewers. The input movie was Bang-Bang! You’re dead by Alfred Hitchcock. Brain activity from different regions (268-dimensional) is embedded in a lower-dimensional (3-dimensions) space ( Heusser et al., 2018 ). Color represents time. As can be seen, the time-varying movie content steers brain activity into different positions.

To make this idea concrete via example, imagine a system of highways connecting several cities. The topological organization of this series of highways requires energy to construct and requires energy to maintain. Imagine now that the topology is updated; a new highway is built and added to the existing network. Constructing the new highway will also require energy, as will maintaining that new highway. Introducing the new highway might also alter the energetic maintenance costs of the highways that already existed in the network (e.g., the new highway may increase or decrease traffic flows on preexisting highways). Similar ideas can be applied to the brain as a network, although empirical evidence about energetic costs and network structure is less robust. For instance, the creation and maintenance of specific connections (white matter pathways) in the brain’s network are associated with energetic costs, and the topological structure is associated with the kinds of states and functions that the network can settle into and perform (e.g., Margulies et al., 2016 ).

Network control theory can be used to calculate a number of metrics that describe these energetic costs. Importantly, some network topologies are easier to reach - requiring less energy to obtain - than others. How can these energetic requirements be quantified? One of the most common approaches is known as controllability. Controllability is the ability to transition a network from one topological organization to another via external energetic input ( Kalman, 1962 ). This controllability metric can be further subdivided into average controllability , modal controllability , and boundary controllability .

Average controllability ( Shaker and Tahavori, 2013 ) describes how much energy needs to be applied to the system to transition the system into a different topological organization. Higher average controllability means that less energy input is necessary to drive the system to different topological organizations. One constraint on average controllability is that it only captures how much energy it takes to move the system into easily reached topological organizations. Modal controllability ( Hamdan and Nayfeh, 1989 ) accounts for circumstances where it takes substantial energy to transition the system into a hard-to-reach topological organization. Finally, boundary controllability ( Bassett et al., 2013 ) identifies nodes within a network that, when targeted with energy, can elicit connection or disconnection among other nodes in the network. Together, these metrics provide insight into the energetic costs and target nodes necessary to drive a network from one topological organization to another.

3.2. How has network control theory already been applied?

Network control theory has been increasingly applied to study the controllability of structural and functional brain networks ( Medaglia et al., 2017 ), but it is not confined to brains alone. Instead, it is also perfectly feasible to apply network control theory to social or psychological networks ( Abelson, 1964 ; Cremonini and Casamassima, 2017 ; Borsboom et al., 2021 ). For example, in neurology and neuropsychology, one can use network control theory to examine how strokes at specific anatomical (structural) sites affect cognitive (functional) processes ( Popova et al., 2022 ). Similarly, in the case of social networks, it becomes possible to ask how structural changes affect function ( Proskurnikov and Tempo, 2017 , 2018 ). For instance, how do changes in leadership structure impact a group, its communication, and ultimately performance? Finally, turning to psychological networks such as attitude and belief networks, network control theory enables simulating how targeted influence (e.g., message-based persuasion attempts geared towards a specific belief) would impact the targeted belief, its associates, and the belief network as a whole ( Schlicht-Schmälzle et al., 2018 ; Chambon et al., 2022 ).

Turning specifically to brain organization, network control theory has revealed some crucial findings about brain structure and function. Possibly most important is that the brain’s intrinsic architecture, that is, the white matter fiber tracts connecting gray matter structures, facilitate controllability in different ways. In a pathbreaking study, Gu et al. (2015) demonstrated that different neural subnetworks had different levels of controllability. For instance, the default mode network has a topological organization that facilitates transitions into other easily reached topological organizations. By comparison, other subnetworks (e.g., fronto-parietal control networks) are better suited to facilitate transitions into difficult-to-reach topological organizations. These controllability characteristics appear to guide high-level cognitive and behavioral responses within organisms ( Rouse et al., 2021 ).

3.3. How can network control theory integrate media content with reception responses and media effects?

How can network control theory be applied to the media content → brain reception mechanism → media effects framework presented above, and what can we gain from it? In a nutshell, our core argument is that under a normal mass communication regime (i.e., one-to-many: same message, many recipients), the arrow of causality starts with the message content. Therefore, understanding the content is the key to understanding downstream effects. 8

To give an example, consider the case of a movie that contains a morally evocative event, such as an innocent person being shot and killed. 9 Such key moments of the story ( Wilensky, 1983 ) evoke predictable audience reactions that are highly consistent across viewers ( Hasson et al., 2008a ; Dmochowski et al., 2012 ; Naci et al., 2014 ; Schmälzle and Grall, 2020a , b ). It is clear that flickering pixels, moving images, and so forth are required to transmit the movie into peoples’ brains. However, the main “effective ingredient” of this content sits at a higher level of plot abstraction. We also know that filmmakers, screenwriters, and fiction authors are very skilled at “pushing” people into certain psychological states (see Figure 5 ). In fact, even the designation ‘director’ clearly alludes to the potential to exert control, that is, by influencing the content creation process in such a way that certain audience reactions follow predictably.

With neuroimaging, we can now capture how brain networks reconfigure dynamically during movie watching, such as how movie events trigger attentional reorienting responses, how close-up shots of protagonists are important events that evoke theory-of-mind processing, or how morality violations engage brain networks involved in emotion and socio-moral cognition. If we can successfully integrate these higher-level layers of the media’s content with the more easily quantifiable characteristics of content that engage sensory and perceptual brain systems, then we can hope to close the explanatory gaps between movie content, reception response, and media effects under one cohesive framework.

To make this all more concrete, consider the following example: We know that simple narratives are easier to follow than complex ones. From a cognitive perspective, we further know that following a complex narrative taxes working memory. Neurally, we know that working memory is associated with (although not in a 1:1 fashion) activity in the executive control and default mode networks. Thus, at a very simple level, we might examine network controllability metrics for different narratives that vary in complexity, and we could expect that simple vs. complex narratives are associated with different controllability values. 10 Further, we might also ask if these controllability values can be used to predict box office revenues of a given narrative, much in the same way as Dmochowski and colleagues ( Dmochowski et al., 2014 ) used neural reliabilities to predict audience preferences. In this case, we would link a high-level media content characteristic (plot complexity), with an equally high-level reception response (controllability), and media effect (box office revenue, a measure of popularity).

Of course, it should also be possible - and maybe more interesting - to apply the approach to a single movie to examine finer-grained elements along the media content, reception response, and media effects pathway. In this case, the time-varying properties of the movie would comprise the input to the system, i.e., the energy that is applied to the network. Mathematically, this can be modeled via impulse response models ( Blaauw et al., 2017 ) when targeting a single node or more generalized control models ( Tang and Bassett, 2018 ) that target multiple nodes in a network (for a review, see Lydon-Staley et al., 2020 ).

The question, then, is what type of media content we should model, to what node or nodes (targets) in the network the resulting energy would get applied, and what sort of outcomes we might expect? Although answers to these questions remain speculative because - to our knowledge - NCT has yet to be applied to content-rich media (as opposed to simpler stimuli and tasks), the cumulative body of knowledge from sensory and cognitive neuroscience, combined with nearly six decades of entertainment research and mass communication research can offer direction.

Starting with basic sensory and perceptual features, we can extract these in much the same way as is currently done for topical studies of vision, audition, or language (e.g., Kauttonen et al., 2015 ; McNamara et al., 2017 ), and we can relate quantified content properties (e.g., over-time variations in brightness, sound energy, etc.) to brain imaging measures. To the extent that the reception mechanisms that correspond to specific content properties are localized, one may not even need to resort to network-based analyses but could even rely on standard brain mapping-style analyses.

Then, as we move from simple features like brightness or sound energy to more complex media content, we need to not only adjust the kinds of content features that are quantified and used to model brain responses but also the kinds of brain response features that are modeled (i.e., moving from localized univariate response models to model networked responses and state-reorganizations, which is what network control theory excels in). With regard to the quantification of content, we argued above that it will no longer be sufficient to model pixels, brightness, or the occurrence of faces. Rather, media psychological research points to the importance of characters, the actions they perform and the outcomes that befall them, and so forth. Using this understanding (for a review, see Grizzard and Eden, 2022 ), the kinds of content we should attend to, and their putative brain targets become clearer. With regard to response features, we can rely on methods from network neuroscience, including parcellations of canonical brain networks, network estimation methods, and knowledge about structure–function relationships (e.g., between the TPJ, a core node of the DMN and social-affective processes, e.g., Yeshurun et al., 2017 ).

Imagine a researcher interested in empathy. Two narratives could be constructed, one where a liked character suffers a dramatic setback (which should elicit an empathetic response), and one where the setback is edited out (which should not elicit an empathetic response). The timing of this empathy-inducing outcome could be used in an impulse response model that targets a specific node in the network, like the temporal–parietal junction, which has long been implicated in empathy processing ( Saxe and Kanwisher, 2003 ; Decety and Lamm, 2007 ; Alcalá-López et al., 2018 ). 11 Then, one would analyze how this intervention (i.e., film event) changes the brain network topology and how this differs between the experimental and control version of the narrative. Moving onwards, if a negative event befalling a liked character changes the brain network into a state of empathy, then that change should be associated with a corresponding change in audience responses (e.g., self-reported empathy), thus completing the sequence from media content, reception response, to media effects.

Another example could be suspense: We know that suspense in media strongly affects the audience, and screenwriters and directors possess a lot of knowledge about how to elicit this phenomenon (e.g., Brewer and Lichtenstein, 1982 ; Douchet, 1985 ; Vorderer et al., 2006 ). Moreover, some prior work has focused on the brain mechanisms of suspense precisely because of its potential to take control of audiences ( Bezdek et al., 2017 ; Schmälzle and Grall, 2020a , b ). Much like in the example about empathy above, it would be possible to create different branches of the same story that incorporate directing techniques, music, narrative devices, or other methods to increase suspense and examine their impact on brain systems. 12 Again, one could then analyze how variations in suspense (either between experimental conditions or variations of suspense over time) impact the brain network topology. One broad prediction, for example, is that ebbs and flows in suspense should impact the saliency and executive control networks, which are associated with attention. Although more difficult to resolve with present-day functional neuroimaging methods (because of limitations in spatial and temporal resolutions), suspense should also impact ascending arousal networks and cortico-subcortical loops associated with emotional arousal. Indeed, previous neuroimaging work points to such responses (e.g., Hermans et al., 2011 ; Naci et al., 2014 ; Young et al., 2017 ; Schmälzle and Grall, 2020a , b ), but whereas much of this work is data-driven and more exploratory in nature, network control theory holds potential to integrate this research and provide a common platform for bringing together content (directors, creators), brain response (cognitive neuroscientists) and effects studies (media psychology and entertainment research).

These represent just a few possible examples that use network control theory as a framework that connects the domains of content analysis, reception analysis, and media effects. The appeal of network control theory is that it enables us to start from media-informed hypotheses about what will be driving brain network dynamics and how while honoring the complexity and hierarchical nature of the content (from simple objective features to more abstract semantic and pragmatic contents), brain responses (from evoked sensory responses to reorganization of higher-level brain systems), and media effects (from effects on individuals to populations, and from obligatory effects in all individuals to effects that could vary based on individual difference, cultural background, or an individual’s position in a larger social network topology).

Although many unknowns and challenges remain, 13 this approach holds the potential to integrate domains that have henceforth been studied separately. Viewed from afar, this endeavor is almost reminiscent of the seminal work of Penfield (1950) , who used intracranial stimulation techniques to map out functional brain systems, but with the difference that movies now offer a way to influence brain systems and associated affective, social, and conceptual reactions, and not only in individuals but multiple brains comprising an audience. 14

4. Future directions

4.1. from traditional mass media to new media.

We are not the first to make arguments about the necessity of quantifying naturalistic and multi-modal media stimuli for understanding the brain, or media effects (see, e.g., Weber et al., 2006 , 2015b ; Spiers and Maguire, 2007 ; Dudai, 2012 ; Sonkusare et al., 2019 ; Aliko et al., 2020 ; Finn et al., 2022 ). Important work headed in this direction already exists, and we have worked to note these developments at relevant points in our manuscript. The point is, however, this approach has not yet reached widespread adoption. We think this is for two key reasons: (1) uncertainty about how to quantify media content, and (2) ambiguity about how to link content’s complex, hierarchically organized, and time-varying effects across complex, hierarchically organized, and time-varying brain systems. The approach outlined above, which advocates jointly studying media content, reception responses, and media effects and suggests NCT as a framework for doing so, addresses these two challenges and is directly applicable to a wide variety of traditional mass media, including TV, cinema, and written or spoken narratives.

However, the notion of mass media today is no longer quite what it was when relevant definitions and theories of mass media were first formulated. Rather, these days the media ecosystem is constantly in flux, and new ways to stimulate brains and entertain audiences are constantly invented. Traditional mass media, most notably radio and television, followed the classical one-to-many model in which a sender emitted the same message that was carried via a medium to a large audience, like when people listened to Orson Wells’ “War of the Worlds” broadcast that prompted them to fear an alien invasion. Similarly, TV and cinema movie viewing also fall under this kind of paradigm (same message, millions of simultaneous receivers), which is very compatible with neuroimaging and leads to a constant increase in papers and publicly available datasets featuring audiobooks and movies ( Aliko et al., 2020 ; Willems et al., 2020 ).

