research about reading proficiency in the philippines

Reading Proficiency Level of Students: Basis for Reading Intervention Program

12 Pages Posted: 8 Jan 2016 Last revised: 5 Mar 2020

Jimmy Rey Cabardo

DepEd-Hagonoy National High School; DepEd - Lapulabao National High School

Date Written: December 10, 2015

The study determined the reading proficiency level of Year 1 to Year 3 students in HNHS-Aplaya Extension High School as basis for reading intervention program for the school year 2014-2015 using descriptive survey research design. The Philippine-Informal Reading Inventory (Phil-IRI) materials were used in assessing the level of reading proficiency of Years 1 to 3 students. The data were statistically analyzed using frequency, mean, standard deviation, t-test for independent sample and analysis of variance. All hypothetical questions will be analyzed and interpreted at 5% level of significance. The results revealed that majority of the students belonged to frustration level of reading proficiency in silent reading while in instructional level for the oral reading in which majority of the males are less proficient in reading compared to females in both silent and oral reading. There is no significant difference on the levels of reading proficiency levels of students when analyzed according to their year levels and gender. However, a significant difference on the levels of reading proficiency of students in silent and oral reading was found.

Keywords: Reading Proficiency, Language, Reading Intervention Program, Descriptive Survey, Philippines

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Jimmy Rey Cabardo (Contact Author)

Deped-hagonoy national high school ( email ).

Guihing, Hagonoy, Davao del Sur Lapulabao Digos, Davao del Sur 8006 Philippines +639273447289 (Phone)

DepEd - Lapulabao National High School ( email )

Lapulabao, Hagonoy, Davao del Sur Hagonoy, Davao del Sur 8006 Philippines +639273447289 (Phone)

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• Published: 03 May 2023

Profiling low-proficiency science students in the Philippines using machine learning

• Allan B. I. Bernardo   ORCID: orcid.org/0000-0003-3938-266X 1 ,
• Macario O. Cordel II 1 ,
• Marissa Ortiz Calleja 1 ,
• Jude Michael M. Teves 1 ,
• Sashmir A. Yap 1 &
• Unisse C. Chua 1  

Humanities and Social Sciences Communications volume  10 , Article number:  192 ( 2023 ) Cite this article

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Filipino students’ performance in global assessments of science literacy has always been low, and this was confirmed again in the PISA 2018, where Filipino learners’ average science literacy scores ranked second to last among 78 countries. In this study, machine learning approaches were used to analyze PISA data from the student questionnaire to test models that best identify the poorest-performing Filipino students. The goal was to explore factors that could help identify the students who are vulnerable to very low achievement in science and that could indicate possible targets for reform in science education in the Philippines. The random forest classifier model was found to be the most accurate and more precise, and Shapley Additive Explanations indicated 15 variables that were most important in identifying the low-proficiency science students. The variables related to metacognitive awareness of reading strategies, social experiences in school, aspirations and pride about achievements, and family/home factors, include parents’ characteristics and access to ICT with internet connections. The results of the factors highlight the importance of considering personal and contextual factors beyond the typical instructional and curricular factors that are the foci of science education reform in the Philippines, and some implications for programs and policies for science education reform are suggested.

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Introduction.

Global concerns such as the ongoing COVID pandemic and climate change crisis underscore the importance of science and technology for providing sustainable and responsible strategies for global development. Yet in many parts of the world, students’ interest and achievement in science continue to decline (Fensham, 2008 ). The Philippines is one of those countries where students are observed to have low levels of science literacy for many years now (Martin et al., 2004 ; Talisayon et al., 2006 ). This pattern was confirmed when the Philippines participated for the first time in the Program for International Student Assessment (PISA) in 2018, where the results found Filipino 15-year-olds near the bottom of the ranking among 78 countries and territories (Organisation for Economic Cooperation and Development [OECD], 2019 a, 2019 b). Some Philippine studies have tried to understand low science achievement by looking at the curriculum (Belmi and Mangali, 2020 ; Cordon and Polong, 2020 ) and instruction (Sumardani, 2021 ). In this study, we used machine learning approaches to determine the most accurate predictive models that can identify the poorest-performing science students in the PISA 2018 sample. For the variables in the predictive model, we consider a range of variables in the student questionnaire of PISA that refer to the student’s home and family background, beliefs, goals, attitudes, perceptions, and school experiences. We focus on non-instructional and non-curriculum variables with the view of understanding the variables that identify the Filipino students who are most vulnerable to poor science learning.