The advent of streaming platforms (e.g., Netflix for movies and shows, YouTube for all kinds of content, Spotify for music) prompted a shift in the landscape because previously more homogenous mass audiences became increasingly fragmented and can now consume content at their own pace and via increasingly niche content. Despite the self-timed nature of such video streaming, however, the basic notion of same-message - many receivers still remains. Thus, these kinds of media models lend themselves exceptionally well to neuroscientific studies like the ones outlined above.

Social media add another layer of complexity, but we argue that key principles of mass communication still remain relevant. Modern social media, like Twitter and TikTok, can be characterized as instant mass media; that is, they deliver the same messages to many recipients in a very swift manner. Moreover, they add novel affordances to engage with content via liking, sharing, and commenting. The resulting mode of communication has been called “masspersonal communication” because it blends elements of interpersonal communication into the mass communication model ( O’Sullivan and Carr, 2018 ). Thus, the content of social media messages can still be studied and linked to brain reception responses, and the additional affordances of social media (like sharing, liking, commenting) can also be studied from a neuroscientific perspective ( Meshi et al., 2015 ; Scholz et al., 2019 ).

4.2. Games and virtual reality as emerging trends

Reflecting on what the future may hold, we see two areas on the rise: Gaming and Virtual Reality (VR). Gaming and VR are both among the fastest-growing media types. Both offer interaction potential, 15 distinguishing them from movies and stories (TV, radio, podcasts, etc.) that are consumed more passively, although even for the latter, audiences can vary in their level and degree of internal activity (e.g., interest, involvement, vigilance). At first glance, the interactive and thus constantly changing nature of gaming and VR media may seem incompatible with the “same stimulus sequence” notion that is so characteristic of movies, audiobooks, and other fixed-type mass media. However, we note that even in games and VR, there are clearly shared aspects as well and that the experiences users have are far from idiosyncratic. In games, for instance, many sub-scenes are prerecorded and thus the same for all audience members, and the same holds true for VR. Moreover, for both games and VR experiences, it is exceptionally well possible to quantify and precisely time-lock contents ( Bente et al., 2007 ; Huskey et al., 2018a ; Lammers et al., 2019 ; Calcagnotto et al., 2021 ).

Thus, although studying brain responses during games and VR will require special consideration, we argue again that the basic model outlined above still applies: As long as fixed content is consumed, one can code it just like one would do for movies or narratives (see above), and to the extent that content varies by person, one can still content-analyze each individual screen-recording using the same principles ( Dmochowski et al., 2018 ; Huskey et al., 2018b , 2022 ; Ki et al., 2020 ).

5. Summary and conclusion

In sum, we have argued that the time is ripe for creating a new substantive science at the intersection of media and neuroscience. The neuroscientifically informed study of media reception processes provides the missing link between media content and media effects, enabling fascinating insights into the hidden mechanisms by which media affect us. However, to avoid reinventing the wheel or creating a mayfly-like field, neuroscientists should engage with research that has studied media content and media effects. The current article offers a springboard for doing so. We have introduced an organizing framework that connects the domains of media content, media reception, and media effects in a logical, sequential manner. In that framework, content is the key to understanding brain responses and subsequent media effects. We then suggested network control theory as a way to link the domains of media content, media reception mechanisms, and media effects (in individuals and social networks) in one multi-layered (or multi-staged) network. This framework offers a clear agenda for future research that uses media in combination with neural or other reception response measures and applies to studies focusing on specific neurocognitive processes (e.g., vision, language, or memory) as well as more integrative investigations of audience responses to movies and narratives. The ideas articulated here are most directly applicable to one-to-many mass communication models (which include neurocinematics, neuroscience of stories, etc.) but can also be adapted to modern social and interactive media.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1 Of note, while we believe that upper levels (i.e., beyond sensory-perceptual content) are important and largely understudied, at least when it comes to neuroimaging, this does not mean that it was not worthwhile to study lower levels. For instance, researchers in visual neuroscience and neurolinguistics have both discovered the benefit of using movies as stimuli, and many other lower- and mid-level neurocognitive processes (e.g., event segmentation, situation model building, etc.) can be examined fruitfully using media.

2 In fact, they may matter even more than lower-level content features because we can easily turn any story into a movie and vice versa, which completely exchanges the lower-level content types, but keeps the higher-level information intact ( Honey et al., 2012 ; Regev et al., 2018 ). Likewise, one can also transform a given movie in many ways, like into a comic, or a reissue with newer actors, thus changing all lower-level features, yet it will still stay the same movie. However, we want to avoid creating the impression that we give priority to higher-level content elements or that we consider lower-level elements as less important. This is not the case. For example, content creators (directors and camera operators) often make strategic use of lower-level content elements (e.g., angle, shot sequence) to create specific impressions. Studying these techniques, their impact on brain activity, and their effects on viewers is as valuable as analyzing, e.g., the plot narrative.

3 Via standard forward inference ( Henson, 2006 ).

4 Again, we are not shy to admit that the content properties that cater to these processes are difficult to quantify: For example, computing a sound envelope/RMSE feature is easy. Nowadays, computing BERT-embeddings for every word of a story is also quite doable. However, even though these properties are relevant to understanding a story, they alone are insufficient. Parallel arguments about this exact issue are also made in the NLP community, where debate rages about the capabilities and limitations of large-language models ( Bender and Koller, 2020 ). Yet, again, we want to emphasize that our goal is not to declare only the plot level as the only level worth quantifying. Rather, examinations of specific linguistic and sound features, their creation, their effects on the brain, and their impact on audiences are inherently relevant and worth studying.

5 Researchers often rely on their intuition. In fact, most movies used in fMRI studies seem to be chosen for their social-affective elicitation potential. Movies that have been used include Bang Bang! And you are dead; The Present; Partly Cloudy; Curb your Enthusiasm; The Office; Sherlock; Memento. These are all great, and it seems clear that "researchers felt something" when they opted to use these movies. However, none of the papers devoted more than one or two sentences to the content and theoretical reasons why it was chosen.

6 Network science is an application of graph theory where systems of information can be grouped into nodes (specific elements) and edges (the relationships between those elements). The beauty of network science is its domain generality. Network systems can be constructed to represent social organization (e.g., each node is an individual, each edge represents if individuals are friends or not), information on the internet (e.g., nodes represent a webpage, edges represent hyperlinks between websites), civil infrastructure (e.g., nodes represent cities, edges represent highways connecting cities), biological systems (e.g., nodes represent gray matter corresponding to specific brain structures, edges represent white matter fiber tracts connecting gray matter), and more (for a review, see Newman, 2010 ). The constellation of edges connected by nodes describes a network’s organization. This organization is commonly referred to as a network’s topology.

7 There is the energy necessary to maintain a given topological organization. In the case of brains and brain networks (both structural and functional), this is associated with energetic costs, most notably metabolic costs ( Bullmore and Sporns, 2012 ). There is also the energy that is necessary to transition a system into, and maintain, a given topological organization. In neurscientific contexts, this includes things external energy sources such as an experimental task, a pharmacological intervention, a specific stimulus, and so on, that drive the brain from one functional topological organization to another. Network control theory can be used to account for both cases (see, e.g., Gu et al., 2015 ; Lydon-Staley et al., 2020 , respectively). In our application, we are particularly focused on the latter case without denying the former.

8 We thank a reviewer for pointing out that based on the picture presented here (content → brain → effects), readers may infer that media reception is a strictly passive process, which we ultimately do not believe to be correct. Rather, there are additional receiver-sided factors that can affect the reception process. For example, the degree of interest among receivers can modulate how people respond to the same incoming message content; the same is true for the belief-consistency of a message, the background knowledge audiences have about a topic, or simply their degree of vigilance. Thus, in reality, the way in which audiences select and engage with media content is going to be more dynamic, creating message-receiver interactions beyond simple message main effects, potential dynamic feedback loops, and other audience effects (e.g., during co-viewing vs. individual viewing). In sum, real audiences are more active (e.g., Biocca, 1988 ; Huskey et al., 2020 ) and these factors must be taken into account. Nevertheless, even if these (or other) additional external or internal factors come into play, it is clear that the proximal causal role of media content is critical and must be quantified.

9 From the perspective of Moral Foundations Theory and the Model of Intuitive Morality and Media Exemplars, this could be considered a violation of the harm/care foundation ( Tamborini, 2011 ; Graham et al., 2013 ).

10 Readers who are familiar with traditional approaches to fMRI data analysis, such as the GLM framework, will realize that this approach is conceptually similar, the main difference being that it is applied here to network metrics as the dependent variable rather than to the activity of individual voxels.

11 Of course, this approach requires a strong a-priori hypothesis. Data-driven approaches are also available. For instance, a researcher could, one by one, apply the energy source to each node in the network in a round-robin style, and observe the outcome.

12 Of note, here we discuss only standard experimental paradigms, but it would not seem infeasible to even create closed-loop, neurofeedback-type systems that feedback audience activity into the creation process, thereby further enhancing collaboration and integration between filmmakers and neuroscience (e.g., Tikka et al., 2012 ; Raz and Hendler, 2014 ).

13 Especially regarding the quantification of content that lies at or behind the "barrier of meaning" and the accurate measurement of the networked structures.

14 We would like to thank a reviewer for suggesting that this all sounds a bit like a "content-powered TMS machine," an idea that we find thought-provoking and appropriate. However, the reviewer is also right to warn against overstretching this analogy because current TMS methods allow causal targeting of single (or few) and localized brain functions. By contrast, when content "targets" brain systems (such as the TPJ in the empathy example above), the TPJ would not receive direct input from content, but its input would consist of the preprocessed visual and auditory information conveying the empathy evoking narrative. Despite the caveats, we believe that the notion that media can precisely steer neurocognitive processes and evoke strong effects like emotional arousal, empathy, and so forth is convincing and that the NCT framework provides a way to examine how this is mediated by brain networks and their dynamic reconfigurations.

15 Clearly, interactive media add immense complexity to the simple linear-causal content-reception-effects perspective offered for movies and comparable consumption media. Note that even for these types, however, there is interaction potential insofar as exposure to movies can shape preferences, thereby affecting future selection decisions and so forth. Thus, the overall picture is clearly more dynamic than described here. Yet, even a complex, convoluted episode with interactive media can be disentangled and causally arranged along the ‘arrow of time’, for which the content, reception, response framework should still hold.

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Media Reception Studies

• Janet Staiger
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• Language: English
• Publisher: New York University Press
• Copyright year: 2005
• Audience: Professional and scholarly;
• Published: July 1, 2005
• ISBN: 9780814786741

• DOI: 10.1075/BTL.141
• Corpus ID: 158265742

Media audiences and reception studies

• Published in Reception Studies and… 4 June 2018
• Reception Studies and Audiovisual Translation

Media Audiences and Reception Studies

• Media and Communication Studies

Research output : Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Subject classification (UKÄ)

• Media Studies

Free keywords

• Media audiences
• reception studies
• Nordic noir
• audience engagement

Access to Document

• 10.1075/btl.141

Fingerprint

• Medium Arts and Humanities 100%
• Cultural Users Arts and Humanities 100%
• Methods Arts and Humanities 75%
• Engagement Arts and Humanities 75%
• Transnational Arts and Humanities 75%
• Time Social Sciences 66%
• Distribution Social Sciences 66%
• Research Social Sciences 66%

T1 - Media Audiences and Reception Studies

AU - Hill, Annette

PY - 2018/6/14

Y1 - 2018/6/14

N2 - Media audiences and reception studies is a shifting area of research in terms of theories and concepts, methodologies and methods. Audiences are on the move, and ways of understanding these transitions involves multi-faceted, pragmatic approaches to varieties of audience experiences in context, including contexts of distribution and media flows, genres and communicative form, and identities and everyday life. The range of methodologies and methods available to audience researchers are multi-form, mixing media, social and cultural theories, with flexible methods for capturing transforming audiences. Transnational audiences for global formats and local content signal an increasing range of audio-visual content available to consumers, fans and publics, including translations, subtitling and fan subbing of fiction and non-fiction television and social media. In relation to audience engagement with screen culture there is an increasing significance of distribution contexts, and the centrality of place and time, to research in transnational audiences. The case study of production and audience research of the Nordic noir television drama The Bridge highlights how engaging with multi-layered storytelling and reading subtitles makes for intensities of cognitive and emotional engagement with the drama, and suggests a sense of place and time is critical to understanding cultural engagement with transnational drama.

AB - Media audiences and reception studies is a shifting area of research in terms of theories and concepts, methodologies and methods. Audiences are on the move, and ways of understanding these transitions involves multi-faceted, pragmatic approaches to varieties of audience experiences in context, including contexts of distribution and media flows, genres and communicative form, and identities and everyday life. The range of methodologies and methods available to audience researchers are multi-form, mixing media, social and cultural theories, with flexible methods for capturing transforming audiences. Transnational audiences for global formats and local content signal an increasing range of audio-visual content available to consumers, fans and publics, including translations, subtitling and fan subbing of fiction and non-fiction television and social media. In relation to audience engagement with screen culture there is an increasing significance of distribution contexts, and the centrality of place and time, to research in transnational audiences. The case study of production and audience research of the Nordic noir television drama The Bridge highlights how engaging with multi-layered storytelling and reading subtitles makes for intensities of cognitive and emotional engagement with the drama, and suggests a sense of place and time is critical to understanding cultural engagement with transnational drama.

KW - Media audiences

KW - reception studies

KW - Nordic noir

KW - audience engagement

U2 - 10.1075/btl.141

DO - 10.1075/btl.141

M3 - Book chapter

SN - 9789027200938

BT - Reception Studies and Audiovisual Translation

A2 - Di Giovanni, Elena

A2 - Gambier, Yves

PB - John Benjamins Publishing Company

CY - Amsterdam

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Introduction: Three Phases of Reception Studies

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Mônica Bertholdo Pieniz

Researches defined as state of the art or state of the question, amongst several other names used, apparently do not constitute a real concern among researchers within Communications. Based on the understanding that this task is not only necessary but also urgent, this research focuses on the sensu stricto academic production on reception studies developed in the past two decades. Theses and dissertations written within the Postgraduate Programmes in Communications from 1990 to 2009 were analysed so as to map the theoretical and methodological framework elected by researchers who made efforts to understand this complex phenomenon of relations between "media and audiences". The results point to a still-slow growth in the number of such studies as well as to several theoretical challenges in dealing with such issues.

Valquiria John

The researches defined as state of the art or state of the question, among several names used, apparently do not constitute a real concern among researchers in Communication. Based on the understanding that this work is not only necessary, but also urgent, this research focuses on the stricto sensu academic production about reception studies developed in the past two decades. Thesis and dissertations produced in the Graduate Programs in Communication from 1990 to 2009 were analyzed, aiming to map the theoretical and methodological scenario adopted by researchers who made efforts to understand this complex phenomenon of relations between " Media and audiences ". The results point

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In an era of “social media” technologies, instrumental goals such as networking, organizing, and information-sharing hold great sway over the study of activist culture. Researchers often conceptualize activists’ media use as participation in message production and dissemination, while overlooking practices related to reception and interpretation – that is, activists as audiences. In this chapter I propose that the moments in which activists engage with media as listeners, readers, and viewers are just as interesting to scholarship as those in which people create and/or share media relevant to activism. By shifting some emphasis from the transmission mode of activists’ media use to the ritual or symbolic dimension, we can better understand how media habits help sustain activist identities and a sense of belonging, which serves as a precursor to participation. I also assert the importance of low-tech media, face-to-face communication, and offline participation among such audiences, whose members aim to connect mediated activities with real-world ones, and identify some social limitations in technological activism. The chapter concludes by suggesting avenues for future study that explore why activists choose to receive certain messages and how ritual contributes to people getting and staying involved with activist communities.

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Article contents

Reception theory, reception history, reception studies.

• Ika Willis Ika Willis School of the Arts, English and Media, University of Wollongong
• https://doi.org/10.1093/acrefore/9780190201098.013.1004
• Published online: 22 January 2021

Reception-oriented literary theory, history, and criticism, all analyze the processes by which literary texts are received, both in the moment of their first publication and long afterwards: how texts are interpreted, appropriated, adapted, transformed, passed on, canonized, and/or forgotten by various audiences. Reception draws on multiple methodologies and approaches including semiotics and deconstruction; ethnography, sociology, and history; media theory and archaeology; and feminist, Marxist, black, and postcolonial criticism. Studying reception gives us insights into the texts themselves and their possible range of meanings, uses, and value; into the interpretative regimes of specific historical periods and cultural milieux; and into the nature of linguistic meaning and communication.

• interpretation
• communication

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Media Reception, Media Effects and Media Practices in Sustainability Communication: State of Research and Research Gaps

• First Online: 13 March 2021

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• Sigrid Kannengießer 4  

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This chapter gives an overview of the research field dealing with media reception, media effects and media practices in relation to sustainability, including the field of environmental and climate communication, and identifies current research gaps within this area. It uses a broad understanding of the term sustainability communication, defining it as all communicative practices, mediated or non-mediated, which deal with any aspect of sustainability (either related to the ecological, economic and/or social dimension) that is, any phenomenon or process which deals with the relation of the needs of current and future generations. Following this understanding, the chapter discusses theoretical and empirical studies which deal with the reception of media content that refers to sustainability, and its effects on media users as well as media practices with which different actors try to contribute to sustainability.

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Kannengießer, S. (2021). Media Reception, Media Effects and Media Practices in Sustainability Communication: State of Research and Research Gaps. In: Weder, F., Krainer, L., Karmasin, M. (eds) The Sustainability Communication Reader. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-31883-3_18

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• Media Reception Studies

In this Book

• Janet Staiger
• Published by: NYU Press
• View Citation

Media Reception Studies broadly surveys the past century of scholarship on the ways in which audiences make meaning out of mass media. It synthesizes in plain language social scientific, linguistic, and cultural studies approaches to film and television as communication media.

Janet Staiger traverses a broad terrain, covering the Chicago School, early psychological approaches, Soviet theory, the Frankfurt School, mass communication research and critical theory, linguistics and semiotic theory, social-psychoanalytical research, cognitive psychology, and cultural studies. She offers these theories as a set of tools for understanding the complex relationships between films and their audiences, TV shows and their viewers. She explains such questions as the behavior of fans; the implications of gender, sexuality, and race/ethnicity with regard to the media; the effect of violence, horror, and sexually explicit images on viewers; and the place of memory in spectatorship.

Providing an organized and lucid introduction to a staggering amount of work, Media Reception Studies is an indispensable resource for anyone interested in understanding the effects of mass media.

Table of Contents

• Frontmatter
• Acknowledgments
• 1. Introduction
• 2. Social Scientific Theories
• 3. Linguistic and Cultural Studies Theories
• 4. Fans and Fan Behaviors
• 5. Viewers of Stars, Cult Media, and Avant-Garde
• pp. 115-138
• 6. Minorities and Media
• pp. 139-164
• 7. Violence, Horror, and Sexually Explicit Images
• pp. 165-185
• 8. Memories
• pp. 186-196
• Selected Bibliography
• pp. 197-236
• pp. 237-251
• About the Author

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Media reception studies.

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A broad survey on how audiences make meaning out of mass media Media Reception Studies broadly surveys the past century of scholarship on the ways in which audiences make meaning out of mass media. It synthesizes in plain language social scientific, linguistic, and cultural studies approaches to film and television as communication media. Janet Staiger traverses a broad terrain, covering the Chicago School, early psychological approaches, Soviet theory, the Frankfurt School, mass communication research and critical theory, linguistics and semiotic theory, social-psychoanalytical research, cognitive psychology, and cultural studies. She offers these theories as a set of tools for understanding the complex relationships between films and their audiences, TV shows and their viewers. She explains such questions as the behavior of fans; the implications of gender, sexuality, and race/ethnicity with regard to the media; the effect of violence, horror, and sexually explicit images on viewers; and the place of memory in spectatorship. Providing an organized and lucid introduction to a staggering amount of work, Media Reception Studies is an indispensable resource for anyone interested in understanding the effects of mass media.

Janet Staiger teaches cultural, gender, sexuality, and media studies at The University of Texas at Austin. Her recent books are Perverse Spectators: The Practices of Film Reception and Blockbuster TV: Must-See Sitcoms in the Network Era (both available from NYU Press).

"Media Reception Studies could be subtitled, ‘Everything You Always Wanted to Know About Reception Studies, But Were Afraid to Ask.’ Staiger presents a robust, sophisticated, and eminently readable account that will enable specialists and students alike to grasp the depth and breadth of one of the most significant areas of inquiry in the field today." ~Barbara Klinger,Indiana University

"I have been waiting for just this book. At last, someone with a deep background in reception research has brought her wisdom to bear on the state of the field. Media Reception Studies has a wonderful range, and is so clearly and incisively written that everyone from undergraduates to senior academics can get great benefit from it." ~Martin Barker, University of Wales, Aberystwyth

"Staiger writes that her purpose is to provide a map of the field of reception studies, and in this she succeeds. This stuyd reveals how much ahs been done in this area and how the study of the effects of media content has evolved. The "selective" bibliography is in fact extensive,a nd the index is comoprehensive." ~CHOICE

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• Published: 02 May 2024

Effectiveness of social media-assisted course on learning self-efficacy

• Jiaying Hu 1 ,
• Yicheng Lai 2 &
• Xiuhua Yi 3  

Scientific Reports volume  14 , Article number:  10112 ( 2024 ) Cite this article

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• Human behaviour

The social media platform and the information dissemination revolution have changed the thinking, needs, and methods of students, bringing development opportunities and challenges to higher education. This paper introduces social media into the classroom and uses quantitative analysis to investigate the relation between design college students’ learning self-efficacy and social media for design students, aiming to determine the effectiveness of social media platforms on self-efficacy. This study is conducted on university students in design media courses and is quasi-experimental, using a randomized pre-test and post-test control group design. The study participants are 73 second-year design undergraduates. Independent samples t-tests showed that the network interaction factors of social media had a significant impact on college students learning self-efficacy. The use of social media has a significant positive predictive effect on all dimensions of learning self-efficacy. Our analysis suggests that using the advantages and value of online social platforms, weakening the disadvantages of the network, scientifically using online learning resources, and combining traditional classrooms with the Internet can improve students' learning self-efficacy.

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Introduction.

Social media is a way of sharing information, ideas, and opinions with others one. It can be used to create relationships between people and businesses. Social media has changed the communication way, it’s no longer just about talking face to face but also using a digital platform such as Facebook or Twitter. Today, social media is becoming increasingly popular in everyone's lives, including students and researchers 1 . Social media provides many opportunities for learners to publish their work globally, bringing many benefits to teaching and learning. The publication of students' work online has led to a more positive attitude towards learning and increased achievement and motivation. Other studies report that student online publications or work promote reflection on personal growth and development and provide opportunities for students to imagine more clearly the purpose of their work 2 . In addition, learning environments that include student publications allow students to examine issues differently, create new connections, and ultimately form new entities that can be shared globally 3 , 4 .

Learning self-efficacy is a belief that you can learn something new. It comes from the Latin word “self” and “efficax” which means efficient or effective. Self-efficacy is based on your beliefs about yourself, how capable you are to learn something new, and your ability to use what you have learned in real-life situations. This concept was first introduced by Bandura (1977), who studied the effects of social reinforcement on children’s learning behavior. He found that when children were rewarded for their efforts they would persist longer at tasks that they did not like or had low interest in doing. Social media, a ubiquitous force in today's digital age, has revolutionized the way people interact and share information. With the rise of social media platforms, individuals now have access to a wealth of online resources that can enhance their learning capabilities. This access to information and communication has also reshaped the way students approach their studies, potentially impacting their learning self-efficacy. Understanding the role of social media in shaping students' learning self-efficacy is crucial in providing effective educational strategies that promote healthy learning and development 5 . Unfortunately, the learning curve for the associated metadata base modeling methodologies and their corresponding computer-aided software engineering (CASE) tools have made it difficult for students to grasp. Addressing this learning issue examined the effect of this MLS on the self-efficacy of learning these topics 6 . Bates et al. 7 hypothesize a mediated model in which a set of antecedent variables influenced students’ online learning self-efficacy which, in turn, affected student outcome expectations, mastery perceptions, and the hours spent per week using online learning technology to complete learning assignments for university courses. Shen et al. 8 through exploratory factor analysis identifies five dimensions of online learning self-efficacy: (a) self-efficacy to complete an online course (b) self-efficacy to interact socially with classmates (c) self-efficacy to handle tools in a Course Management System (CMS) (d) self-efficacy to interact with instructors in an online course, and (e) self-efficacy to interact with classmates for academic purposes. Chiu 9 established a model for analyzing the mediating effect that learning self-efficacy and social self-efficacy have on the relationship between university students’ perceived life stress and smartphone addiction. Kim et al. 10 study was conducted to examine the influence of learning efficacy on nursing students' self-confidence. The objective of Paciello et al. 11 was to identify self-efficacy configurations in different domains (i.e., emotional, social, and self-regulated learning) in a sample of university students using a person-centered approach. The role of university students’ various conceptions of learning in their academic self-efficacy in the domain of physics is initially explored 12 . Kumar et al. 13 investigated factors predicting students’ behavioral intentions towards the continuous use of mobile learning. Other influential work includes 14 .

Many studies have focused on social networking tools such as Facebook and MySpace 15 , 16 . Teachers are concerned that the setup and use of social media apps take up too much of their time, may have plagiarism and privacy issues, and contribute little to actual student learning outcomes; they often consider them redundant or simply not conducive to better learning outcomes 17 . Cao et al. 18 proposed that the central questions in addressing the positive and negative pitfalls of social media on teaching and learning are whether the use of social media in teaching and learning enhances educational effectiveness, and what motivates university teachers to use social media in teaching and learning. Maloney et al. 3 argued that social media can further improve the higher education teaching and learning environment, where students no longer access social media to access course information. Many studies in the past have shown that the use of modern IT in the classroom has increased over the past few years; however, it is still limited mainly to content-driven use, such as accessing course materials, so with the emergence of social media in students’ everyday lives 2 , we need to focus on developing students’ learning self-efficacy so that they can This will enable students to 'turn the tables and learn to learn on their own. Learning self-efficacy is considered an important concept that has a powerful impact on learning outcomes 19 , 20 .

Self-efficacy for learning is vital in teaching students to learn and develop healthily and increasing students' beliefs in the learning process 21 . However, previous studies on social media platforms such as Twitter and Weibo as curriculum support tools have not been further substantiated or analyzed in detail. In addition, the relationship between social media, higher education, and learning self-efficacy has not yet been fully explored by researchers in China. Our research aims to fill this gap in the topic. Our study explored the impact of social media on the learning self-efficacy of Chinese college students. Therefore, it is essential to explore the impact of teachers' use of social media to support teaching and learning on students' learning self-efficacy. Based on educational theory and methodological practice, this study designed a teaching experiment using social media to promote learning self-efficacy by posting an assignment for post-course work on online media to explore the actual impact of social media on university students’ learning self-efficacy. This study examines the impact of a social media-assisted course on university students' learning self-efficacy to explore the positive impact of a social media-assisted course.

Theoretical background

• Social media

Social media has different definitions. Mayfield (2013) first introduced the concept of social media in his book-what is social media? The author summarized the six characteristics of social media: openness, participation, dialogue, communication, interaction, and communication. Mayfield 22 shows that social media is a kind of new media. Its uniqueness is that it can give users great space and freedom to participate in the communication process. Jen (2020) also suggested that the distinguishing feature of social media is that it is “aggregated”. Social media provides users with an interactive service to control their data and information and collaborate and share information 2 . Social media offers opportunities for students to build knowledge and helps them actively create and share information 23 . Millennial students are entering higher education institutions and are accustomed to accessing and using data from the Internet. These individuals go online daily for educational or recreational purposes. Social media is becoming increasingly popular in the lives of everyone, including students and researchers 1 . A previous study has shown that millennials use the Internet as their first source of information and Google as their first choice for finding educational and personal information 24 . Similarly, many institutions encourage teachers to adopt social media applications 25 . Faculty members have also embraced social media applications for personal, professional, and pedagogical purposes 17 .

Social networks allow one to create a personal profile and build various networks that connect him/her to family, friends, and other colleagues. Users use these sites to stay in touch with their friends, make plans, make new friends, or connect with someone online. Therefore, extending this concept, these sites can establish academic connections or promote cooperation and collaboration in higher education classrooms 2 . This study defines social media as an interactive community of users' information sharing and social activities built on the technology of the Internet. Because the concept of social media is broad, its connotations are consistent. Research shows that Meaning and Linking are the two key elements that make up social media existence. Users and individual media outlets generate social media content and use it as a platform to get it out there. Social media distribution is based on social relationships and has a better platform for personal information and relationship management systems. Examples of social media applications include Facebook, Twitter, MySpace, YouTube, Flickr, Skype, Wiki, blogs, Delicious, Second Life, open online course sites, SMS, online games, mobile applications, and more 18 . Ajjan and Hartshorne 2 investigated the intentions of 136 faculty members at a US university to adopt Web 2.0 technologies as tools in their courses. They found that integrating Web 2.0 technologies into the classroom learning environment effectively increased student satisfaction with the course and improved their learning and writing skills. His research focused on improving the perceived usefulness, ease of use, compatibility of Web 2.0 applications, and instructor self-efficacy. The social computing impact of formal education and training and informal learning communities suggested that learning web 2.0 helps users to acquire critical competencies, and promotes technological, pedagogical, and organizational innovation, arguing that social media has a variety of learning content 26 . Users can post digital content online, enabling learners to tap into tacit knowledge while supporting collaboration between learners and teachers. Cao and Hong 27 investigated the antecedents and consequences of social media use in teaching among 249 full-time and part-time faculty members, who reported that the factors for using social media in teaching included personal social media engagement and readiness, external pressures; expected benefits; and perceived risks. The types of Innovators, Early adopters, Early majority, Late majority, Laggards, and objectors. Cao et al. 18 studied the educational effectiveness of 168 teachers' use of social media in university teaching. Their findings suggest that social media use has a positive impact on student learning outcomes and satisfaction. Their research model provides educators with ideas on using social media in the education classroom to improve student performance. Maqableh et al. 28 investigated the use of social networking sites by 366 undergraduate students, and they found that weekly use of social networking sites had a significant impact on student's academic performance and that using social networking sites had a significant impact on improving students' effective time management, and awareness of multitasking. All of the above studies indicate the researcher’s research on social media aids in teaching and learning. All of these studies indicate the positive impact of social media on teaching and learning.

• Learning self-efficacy

For the definition of concepts related to learning self-efficacy, scholars have mainly drawn on the idea proposed by Bandura 29 that defines self-efficacy as “the degree to which people feel confident in their ability to use the skills they possess to perform a task”. Self-efficacy is an assessment of a learner’s confidence in his or her ability to use the skills he or she possesses to complete a learning task and is a subjective judgment and feeling about the individual’s ability to control his or her learning behavior and performance 30 . Liu 31 has defined self-efficacy as the belief’s individuals hold about their motivation to act, cognitive ability, and ability to perform to achieve their goals, showing the individual's evaluation and judgment of their abilities. Zhang (2015) showed that learning efficacy is regarded as the degree of belief and confidence that expresses the success of learning. Yan 32 showed the extent to which learning self-efficacy is viewed as an individual. Pan 33 suggested that learning self-efficacy in an online learning environment is a belief that reflects the learner's ability to succeed in the online learning process. Kang 34 believed that learning self-efficacy is the learner's confidence and belief in his or her ability to complete a learning task. Huang 35 considered self-efficacy as an individual’s self-assessment of his or her ability to complete a particular task or perform a specific behavior and the degree of confidence in one’s ability to achieve a specific goal. Kong 36 defined learning self-efficacy as an individual’s judgment of one’s ability to complete academic tasks.

Based on the above analysis, we found that scholars' focus on learning self-efficacy is on learning behavioral efficacy and learning ability efficacy, so this study divides learning self-efficacy into learning behavioral efficacy and learning ability efficacy for further analysis and research 37 , 38 . Search the CNKI database and ProQuest Dissertations for keywords such as “design students’ learning self-efficacy”, “design classroom self-efficacy”, “design learning self-efficacy”, and other keywords. There are few relevant pieces of literature about design majors. Qiu 39 showed that mobile learning-assisted classroom teaching can control the source of self-efficacy from many aspects, thereby improving students’ sense of learning efficacy and helping middle and lower-level students improve their sense of learning efficacy from all dimensions. Yin and Xu 40 argued that the three elements of the network environment—“learning content”, “learning support”, and “social structure of learning”—all have an impact on university students’ learning self-efficacy. Duo et al. 41 recommend that learning activities based on the mobile network learning community increase the trust between students and the sense of belonging in the learning community, promote mutual communication and collaboration between students, and encourage each other to stimulate their learning motivation. In the context of social media applications, self-efficacy refers to the level of confidence that teachers can successfully use social media applications in the classroom 18 . Researchers have found that self-efficacy is related to social media applications 42 . Students had positive experiences with social media applications through content enhancement, creativity experiences, connectivity enrichment, and collaborative engagement 26 . Students who wish to communicate with their tutors in real-time find social media tools such as web pages, blogs, and virtual interactions very satisfying 27 . Overall, students report their enjoyment of different learning processes through social media applications; simultaneously, they show satisfactory tangible achievement of tangible learning outcomes 18 . According to Bandura's 'triadic interaction theory’, Bian 43 and Shi 44 divided learning self-efficacy into two main elements, basic competence, and control, where basic competence includes the individual's sense of effort, competence, the individual sense of the environment, and the individual's sense of control over behavior. The primary sense of competence includes the individual's Sense of effort, competence, environment, and control over behavior. In this study, learning self-efficacy is divided into Learning behavioral efficacy and Learning ability efficacy. Learning behavioral efficacy includes individuals' sense of effort, environment, and control; learning ability efficacy includes individuals' sense of ability, belief, and interest.

In Fig.  1 , learning self-efficacy includes learning behavior efficacy and learning ability efficacy, in which the learning behavior efficacy is determined by the sense of effort, the sense of environment, the sense of control, and the learning ability efficacy is determined by the sense of ability, sense of belief, sense of interest. “Sense of effort” is the understanding of whether one can study hard. Self-efficacy includes the estimation of self-effort and the ability, adaptability, and creativity shown in a particular situation. One with a strong sense of learning self-efficacy thinks they can study hard and focus on tasks 44 . “Sense of environment” refers to the individual’s feeling of their learning environment and grasp of the environment. The individual is the creator of the environment. A person’s feeling and grasp of the environment reflect the strength of his sense of efficacy to some extent. A person with a shared sense of learning self-efficacy is often dissatisfied with his environment, but he cannot do anything about it. He thinks the environment can only dominate him. A person with a high sense of learning self-efficacy will be more satisfied with his school and think that his teachers like him and are willing to study in school 44 . “Sense of control” is an individual’s sense of control over learning activities and learning behavior. It includes the arrangement of individual learning time, whether they can control themselves from external interference, and so on. A person with a strong sense of self-efficacy will feel that he is the master of action and can control the behavior and results of learning. Such a person actively participates in various learning activities. When he encounters difficulties in learning, he thinks he can find a way to solve them, is not easy to be disturbed by the outside world, and can arrange his own learning time. The opposite is the sense of losing control of learning behavior 44 . “Sense of ability” includes an individual’s perception of their natural abilities, expectations of learning outcomes, and perception of achieving their learning goals. A person with a high sense of learning self-efficacy will believe that he or she is brighter and more capable in all areas of learning; that he or she is more confident in learning in all subjects. In contrast, people with low learning self-efficacy have a sense of powerlessness. They are self-doubters who often feel overwhelmed by their learning and are less confident that they can achieve the appropriate learning goals 44 . “Sense of belief” is when an individual knows why he or she is doing something, knows where he or she is going to learn, and does not think before he or she even does it: What if I fail? These are meaningless, useless questions. A person with a high sense of learning self-efficacy is more robust, less afraid of difficulties, and more likely to reach their learning goals. A person with a shared sense of learning self-efficacy, on the other hand, is always going with the flow and is uncertain about the outcome of their learning, causing them to fall behind. “Sense of interest” is a person's tendency to recognize and study the psychological characteristics of acquiring specific knowledge. It is an internal force that can promote people's knowledge and learning. It refers to a person's positive cognitive tendency and emotional state of learning. A person with a high sense of self-efficacy in learning will continue to concentrate on studying and studying, thereby improving learning. However, one with low learning self-efficacy will have psychology such as not being proactive about learning, lacking passion for learning, and being impatient with learning. The elements of learning self-efficacy can be quantified and detailed in the following Fig.  1 .

Learning self-efficacy research structure in this paper.

Research participants

All the procedures were conducted in adherence to the guidelines and regulations set by the institution. Prior to initiating the study, informed consent was obtained in writing from the participants, and the Institutional Review Board for Behavioral and Human Movement Sciences at Nanning Normal University granted approval for all protocols.

Two parallel classes are pre-selected as experimental subjects in our study, one as the experimental group and one as the control group. Social media assisted classroom teaching to intervene in the experimental group, while the control group did not intervene. When selecting the sample, it is essential to consider, as far as possible, the shortcomings of not using randomization to select or assign the study participants, resulting in unequal experimental and control groups. When selecting the experimental subjects, classes with no significant differences in initial status and external conditions, i.e. groups with homogeneity, should be selected. Our study finally decided to select a total of 44 students from Class 2021 Design 1 and a total of 29 students from Class 2021 Design 2, a total of 74 students from Nanning Normal University, as the experimental subjects. The former served as the experimental group, and the latter served as the control group. 73 questionnaires are distributed to measure before the experiment, and 68 are returned, with a return rate of 93.15%. According to the statistics, there were 8 male students and 34 female students in the experimental group, making a total of 44 students (mirrors the demographic trends within the humanities and arts disciplines from which our sample was drawn); there are 10 male students and 16 female students in the control group, making a total of 26 students, making a total of 68 students in both groups. The sample of those who took the course were mainly sophomores, with a small number of first-year students and juniors, which may be related to the nature of the subject of this course and the course system offered by the university. From the analysis of students' majors, liberal arts students in the experimental group accounted for the majority, science students and art students accounted for a small part. In contrast, the control group had more art students, and liberal arts students and science students were small. In the daily self-study time, the experimental and control groups are 2–3 h. The demographic information of research participants is shown in Table 1 .

Research procedure

Firstly, the ADDIE model is used for the innovative design of the teaching method of the course. The number of students in the experimental group was 44, 8 male and 35 females; the number of students in the control group was 29, 10 male and 19 females. Secondly, the classes are targeted at students and applied. Thirdly, the course for both the experimental and control classes is a convenient and practice-oriented course, with the course title “Graphic Design and Production”, which focuses on learning the graphic design software Photoshop. The course uses different cases to explain in detail the process and techniques used to produce these cases using Photoshop, and incorporates practical experience as well as relevant knowledge in the process, striving to achieve precise and accurate operational steps; at the end of the class, the teacher assigns online assignments to be completed on social media, allowing students to post their edited software tutorials online so that students can master the software functions. The teacher assigns online assignments to be completed on social media at the end of the lesson, allowing students to post their editing software tutorials online so that they can master the software functions and production skills, inspire design inspiration, develop design ideas and improve their design skills, and improve students' learning self-efficacy through group collaboration and online interaction. Fourthly, pre-tests and post-tests are conducted in the experimental and control classes before the experiment. Fifthly, experimental data are collected, analyzed, and summarized.

We use a questionnaire survey to collect data. Self-efficacy is a person’s subjective judgment on whether one can successfully perform a particular achievement. American psychologist Albert Bandura first proposed it. To understand the improvement effect of students’ self-efficacy after the experimental intervention, this work questionnaire was referenced by the author from “Self-efficacy” “General Perceived Self Efficacy Scale” (General Perceived Self Efficacy Scale) German psychologist Schwarzer and Jerusalem (1995) and “Academic Self-Efficacy Questionnaire”, a well-known Chinese scholar Liang 45 .  The questionnaire content is detailed in the supplementary information . A pre-survey of the questionnaire is conducted here. The second-year students of design majors collected 32 questionnaires, eliminated similar questions based on the data, and compiled them into a formal survey scale. The scale consists of 54 items, 4 questions about basic personal information, and 50 questions about learning self-efficacy. The Likert five-point scale is the questionnaire used in this study. The answers are divided into “completely inconsistent", “relatively inconsistent”, “unsure”, and “relatively consistent”. The five options of “Completely Meet” and “Compliant” will count as 1, 2, 3, 4, and 5 points, respectively. Divided into a sense of ability (Q5–Q14), a sense of effort (Q15–Q20), a sense of environment (Q21–Q28), a sense of control (Q29–Q36), a sense of Interest (Q37–Q45), a sense of belief (Q46–Q54). To demonstrate the scientific effectiveness of the experiment, and to further control the influence of confounding factors on the experimental intervention. This article thus sets up a control group as a reference. Through the pre-test and post-test in different periods, comparison of experimental data through pre-and post-tests to illustrate the effects of the intervention.

Reliability indicates the consistency of the results of a measurement scale (See Table 2 ). It consists of intrinsic and extrinsic reliability, of which intrinsic reliability is essential. Using an internal consistency reliability test scale, a Cronbach's alpha coefficient of reliability statistics greater than or equal to 0.9 indicates that the scale has good reliability, 0.8–0.9 indicates good reliability, 7–0.8 items are acceptable. Less than 0.7 means to discard some items in the scale 46 . This study conducted a reliability analysis on the effects of the related 6-dimensional pre-test survey to illustrate the reliability of the questionnaire.

From the Table 2 , the Cronbach alpha coefficients for the pre-test, sense of effort, sense of environment, sense of control, sense of interest, sense of belief, and the total questionnaire, were 0.919, 0.839, 0.848, 0.865, 0.852, 0.889 and 0.958 respectively. The post-test Cronbach alpha coefficients were 0.898, 0.888, 0.886, 0.889, 0.900, 0.893 and 0.970 respectively. The Cronbach alpha coefficients were all greater than 0.8, indicating a high degree of reliability of the measurement data.

The validity, also known as accuracy, reflects how close the measurement result is to the “true value”. Validity includes structure validity, content validity, convergent validity, and discriminative validity. Because the experiment is a small sample study, we cannot do any specific factorization. KMO and Bartlett sphericity test values are an important part of structural validity. Indicator, general validity evaluation (KMO value above 0.9, indicating very good validity; 0.8–0.9, indicating good validity; 0.7–0.8 validity is good; 0.6–0.7 validity is acceptable; 0.5–0.6 means poor validity; below 0.45 means that some items should be abandoned.

Table 3 shows that the KMO values of ability, effort, environment, control, interest, belief, and the total questionnaire are 0.911, 0.812, 0.778, 0.825, 0.779, 0.850, 0.613, and the KMO values of the post-test are respectively. The KMO values are 0.887, 0.775, 0.892, 0.868, 0.862, 0.883, 0.715. KMO values are basically above 0.8, and all are greater than 0.6. This result indicates that the validity is acceptable, the scale has a high degree of reasonableness, and the valid data.

In the graphic design and production (professional design course), we will learn the practical software with cases. After class, we will share knowledge on the self-media platform. We will give face-to-face computer instruction offline from 8:00 to 11:20 every Wednesday morning for 16 weeks. China's top online sharing platform (APP) is Tik Tok, micro-blog (Micro Blog) and Xiao hong shu. The experiment began on September 1, 2022, and conducted the pre-questionnaire survey simultaneously. At the end of the course, on January 6, 2023, the post questionnaire survey was conducted. A total of 74 questionnaires were distributed in this study, recovered 74 questionnaires. After excluding the invalid questionnaires with incomplete filling and wrong answers, 68 valid questionnaires were obtained, with an effective rate of 91%, meeting the test requirements. Then, use the social science analysis software SPSS Statistics 26 to analyze the data: (1) descriptive statistical analysis of the dimensions of learning self-efficacy; (2) Using correlation test to analyze the correlation between learning self-efficacy and the use of social media; (3) This study used a comparative analysis of group differences to detect the influence of learning self-efficacy on various dimensions of social media and design courses. For data processing and analysis, use the spss26 version software and frequency statistics to create statistics on the basic situation of the research object and the basic situation of the use of live broadcast. The reliability scale analysis (internal consistency test) and use Bartlett's sphericity test to illustrate the reliability and validity of the questionnaire and the individual differences between the control group and the experimental group in demographic variables (gender, grade, Major, self-study time per day) are explained by cross-analysis (chi-square test). In the experimental group and the control group, the pre-test, post-test, before-and-after test of the experimental group and the control group adopt independent sample T-test and paired sample T-test to illustrate the effect of the experimental intervention (The significance level of the test is 0.05 two-sided).

Results and discussion

Comparison of pre-test and post-test between groups.

To study whether the data of the experimental group and the control group are significantly different in the pre-test and post-test mean of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. The research for this situation uses an independent sample T-test and an independent sample. The test needs to meet some false parameters, such as normality requirements. Generally passing the normality test index requirements are relatively strict, so it can be relaxed to obey an approximately normal distribution. If there is serious skewness distribution, replace it with the nonparametric test. Variables are required to be continuous variables. The six variables in this study define continuous variables. The variable value information is independent of each other. Therefore, we use the independent sample T-test.

From the Table 4 , a pre-test found that there was no statistically significant difference between the experimental group and the control group at the 0.05 confidence level ( p  > 0.05) for perceptions of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two groups of test groups have the same quality in measuring self-efficacy. The experimental class and the control class are homogeneous groups. Table 5 shows the independent samples t-test for the post-test, used to compare the experimental and control groups on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief.

The experimental and control groups have statistically significant scores ( p  < 0.05) for sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief, and the experimental and control groups have statistically significant scores (t = 3.177, p  = 0.002) for a sense of competence. (t = 3.177, p  = 0.002) at the 0.01 level, with the experimental group scoring significantly higher (3.91 ± 0.51) than the control group (3.43 ± 0.73). The experimental group and the control group showed significance for the perception of effort at the 0.01 confidence level (t = 2.911, p  = 0.005), with the experimental group scoring significantly higher (3.88 ± 0.66) than the control group scoring significantly higher (3.31 ± 0.94). The experimental and control groups show significance at the 0.05 level (t = 2.451, p  = 0.017) for the sense of environment, with the experimental group scoring significantly higher (3.95 ± 0.61) than the control group scoring significantly higher (3.58 ± 0.62). The experimental and control groups showed significance for sense of control at the 0.05 level of significance (t = 2.524, p  = 0.014), and the score for the experimental group (3.76 ± 0.67) would be significantly higher than the score for the control group (3.31 ± 0.78). The experimental and control groups showed significance at the 0.01 level for sense of interest (t = 2.842, p  = 0.006), and the experimental group's score (3.87 ± 0.61) would be significantly higher than the control group's score (3.39 ± 0.77). The experimental and control groups showed significance at the 0.01 level for the sense of belief (t = 3.377, p  = 0.001), and the experimental group would have scored significantly higher (4.04 ± 0.52) than the control group (3.56 ± 0.65). Therefore, we can conclude that the experimental group's post-test significantly affects the mean scores of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. A social media-assisted course has a positive impact on students' self-efficacy.

Comparison of pre-test and post-test of each group

The paired-sample T-test is an extension of the single-sample T-test. The purpose is to explore whether the means of related (paired) groups are significantly different. There are four standard paired designs: (1) Before and after treatment of the same subject Data, (2) Data from two different parts of the same subject, (3) Test results of the same sample with two methods or instruments, 4. Two matched subjects receive two treatments, respectively. This study belongs to the first type, the 6 learning self-efficacy dimensions of the experimental group and the control group is measured before and after different periods.

Paired t-tests is used to analyze whether there is a significant improvement in the learning self-efficacy dimension in the experimental group after the experimental social media-assisted course intervention. In Table 6 , we can see that the six paired data groups showed significant differences ( p  < 0.05) in the pre and post-tests of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. There is a level of significance of 0.01 (t = − 4.540, p  = 0.000 < 0.05) before and after the sense of ability, the score after the sense of ability (3.91 ± 0.51), and the score before the Sense of ability (3.41 ± 0.55). The level of significance between the pre-test and post-test of sense of effort is 0.01 (t = − 4.002, p  = 0.000). The score of the sense of effort post-test (3.88 ± 0.66) will be significantly higher than the average score of the sense of effort pre-test (3.31 ± 0.659). The significance level between the pre-test and post-test Sense of environment is 0.01 (t = − 3.897, p  = 0.000). The average score for post- Sense of environment (3.95 ± 0.61) will be significantly higher than that of sense of environment—the average score of the previous test (3.47 ± 0.44). The average value of a post- sense of control (3.76 ± 0.67) will be significantly higher than the average of the front side of the Sense of control value (3.27 ± 0.52). The sense of interest pre-test and post-test showed a significance level of 0.01 (− 4.765, p  = 0.000), and the average value of Sense of interest post-test was 3.87 ± 0.61. It would be significantly higher than the average value of the Sense of interest (3.25 ± 0.59), the significance between the pre-test and post-test of belief sensing is 0.01 level (t = − 3.939, p  = 0.000). Thus, the average value of a post-sense of belief (4.04 ± 0.52) will be significantly higher than that of a pre-sense of belief Average value (3.58 ± 0.58). After the experimental group’s post-test, the scores for the Sense of ability, effort, environment, control, interest, and belief before the comparison experiment increased significantly. This result has a significant improvement effect. Table 7 shows that the control group did not show any differences in the pre and post-tests using paired t-tests on the dimensions of learning self-efficacy such as sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief ( p  > 0.05). It shows no experimental intervention for the control group, and it does not produce a significant effect.

The purpose of this study aims to explore the impact of social media use on college students' learning self-efficacy, examine the changes in the elements of college students' learning self-efficacy before and after the experiment, and make an empirical study to enrich the theory. This study developed an innovative design for course teaching methods using the ADDIE model. The design process followed a series of model rules of analysis, design, development, implementation, and evaluation, as well as conducted a descriptive statistical analysis of the learning self-efficacy of design undergraduates. Using questionnaires and data analysis, the correlation between the various dimensions of learning self-efficacy is tested. We also examined the correlation between the two factors, and verifies whether there was a causal relationship between the two factors.

Based on prior research and the results of existing practice, a learning self-efficacy is developed for university students and tested its reliability and validity. The scale is used to pre-test the self-efficacy levels of the two subjects before the experiment, and a post-test of the self-efficacy of the two groups is conducted. By measuring and investigating the learning self-efficacy of the study participants before the experiment, this study determined that there was no significant difference between the experimental group and the control group in terms of sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. Before the experiment, the two test groups had homogeneity in measuring the dimensionality of learning self-efficacy. During the experiment, this study intervened in social media assignments for the experimental group. The experiment used learning methods such as network assignments, mutual aid communication, mutual evaluation of assignments, and group discussions. After the experiment, the data analysis showed an increase in learning self-efficacy in the experimental group compared to the pre-test. With the test time increased, the learning self-efficacy level of the control group decreased slightly. It shows that social media can promote learning self-efficacy to a certain extent. This conclusion is similar to Cao et al. 18 , who suggested that social media would improve educational outcomes.

We have examined the differences between the experimental and control group post-tests on six items, including the sense of ability, sense of effort, sense of environment, sense of control, sense of interest, and sense of belief. This result proves that a social media-assisted course has a positive impact on students' learning self-efficacy. Compared with the control group, students in the experimental group had a higher interest in their major. They showed that they liked to share their learning experiences and solve difficulties in their studies after class. They had higher motivation and self-directed learning ability after class than students in the control group. In terms of a sense of environment, students in the experimental group were more willing to share their learning with others, speak boldly, and participate in the environment than students in the control group.

The experimental results of this study showed that the experimental group showed significant improvement in the learning self-efficacy dimensions after the experimental intervention in the social media-assisted classroom, with significant increases in the sense of ability, sense of effort, sense of environment, sense of control, sense of interest and sense of belief compared to the pre-experimental scores. This result had a significant improvement effect. Evidence that a social media-assisted course has a positive impact on students' learning self-efficacy. Most of the students recognized the impact of social media on their learning self-efficacy, such as encouragement from peers, help from teachers, attention from online friends, and recognition of their achievements, so that they can gain a sense of achievement that they do not have in the classroom, which stimulates their positive perception of learning and is more conducive to the awakening of positive effects. This phenomenon is in line with Ajjan and Hartshorne 2 . They argue that social media provides many opportunities for learners to publish their work globally, which brings many benefits to teaching and learning. The publication of students' works online led to similar positive attitudes towards learning and improved grades and motivation. This study also found that students in the experimental group in the post-test controlled their behavior, became more interested in learning, became more purposeful, had more faith in their learning abilities, and believed that their efforts would be rewarded. This result is also in line with Ajjan and Hartshorne's (2008) indication that integrating Web 2.0 technologies into classroom learning environments can effectively increase students' satisfaction with the course and improve their learning and writing skills.

We only selected students from one university to conduct a survey, and the survey subjects were self-selected. Therefore, the external validity and generalizability of our study may be limited. Despite the limitations, we believe this study has important implications for researchers and educators. The use of social media is the focus of many studies that aim to assess the impact and potential of social media in learning and teaching environments. We hope that this study will help lay the groundwork for future research on the outcomes of social media utilization. In addition, future research should further examine university support in encouraging teachers to begin using social media and university classrooms in supporting social media (supplementary file 1 ).

The present study has provided preliminary evidence on the positive association between social media integration in education and increased learning self-efficacy among college students. However, several avenues for future research can be identified to extend our understanding of this relationship.

Firstly, replication studies with larger and more diverse samples are needed to validate our findings across different educational contexts and cultural backgrounds. This would enhance the generalizability of our results and provide a more robust foundation for the use of social media in teaching. Secondly, longitudinal investigations should be conducted to explore the sustained effects of social media use on learning self-efficacy. Such studies would offer insights into how the observed benefits evolve over time and whether they lead to improved academic performance or other relevant outcomes. Furthermore, future research should consider the exploration of potential moderators such as individual differences in students' learning styles, prior social media experience, and psychological factors that may influence the effectiveness of social media in education. Additionally, as social media platforms continue to evolve rapidly, it is crucial to assess the impact of emerging features and trends on learning self-efficacy. This includes an examination of advanced tools like virtual reality, augmented reality, and artificial intelligence that are increasingly being integrated into social media environments. Lastly, there is a need for research exploring the development and evaluation of instructional models that effectively combine traditional teaching methods with innovative uses of social media. This could guide educators in designing courses that maximize the benefits of social media while minimizing potential drawbacks.

In conclusion, the current study marks an important step in recognizing the potential of social media as an educational tool. Through continued research, we can further unpack the mechanisms by which social media can enhance learning self-efficacy and inform the development of effective educational strategies in the digital age.

Data availability

The data that support the findings of this study are available from the corresponding authors upon reasonable request. The data are not publicly available due to privacy or ethical restrictions.

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Acknowledgements

This work is supported by the 2023 Guangxi University Young and middle-aged Teachers' Basic Research Ability Enhancement Project—“Research on Innovative Communication Strategies and Effects of Zhuang Traditional Crafts from the Perspective of the Metaverse” (Grant Nos. 2023KY0385), and the special project on innovation and entrepreneurship education in universities under the “14th Five-Year Plan” for Guangxi Education Science in 2023, titled “One Core, Two Directions, Three Integrations - Strategy and Practical Research on Innovation and Entrepreneurship Education in Local Universities” (Grant Nos. 2023ZJY1955), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform General Project (Category B) “Research on the Construction and Development of PBL Teaching Model in Advertising” (Grant Nos.2023JGB294), and the 2022 Guangxi Higher Education Undergraduate Teaching Reform Project (General Category A) “Exploration and Practical Research on Public Art Design Courses in Colleges and Universities under Great Aesthetic Education” (Grant Nos. 2022JGA251), and the 2023 Guangxi Higher Education Undergraduate Teaching Reform Project Key Project “Research and Practice on the Training of Interdisciplinary Composite Talents in Design Majors Based on the Concept of Specialization and Integration—Taking Guangxi Institute of Traditional Crafts as an Example” (Grant Nos. 2023JGZ147), and the2024 Nanning Normal University Undergraduate Teaching Reform Project “Research and Practice on the Application of “Guangxi Intangible Cultural Heritage” in Packaging Design Courses from the Ideological and Political Perspective of the Curriculum” (Grant Nos. 2024JGX048),and the 2023 Hubei Normal University Teacher Teaching Reform Research Project (Key Project) -Curriculum Development for Improving Pre-service Music Teachers' Teaching Design Capabilities from the Perspective of OBE (Grant Nos. 2023014), and the 2023 Guangxi Education Science “14th Five-Year Plan” special project: “Specialized Integration” Model and Practice of Art and Design Majors in Colleges and Universities in Ethnic Areas Based on the OBE Concept (Grant Nos. 2023ZJY1805), and the 2024 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project “Research on the Integration Path of University Entrepreneurship and Intangible Inheritance - Taking Liu Sanjie IP as an Example” (Grant Nos. 2024KY0374), and the 2022 Research Project on the Theory and Practice of Ideological and Political Education for College Students in Guangxi - “Party Building + Red”: Practice and Research on the Innovation of Education Model in College Student Dormitories (Grant Nos. 2022SZ028), and the 2021 Guangxi University Young and Middle-aged Teachers’ Scientific Research Basic Ability Improvement Project - "Research on the Application of Ethnic Elements in the Visual Design of Live Broadcast Delivery of Guangxi Local Products" (Grant Nos. 2021KY0891).

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The contribution of H. to this paper primarily lies in research design and experimental execution. H. was responsible for the overall framework design of the paper, setting research objectives and methods, and actively participating in data collection and analysis during the experimentation process. Furthermore, H. was also responsible for conducting literature reviews and played a crucial role in the writing and editing phases of the paper. L.'s contribution to this paper primarily manifests in theoretical derivation and the discussion section. Additionally, author L. also proposed future research directions and recommendations in the discussion section, aiming to facilitate further research explorations. Y.'s contribution to this paper is mainly reflected in data analysis and result interpretation. Y. was responsible for statistically analyzing the experimental data and employing relevant analytical tools and techniques to interpret and elucidate the data results.

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Hu, J., Lai, Y. & Yi, X. Effectiveness of social media-assisted course on learning self-efficacy. Sci Rep 14 , 10112 (2024). https://doi.org/10.1038/s41598-024-60724-0

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Gage Skidmore / Media Matters

Research/Study Research/Study

Study: Top newspapers fixate on Biden's age

Over the past five months, five of the top US newspapers have published nearly 10 times as many articles focused just on Biden's age or mental acuity as focused on just Trump’s

Written by Harrison Ray & Rob Savillo

Published 06/24/24 3:00 PM EDT

Media Matters has reviewed articles in five of the top U.S. newspapers by circulation – The Washington Post, The Wall Street Journal, The New York Times, the Los Angeles Times, and USA Today – that focused on either or both Biden's and Trump's ages or mental acuities from January 15, when the Iowa caucuses were held as the first contest in the 2024 presidential election cycle, though June 17. We considered an article to be “focused on” a candidate’s age or mental acuity when it mentioned the candidate’s age or mental acuity in the headline or lead paragraphs; some articles mentioned both the candidates' ages or mental acuities in the headline or lead paragraphs.

We found 144 articles focused on either or both Biden’s and Trump’s ages or mental acuities in the period studied, with 67% focused just on Biden’s age or mental acuity and only 7% on just Trump’s.

President Joe Biden has long dealt with right-wing attacks on his age and supposed problems with his mental acuity. Media Matters has repeatedly found that national news media harp on Biden's age or mental acuity while largely failing to highlight former President Donald Trump's – despite his similar age, frequent gaffes , and incoherent ramblings.

Since the Iowa caucuses, as Biden continued his 2024 reelection campaign at age 81, this right-wing line of attack continued to proliferate throughout mainstream media. Conservative actors – followed by mainstream media – seized on special counsel Robert Hur’s classified documents report as well as any verbal stumbles or gaffes to argue that Biden is unfit for office even though his doctor declared him healthy and he has long been prone to similar missteps not uncommon for his age or a politician constantly required to speak before cameras .

Of the 144 articles published, 97 (67%) focused just on Biden's age or mental acuity. Only 10 (7%) articles focused on just Trump’s.

In total, among the five major newspapers, nearly 10 times as many articles focused on just Biden’s age or mental acuity as focused on just Trump’s.

Broken down by paper:

• 80% of the Los Angeles Times' articles were focused on just Biden’s age or mental acuity but not Trump's while no articles were focused on just Trump's age or mental acuity and not Biden's. 20% of articles were focused on both candidates' ages or mental acuities.
• 78% of The New York Times' articles were focused on just Biden’s age or mental acuity but not Trump's while only 6% — 2 articles — were focused on just Trump's. 16% of articles were focused on both candidates' ages or mental acuities.
• 73% of The Wall Street Journal's articles were focused on just Biden's age or mental acuity but not Trump's while 3% — a single article — were focused on just Trump's age or mental acuity but not Biden's. 25% of articles were focused on both candidates' ages or mental acuities.
• 51% of The Washington Post's articles were focused on just Biden's age or mental acuity but not Trump's while 9% were focused on just Trump's age or mental acuity but not Biden's. The Post had the highest proportion (40%) of articles focused on both candidates' ages or mental acuities.
• USA Today had far fewer articles focused on either or both of the candidates' ages or mental acuities: 9 total. Of those, 5 were focused on just Biden's age or mental acuity and not Trump's while 3 articles were focused on just Trump's age or mental acuity and not Biden's.

When you add the “both” column to the separate tallies for each Biden and Trump, you get the total number of articles that focused on each candidate’s age or mental acuity, as separate individuals or jointly. Broken down by paper, those are:

• 100% of the Los Angeles Times' 20 articles were focused on Biden's age or mental acuity while 20% were focused on Trump's.
• 98% of The Wall Street Journal's 40 articles were focused on Biden's age or mental acuity while 28% were focused on Trump's.
• 94% of The New York Times' 32 articles were focused on Biden's age or mental acuity while 22% were focused on Trump's.
• 91% of The Washington Post's 43 total articles were focused on Biden's age or mental acuity while 49% were focused on Trump's.
• 6 of USA Today's 9 articles were focused on Biden's age or mental acuity while 4 were focused on Trump's.

Special counsel Robert Hur's February report, which included a mention that Biden might come across as an “elderly man with a poor memory,” drove coverage in February and March

On February 5, special counsel Robert Hur released his report on Biden's handling of classified documents, and shortly thereafter, media pounced on one line from the 388-page document, which baselessly speculated that Biden would present himself at a hypothetical trial as “a sympathetic, well-meaning, elderly man with a poor memory.” Outlets continued to focus on the “elderly man” bit well into March when Hur testified in front of the House Judiciary Committee.

Articles often speculated about Biden's ability to handle the rigors of governing due to his age despite the specter of Trump's own mental challenges. The focus on age and mental acuity continued regardless of positive first-hand accounts from staff working closely with Biden surfacing. Newspapers also at times published articles that pushed back on criticism of Biden's age or the media's focus on the age issue.

Methodology

Media Matters searched print articles in the Factiva database from the Los Angeles Times, The New York Times, USA Today, The Wall Street Journal, and The Washington Post for any of the terms “Biden,” “Trump,” or “president” within the same headline or lead paragraph as any of the terms “age,” “mental,” “acuity,” “faculty,” “health,” “70,” “77,” “78,” “80,” “81,” “82,” “86,” “70s,” or “80s” or any variations of any of the terms “fitness,” “cognitive,” “old,” “elder,” “seventy,” or “eighty” from January 15, 2024, the date of the first caucus contest in the 2024 presidential election cycle, through June 17, 2024.

We included articles, which we defined as instances when Biden’s age or mental acuity, Trump’s age or mental acuity, or both Biden’s and Trump’s ages or mental acuities were mentioned in the headline or lead paragraphs (as designated by Factiva) in the A section of the paper. We included editorial and op-eds but not letters to the editor.

For articles that mentioned only Biden's age or mental acuity in the headline or lead paragraphs, we considered them to be focused on Biden's age or mental acuity; for those that mentioned only Trump's age or mental acuity in the headline or lead paragraphs, we considered them to be focused on Trump's age or mental acuity; for those that mentioned both Biden's and Trump's ages or mental acuities in the headline or lead paragraphs, we considered them to be focused on both Biden's and Trump's ages or mental acuities.

Fall 2024 Semester

Undergraduate courses.

Composition courses that offer many sections (ENGL 101, 201, 277 and 379) are not listed on this schedule unless they are tailored to specific thematic content or particularly appropriate for specific programs and majors.

• 100-200 level

ENGL 151.S01: Introduction to English Studies

Tuesday and Thursday, 11 a.m.-12:15 p.m.

Sharon Smith

ENGL 151 serves as an introduction to both the English major and the discipline of English studies. In this class, you will develop the thinking, reading, writing and research practices that define both the major and the discipline. Much of the semester will be devoted to honing your literary analysis skills, and we will study and discuss texts from several different genres—poetry, short fiction, the novel, drama and film—as well as some literary criticism. As we do so, we will explore the language of the discipline, and you will learn a variety of key literary terms and concepts. In addition, you will develop your skills as both a writer and researcher within the discipline of English.

ENGL 201.ST1 Composition II: The Mind/Body Connection

In this section of English 201, students will use research and writing to learn more about problems that are important to them and articulate ways to address those problems. The course will focus specifically on issues related to the mind, the body and the relationship between them. The topics we will discuss during the course will include the correlation between social media and body image; the efficacy of sex education programs; the degree to which beliefs about race and gender influence school dress codes; and the unique mental and physical challenges faced by college students today. In this course, you will be learning about different approaches to argumentation, analyzing the arguments of others and constructing your own arguments. At the same time, you will be honing your skills as a researcher and developing your abilities as a persuasive and effective writer.

ENGL 201.S10 Composition II: Environmental Writing   

Monday/Wednesday/Friday 1-1:50 p.m.

Gwen Horsley

English 201 will help students develop the ability to think critically and analytically and to write effectively for other university courses and careers. This course will provide opportunities to develop analytical skills that will help students become critical readers and effective writers. Specifically, in this class, students will:

• Focus on the relationships between world environments, land, animals and humankind.
• Read various essays by environmental, conservational and regional authors.
• Produce student writings. 

Students will improve their writing skills by reading essays and applying techniques they witness in others’ work and those learned in class. This class is also a course in logical and creative thought. Students will write about humankind’s place in the world and our influence on the land and animals, places that hold special meaning to them or have influenced their lives and stories of their own families and their places and passions in the world. Students will practice writing in an informed and persuasive manner, in language that engages and enlivens readers by using vivid verbs and avoiding unnecessary passives, nominalizations and expletive constructions.

Students will prepare writing assignments based on readings and discussions of essays included in "Literature and the Environment " and other sources. They may use "The St. Martin’s Handbook," as well as other sources, to review grammar, punctuation, mechanics and usage as needed.

ENGL 201.13 Composition II: Writing the Environment

Tuesday and Thursday 9:30-10:45 a.m.

Paul Baggett

For generations, environmentalists have relied on the power of prose to change the minds and habits of their contemporaries. In the wake of fires, floods, storms and droughts, environmental writing has gained a new sense of urgency, with authors joining activists in their efforts to educate the public about the grim realities of climate change. But do they make a difference? Have reports of present and future disasters so saturated our airwaves that we no longer hear them? How do writers make us care about the planet amidst all the noise? In this course, students will examine the various rhetorical strategies employed by some of today’s leading environmental writers and filmmakers. And while analyzing their different arguments, students also will strengthen their own strategies of argumentation as they research and develop essays that explore a range of environmental concerns.

ENGL 201 Composition II: Food Writing

S17 Tuesday and Thursday 12:30-1:45 p.m.

S18 Tuesday and Thursday 2-3:15 p.m.

Jodi Andrews

In this composition class, students will critically analyze essays about food, food systems and environments, food cultures, the intersections of personal choice, market forces and policy and the values underneath these forces. Students will learn to better read like writers, noting authors’ purpose, audience organizational moves, sentence-level punctuation and diction. We will read a variety of essays including research-intensive arguments and personal narratives which intersect with one of our most primal needs as humans: food consumption. Students will rhetorically analyze texts, conduct advanced research, reflect on the writing process and write essays utilizing intentional rhetorical strategies. Through doing this work, students will practice the writing moves valued in every discipline: argument, evidence, concision, engaging prose and the essential research skills for the 21st century.

ENGL 221.S01 British Literature I

Michael S. Nagy

English 221 is a survey of early British literature from its inception in the Old English period with works such as "Beowulf" and the “Battle of Maldon,” through the Middle Ages and the incomparable writings of Geoffrey Chaucer and the Gawain - poet, to the Renaissance and beyond. Students will explore the historical and cultural contexts in which all assigned reading materials were written, and they will bring that information to bear on class discussion. Likely themes that this class will cover include heroism, humor, honor, religion, heresy and moral relativity. Students will write one research paper in this class and sit for two formal exams: a midterm covering everything up to that point in the semester, and a comprehensive final. Probable texts include the following:

• The Norton Anthology of English Literature: The Middle Ages. Ed. Alfred David, M. H. Abrams, and Stephen Greenblatt. 9th ed. New York: W. W. Norton & Company, 2012.
• The Norton Anthology of English Literature: The Sixteenth Century and Early Seventeenth Century. Ed. George M. Logan, Stephen Greenblatt, Barbara K Lewalski, and M. H. Abrams. 9th ed. New York: W. W. Norton & Company, 2012.
• The Norton Anthology of English Literature: The Restoration and the Eighteenth Century. Ed. George M. Logan, Stephen Greenblatt, Barbara K Lewalski, and M. H. Abrams. 9th ed. New York: W. W. Norton & Company, 2012.
• Gibaldi, Joseph. The MLA Handbook for Writers of Research Papers. 6th ed. New York: The Modern Language Association of America, 2003.
• Any Standard College Dictionary.

ENGL 240.S01 Juvenile Literature Elementary-5th Grade

Monday, Wednesday and Friday noon-12:50 p.m.

April Myrick

A survey of the history of literature written for children and adolescents, and a consideration of the various types of juvenile literature. Text selection will focus on the themes of imagination and breaking boundaries.

ENGL 240.ST1 Juvenile Literature Elementary-5th Grade

Randi Anderson

In English 240 students will develop the skills to interpret and evaluate various genres of literature for juvenile readers. This particular section will focus on various works of literature at approximately the K-5 grade level. We will read a large range of works that fall into this category, as well as information on the history, development and genre of juvenile literature.

Readings for this course include classical works such as "Hatchet," "Little Women", "The Lion, the Witch and the Wardrobe" and "Brown Girl Dreaming," as well as newer works like "Storm in the Barn," "Anne Frank’s Diary: A Graphic Adaptation," "Lumberjanes," and a variety of picture books. These readings will be paired with chapters from "Reading Children’s Literature: A Critical Introduction " to help develop understanding of various genres, themes and concepts that are both related to juvenile literature and also present in our readings.

In addition to exposing students to various genres of writing (poetry, historical fiction, non-fiction, fantasy, picture books, graphic novels, etc.) this course will also allow students to engage in a discussion of larger themes present in these works such as censorship, race and gender. Students’ understanding of these works and concepts will be developed through readings, research, discussion posts, exams and writing assignments designed to get students to practice analyzing poetry, picture books, informational books and transitional/easy readers.

ENGL 241.S01: American Literature I

Tuesday and Thursday 12:30-1:45 p.m.

This course provides a broad, historical survey of American literature from the early colonial period to the Civil War. Ranging across historical periods and literary genres—including early accounts of contact and discovery, narratives of captivity and slavery, poetry of revolution, essays on gender equality and stories of industrial exploitation—this class examines how subjects such as colonialism, nationhood, religion, slavery, westward expansion, race, gender and democracy continue to influence how Americans see themselves and their society.

Required Texts

• The Norton Anthology of American Literature: Package 1, Volumes A and B Beginnings to 1865, Ninth Edition. (ISBN 978-0-393-26454-8)

ENGL 283.S01 Introduction to Creative Writing

Steven Wingate

Students will explore the various forms of creative writing (fiction, nonfiction and poetry) not one at a time in a survey format—as if there were decisive walls of separation between then—but as intensely related genres that share much of their creative DNA. Through close reading and work on personal texts, students will address the decisions that writers in any genre must face on voice, rhetorical position, relationship to audience, etc. Students will produce and revise portfolios of original creative work developed from prompts and research. This course fulfills the same SGR #2 requirements ENGL 201; note that the course will involve a research project. Successful completion of ENGL 101 (including by test or dual credit) is a prerequisite.

ENGL 283.S02 Introduction to Creative Writing

Jodilyn Andrews

This course introduces students to the craft of writing, with readings and practice in at least two genres (including fiction, poetry and drama).

ENGL 283.ST1 Introduction to Creative Writing

Amber Jensen, M.A., M.F.A.

This course explores creative writing as a way of encountering the world, research as a component of the creative writing process, elements of craft and their rhetorical effect and drafting, workshop and revision as integral parts of writing polished literary creative work. Student writers will engage in the research practices that inform the writing of literature and in the composing strategies and writing process writers use to create literary texts. Through their reading and writing of fiction, poetry and creative nonfiction, students will learn about craft elements, find examples of those craft elements in published works and apply these elements in their own creative work, developed through weekly writing activities, small group and large group workshop and conferences with the instructor. Work will be submitted, along with a learning reflection and revision plan in each genre and will then be revised and submitted as a final portfolio at the end of the semester to demonstrate continued growth in the creation of polished literary writing.

• 300-400 level

ENGL 424.S01 Language Arts Methods grades 7-12  

Tuesday 6-8:50 p.m.

Danielle Harms

Techniques, materials and resources for teaching English language and literature to middle and secondary school students. Required of students in the English education option.

AIS/ENGL 447.S01: American Indian Literature of the Present 

Thursdays 3-6 p.m.

This course introduces students to contemporary works by authors from various Indigenous nations. Students examine these works to enhance their historical understanding of Indigenous peoples, discover the variety of literary forms used by those who identify as Indigenous writers, and consider the cultural and political significance of these varieties of expression. Topics and questions to be explored include:

• Genre: What makes Indigenous literature indigenous?
• Political and Cultural Sovereignty: Why have an emphasis on tribal specificity and calls for “literary separatism” emerged in recent decades, and what are some of the critical conversations surrounding such particularized perspectives?
• Gender and Sexuality: What are the intersecting concerns of Indigenous Studies and Women, Gender and Sexuality Studies, and how might these research fields inform one another?
• Trans-Indigeneity: What might we learn by comparing works across different Indigenous traditions, and what challenges do such comparisons present?
• Aesthetics: How do Indigenous writers understand the dynamics between tradition and creativity?
• Visual Forms: What questions or concerns do visual representations (television and film) by or about Indigenous peoples present?

Possible Texts

• Akiwenzie-Damm, Kateri and Josie Douglas (eds), Skins: Contemporary Indigenous Writing. IAD Press, 2000. (978-1864650327)
• Erdrich, Louise, The Sentence. Harper, 2021 (978-0062671127)
• Harjo, Joy, Poet Warrior: A Memoir. Norton, 2021 (978-0393248524)
• Harjo, Sterlin and Taika Waititi, Reservation Dogs (selected episodes)
• Talty, Morgan. Night of the Living Rez, 2022, Tin House (978-1953534187)
• Wall Kimmerer, Robin. Braiding Sweet Grass, Milkweed Editions (978-1571313560)
• Wilson, Diane. The Seed Keeper: A Novel. Milkweed Editions (978-1571311375)
• Critical essays by Alexie, Allen, Cohen, Cox, King, Kroeber, Ortiz, Piatote, Ross and Sexton, Smith, Taylor, Teuton, Treuer, Vizenor, and Womack.

ENGL 472.S01: Film Criticism

Tuesdays 2-4:50 p.m.

Jason McEntee

Do you have an appreciation for, and enjoy watching, movies? Do you want to study movies in a genre-oriented format (such as those we typically call the Western, the screwball comedy, the science fiction or the crime/gangster, to name a few)? Do you want to explore the different critical approaches for talking and writing about movies (such as auteur, feminist, genre or reception)?

In this class, you will examine movies through viewing and defining different genres while, at the same time, studying and utilizing different styles of film criticism. You will share your discoveries in both class discussions and short writings. The final project will be a formal written piece of film criticism based on our work throughout the semester. The course satisfies requirements and electives for all English majors and minors, including both the Film Studies and Professional Writing minors. (Note: Viewing of movies outside of class required and may require rental and/or streaming service fees.)

ENGL 476.ST1: Fiction

In this workshop-based creative writing course, students will develop original fiction based on strong attention to the fundamentals of literary storytelling: full-bodied characters, robust story lines, palpable environments and unique voices. We will pay particular attention to process awareness, to the integrity of the sentence, and to authors' commitments to their characters and the places in which their stories unfold. Some workshop experience is helpful, as student peer critique will be an important element of the class.

ENGL 479.01 Capstone: The Gothic

Wednesday 3-5:50 p.m.

With the publication of Horace Walpole’s "The Castle of Otranto " in 1764, the Gothic officially came into being. Dark tales of physical violence and psychological terror, the Gothic incorporates elements such as distressed heroes and heroines pursued by tyrannical villains; gloomy estates with dark corridors, secret passageways and mysterious chambers; haunting dreams, troubling prophecies and disturbing premonitions; abduction, imprisonment and murder; and a varied assortment of corpses, apparitions and “monsters.” In this course, we will trace the development of Gothic literature—and some film—from the eighteenth-century to the present time. As we do so, we will consider how the Gothic engages philosophical beliefs about the beautiful and sublime; shapes psychological understandings of human beings’ encounters with horror, terror, the fantastic and the uncanny; and intervenes in the social and historical contexts in which it was written. We’ll consider, for example, how the Gothic undermines ideals related to domesticity and marriage through representations of domestic abuse, toxicity and gaslighting. In addition, we’ll discuss Gothic texts that center the injustices of slavery and racism. As many Gothic texts suggest, the true horrors of human existence often have less to do with inexplicable supernatural phenomena than with the realities of the world in which we live. 

ENGL 485.S01: Undergraduate Writing Center Learning Assistants 

Flexible Scheduling

Nathan Serfling

Since their beginnings in the 1920s and 30s, writing centers have come to serve numerous functions: as hubs for writing across the curriculum initiatives, sites to develop and deliver workshops and resource centers for faculty as well as students, among other functions. But the primary function of writing centers has necessarily and rightfully remained the tutoring of student writers. This course will immerse you in that function in two parts. During the first four weeks, you will explore writing center praxis—that is, the dialogic interplay of theory and practice related to writing center work. This part of the course will orient you to writing center history, key theoretical tenets and practical aspects of writing center tutoring. Once we have developed and practiced this foundation, you will begin work in the writing center as a tutor, responsible for assisting a wide variety of student clients with numerous writing tasks. Through this work, you will learn to actively engage with student clients in the revision of a text, respond to different student needs and abilities, work with a variety of writing tasks and rhetorical situations, and develop a richer sense of writing as a complex and negotiated social process.

Graduate Courses

Engl 572.s01: film criticism, engl 576.st1 fiction.

In this workshop-based creative writing course, students will develop original fiction based on strong attention to the fundamentals of literary storytelling: full-bodied characters, robust story lines, palpable environments and unique voices. We will pay particular attention to process awareness, to the integrity of the sentence and to authors' commitments to their characters and the places in which their stories unfold. Some workshop experience is helpful, as student peer critique will be an important element of the class.

ENGL 605.S01 Seminar in Teaching Composition

Thursdays 1-3:50 p.m.

This course will provide you with a foundation in the pedagogies and theories (and their attendant histories) of writing instruction, a foundation that will prepare you to teach your own writing courses at SDSU and elsewhere. As you will discover through our course, though, writing instruction does not come with any prescribed set of “best” practices. Rather, writing pedagogies stem from and continue to evolve because of various and largely unsettled conversations about what constitutes effective writing and effective writing instruction. Part of becoming a practicing writing instructor, then, is studying these conversations to develop a sense of what “good writing” and “effective writing instruction” might mean for you in our particular program and how you might adapt that understanding to different programs and contexts.

As we read about, discuss and research writing instruction, we will address a variety of practical and theoretical topics. The practical focus will allow us to attend to topics relevant to your immediate classroom practices: designing a curriculum and various types of assignments, delivering the course content and assessing student work, among others. Our theoretical topics will begin to reveal the underpinnings of these various practical matters, including their historical, rhetorical, social and political contexts. In other words, we will investigate the praxis—the dialogic interaction of practice and theory—of writing pedagogy. As a result, this course aims to prepare you not only as a writing teacher but also as a nascent writing studies/writing pedagogy scholar.

At the end of this course, you should be able to engage effectively in the classroom practices described above and participate in academic conversations about writing pedagogy, both orally and in writing. Assessment of these outcomes will be based primarily on the various writing assignments you submit and to a smaller degree on your participation in class discussions and activities.

ENGL 726.S01: The New Woman, 1880–1900s 

Thursdays 3–5:50 p.m.

Katherine Malone

This course explores the rise of the New Woman at the end of the nineteenth century. The label New Woman referred to independent women who rebelled against social conventions. Often depicted riding bicycles, smoking cigarettes and wearing masculine clothing, these early feminists challenged gender roles and sought broader opportunities for women’s employment and self-determination. We will read provocative fiction and nonfiction by New Women writers and their critics, including authors such as Sarah Grand, Mona Caird, George Egerton, Amy Levy, Ella Hepworth Dixon, Grant Allen and George Gissing. We will analyze these exciting texts through a range of critical lenses and within the historical context of imperialism, scientific and technological innovation, the growth of the periodical press and discourse about race, class and gender. In addition to writing an argumentative seminar paper, students will complete short research assignments and lead discussion.

ENGL 792.ST1 Women in War: Female Authors and Characters in Contemporary War Lit

In this course, we will explore the voices of female authors and characters in contemporary literature of war. Drawing from various literary theories, our readings and discussion will explore the contributions of these voices to the evolving literature of war through archetypal and feminist criticism. We will read a variety of short works (both theoretical and creative) and complete works such as (selections subject to change): "Eyes Right" by Tracy Crow, "Plenty of Time When We Get Home" by Kayla Williams, "You Know When the Men are Gone" by Siobhan Fallon, "Still, Come Home" by Katie Schultz and "The Fine Art of Camouflage" by Lauren Johnson.

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A comparative study of the influence of communication on the adoption of digital agriculture in the united states and brazil  †.

1. Introduction

2. materials and methods, 2.1. study region, 2.2. survey instrument, 2.3. data collection, 2.4. data analysis.

• n = number of elements in the sample;
• p = probability of finding the phenomenon studied in the population;
• q = probability of not finding the phenomenon studied in the population; and
• E = margin of error.

2.5. Sample Characteristics

3. results and discussion, 3.1. technology adoption, decisions, and benefits, 3.2. level of influence from mass media, social media, and interpersonal meetings, 3.3. relationship between the adoption of technologies and communication channels, 4. conclusions, author contributions, institutional review board statement, data availability statement, acknowledgments, conflicts of interest.

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Colussi, J.; Sonka, S.; Schnitkey, G.D.; Morgan, E.L.; Padula, A.D. A Comparative Study of the Influence of Communication on the Adoption of Digital Agriculture in the United States and Brazil. Agriculture 2024 , 14 , 1027. https://doi.org/10.3390/agriculture14071027

Colussi J, Sonka S, Schnitkey GD, Morgan EL, Padula AD. A Comparative Study of the Influence of Communication on the Adoption of Digital Agriculture in the United States and Brazil. Agriculture . 2024; 14(7):1027. https://doi.org/10.3390/agriculture14071027

Colussi, Joana, Steve Sonka, Gary D. Schnitkey, Eric L. Morgan, and Antônio D. Padula. 2024. "A Comparative Study of the Influence of Communication on the Adoption of Digital Agriculture in the United States and Brazil" Agriculture 14, no. 7: 1027. https://doi.org/10.3390/agriculture14071027

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• Americans’ Views of Government’s Role: Persistent Divisions and Areas of Agreement

2. Americans’ views of government aid to poor, role in health care and Social Security

Table of contents.

• Views on the efficiency of government
• Views on the government’s regulation of business
• Confidence in the nation’s ability to solve problems
• Views on the effect of government aid to the poor
• Views on government’s role in health care
• Views on the future of Social Security
• Trust in government
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• Acknowledgments
• The American Trends Panel survey methodology

Majorities of Americans see a role for government on several safety net issues.

• 65% say the government has a responsibility to ensure all Americans have health care coverage.
• 55% say government aid to the poor does more good than harm.
• 79% say Social Security benefits should not be reduced in any way.

A majority of Americans (55%) say that, overall, government aid to the poor does more good than harm, while about four-in-ten (43%) say it does more harm than good.

Republicans and Republican-leaning independents continue to be critical of government aid to the poor:

• 65% say government aid does more harm than good, while 34% say it does more good than harm.
• However, the share of Republicans saying government aid has a positive effect is up 6 percentage points since 2022.

As in past years, about three-quarters of Democrats (76%) say government aid to the poor has an overall positive effect (23% say it does more harm).

About half of Americans (52%) now say the government should do more to help the needy, even if it means going deeper into debt. By comparison, 45% say the government can’t afford to do much more to help the needy.

Support for doing more to help those in need, even if the debt increases, is 6 percentage points lower than it was in June 2020, in the early months of the COVID-19 pandemic.

• About seven-in-ten Democrats (72%) say the government should do more to help the needy, down from 79% who said the same in 2020.
• A far smaller share of Republicans – 33% – say the government should do more even if it leads to additional debt. Views among Republicans are largely unchanged since 2020.

Government assistance to people in need

About four-in-ten Americans (41%) say the government should provide more assistance to people in need, while about a quarter say it should provide less (27%). Three-in-ten say the government is providing about the right amount of assistance.

Like other attitudes about social safety net policies, there are wide partisan differences.

Six-in-ten Democrats say the government should provide more assistance to people in need. Just one-in-ten say it should provide less, while three-in-ten say the current level is about right.

By comparison, 46% of Republicans say the government should provide less assistance, while 21% say it should provide more.Three-in-ten say the government is providing the right amount of assistance.

There are other demographic differences:

• Two-thirds of Black adults say the government should provide more assistance to people in need, while smaller shares of Asian (40%), Hispanic (39%) and White (37%) adults say the same.
• Younger adults are more likely than older adults to say the government should provide more assistance.
• A majority of lower-income adults (56%) say the government should provide more assistance. Smaller shares of middle- (36%) and upper-income (31%) adults say the same.

About two-thirds of Americans (65%) say it is the federal government’s responsibility to make sure all Americans have health care coverage, while roughly a third (34%) say it does not.

Americans are slightly more likely to say it is the government’s responsibility to ensure health care coverage for all than they were a few years ago (62% in 2021). While Democratic opinion has not changed over this period, the share of Republicans who say government has this responsibility has grown.

• Four-in-ten Republicans and Republican-leaning independents now say it is the government’s responsibility to ensure health care coverage for all, up from 32% who said this in 2022. Six-in-ten say it is not the government’s responsibility, down from 68% who said the same three years ago.
• Democrats and Democratic leaners overwhelmingly hold the view that the government has a responsibility to ensure health care coverage: 88% say this. Democrats’ views on this question are largely unchanged in recent years.

Views by party and income

Majorities of adults at all income levels say the government is responsible for ensuring health care coverage. However, lower-income adults (73%) are more likely than upper- (63%) or middle-income (62%) adults to say this.

Among Republicans, there are differences within income groups on whether government is responsible for ensuring all Americans have health care coverage:

• 56% of lower-income Republicans say it is the government’s responsibility to make sure all Americans have health care coverage, including about a quarter (24%) who say this should be done through a single national government program.
• Middle-income (36%) and upper-income Republicans (29%) are far less likely to say the government has a responsibility to ensure people have health care coverage. Majorities in both of these groups say it is not the government’s responsibility.

By comparison, more than eight-in-ten Democrats across all income levels say the government is responsible for ensuring all Americans have health care coverage.

• Overall, about half of Democrats (53%) say this should be done through a single national government program. About six-in-ten upper-income (57%) and middle-income (58%) Democrats say this, compared with about four-in-ten lower-income Democrats (43%).

Americans overwhelmingly (79%) say Social Security benefits should not be reduced in any way, including four-in-ten who say it should cover more people with greater benefits. Roughly two-in-ten (19%) say some future reductions need to be considered.

Wide majorities of both Republicans and Democrats do not support Social Security benefit reductions: 77% of Republicans and 83% of Democrats say Social Security benefits should not be reduced in any way.

However, Democrats (51%) are more likely than Republicans (29%) to say Social Security should be expanded.

Across demographic groups there is broad opposition to Social Security benefit cuts. But there are more sizable differences in support for expanding benefits:

• Black (58%) and Hispanic (51%) adults are more likely than White (33%) and Asian (38%) adults to say benefits should be expanded.
• Older adults are less likely than those in other age groups to say benefits should be expanded to cover more people with greater benefits: 26% of those 65 and older say this. By comparison, adults under 30 (51%) are most likely to favor expansion. 
• Lower-income adults are the most supportive of expanding Social Security benefits: 53% say this, compared with 39% of those in middle-income families and 23% of upper-income adults.

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“Landfill Gas: A Major Pathway for Neutral Per- and Polyfluoroalkyl Substance (PFAS) Release” Environmental Science & Technology Letters

Many municipal landfills “burp” gas from decomposing organic matter rather than letting it build up. And burps from buried waste containing per- and polyfluoroalkyl substances (PFAS) can release these “forever chemicals” into the air, say researchers in ACS’ Environmental Science & Technology Letters. Their study reports unexpectedly high levels of airborne PFAS at three landfills and demonstrates that vented gases and liquid by-products called leachates could transport similar amounts of these contaminants to the environment. 

Many municipal landfills “burp” gas from decomposing organic matter rather than letting it build up. And burps from buried waste containing per- and polyfluoroalkyl substances (PFAS) can release these “forever chemicals” into the air, say researchers in ACS’ Environmental Science & Technology Letters. Their study reports unexpectedly high levels of airborne PFAS at three landfills and demonstrates that vented gases and liquid by-products called leachates could transport similar amounts of these contaminants to the environment.

Some consumer products and commercial waste, such as children’s clothing , cosmetics and wastewater treatment sludge solids , contain PFAS — and they ultimately end up in landfills. Timothy Townsend and colleagues previously established that PFAS-containing waste can contaminate the water that seeps through landfills. This leachate is usually captured and treated before entering the environment. Landfills also produce gas that can be captured and controlled, but unlike leachate, it’s often released untreated. The burped gas is mostly made up of methane and carbon dioxide; however, two recent studies also discovered a subset of airborne PFAS called fluorotelomer alcohols, which have the potential to be toxic when inhaled and can be transported long distances. Since the prevalence of PFAS-contaminated landfill vapors isn’t yet widely known, Townsend, Ashley Lin and their team wanted to identify and measure them in vented gas at three sites in Florida.

The researchers pumped landfill gas from pipes through cartridges filled with resin that captured the airborne PFAS. They freed the compounds from the cartridges with organic solvents and analyzed the extracts for 27 neutrally charged PFAS, including fluorotelomer alcohols. Surprisingly, some of the fluorotelomer alcohol levels were up to two orders of magnitude higher than previous studies at other landfills. Three of these alcohols (abbreviated 6:2, 8:2 and 10:2) comprised most of the vaporized contaminants measured at each site. The researchers also collected leachate samples at the Florida sites and analyzed them for ionic PFAS commonly found in water samples. From this data, they estimated that the annual amount of fluorine (as a proxy for PFAS content) leaving the landfills through gas emissions could be similar to, or even greater than, the amount leaving through leachates.

Because landfills are repositories for PFAS, this work indicates that vented gas from these sites should be considered in future mitigation and management strategies to reduce potential inhalation exposure and release to the environment. Some landfills burn the vapors or trap them for energy production, and the team suggests that further research is needed to determine the degree of removal these treatments provide for airborne contaminants.

The authors acknowledge funding from the Florida Department of Environmental Protection and from the U.S. Environmental Protection Agency under the Science to Achieve Results grant program.

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