Filipino students’ science literacy in PISA

The Philippines participated in PISA for the first time in 2018, with students’ answering the assessments in reading, mathematics, science, and global competencies. For science literacy assessment, the PISA 2018 Framework broadly defines science literacy as “the ability to engage with science-related issues, and with the ideas of science, as a reflective citizen” (OECD, 2019 a, 2019 b, p. 100). According to the PISA science framework, scientific literacy relies on a combination of knowledge and competencies that are applied to different contexts. Student performance was reported using seven levels of proficiency, with Level 6 being the highest level of proficiency and Level 2 as the minimum level of proficiency. Students who achieve Level 2 proficiency are able to demonstrate the ability to use basic or everyday knowledge to explain scientific phenomena in familiar contexts and to interpret simple data sets. This level of proficiency serves as a baseline or minimum evidence for science literacy.

There were 7233 15-year-old Filipino students who participated in the PISA 2018 cycle (OECD, 2019 a, 2019 b), where the Philippines ranked as one of the poor-performing countries in science. The country had an average score of 357 which is significantly below the OECD average score of 489 with boys and girls performing similarly (355 and 359 average performance, respectively). Only about 22% of these students achieved Science Literacy scores at Level 2 or higher. In comparison, an average of 78% of students from OECD countries reached Level 2 or higher in the science literacy assessment. Students at Level 2 or higher can recognize the correct interpretation for familiar scientific phenomena and can use such knowledge to identify, in simple cases, whether a conclusion is valid based on the data provided. The poor performance of Filipino students is reflected in the fact that around 77% of them did not reach the minimum proficiency level. At the lowest proficiency levels (1A and 1B), students are only able to use everyday content and procedural knowledge to explain simple or familiar phenomena. Their ability to understand data and to design scientific inquiry is highly limited (OECD, 2019 a, 2019 b).

The pattern of Filipino students’ performance in PISA 2018 matches their achievement in another international assessment, the Trends in International Mathematics and Science Study (TIMSS). Similar to PISA, TIMSS measures students’ ability to apply their knowledge in different content areas of science. Performance was evaluated using benchmarks, each with a corresponding scale score: Low (400), Intermediate (475), High (550), and Advanced (625) (Mullis et al., 2020 ). Fourth-grade Filipino students who participated in the TIMSS 2019 cycle achieved an average scale score of 249, the lowest in 58 participating countries with an overall average score of around 491. Only 19% of Filipino students achieved scores in the Low benchmark or higher, which implies that the overwhelming majority of Filipino students “show limited understanding of scientific concepts and limited knowledge of foundational science facts” (Mullis et al., 2020 , p. 107).

Such consistently poor achievement levels in science are very likely the results of a wide range of interacting factors. Previous research using PISA data has attempted to identify important factors that differentiate the performance of high and low high and low scorers in PISA. For example, to determine which factors contribute to the gap between high and low PISA science scores, Alivernini and Manganelli ( 2015 ) considered factors coming from country, school, and student levels. They applied a classification and regression tree analysis to the PISA 2006 data from 25 countries to identify the factors that predicted high (above Level 4) or low (below Level 2) proficiency. The strongest country-level predictor was teacher salary. At the school level, parental pressure on the school’s standards (for low teacher salaries) and school size (for high teacher salaries) predicted students’ PISA performance. At the student level, science self-efficacy and awareness of environmental issues determined whether a student would be a low or high performance in the PISA science assessment.

In this study, we employ a similar approach to studying the variables that might explain the poor performance of most Filipino students. We compare the group of poor-performing students with the group of better-performing students and consider variables related to the student’s family/home backgrounds, beliefs, goals, attitudes, perceptions, and school experiences. Instead of using statistical approaches, we use machine learning approaches to test models that best identify and distinguish the group of poor-performing students from the better-performing ones. Machine learning approaches have been proposed as complementary to statistical approaches (Lezhnina and Kismihok, 2022 ), particularly for purposes of handling very large numbers of variables in high-dimensional datasets (like those in the PISA) while avoiding convergence problems and for developing multidimensional complex models that may feature nonlinear relationships (Hilbert et al., 2021 ; Yarkoni and Westfall, 2017 ). Such machine learning approaches have been used to study science achievement in PISA 2015 (Chen et al., 2021 ), but the study focused on identifying the top performers, not the poor performers. Such approaches have been used to study the PISA 2018 data in other countries like China (Lee, 2022 ), Singapore (Dong and Hu, 2019 ), and the Philippines (Bernardo et al., 2021 , 2022 ), but these studies focused on predicting either performance in reading, mathematics, or the average across domains, and none so far, have focused on the PISA 2018 science results. The analytic approaches are discussed in the methods section. But we first consider the range of possible predictor variables suggested by the relevant literature and that were available in the PISA student questionnaire the Filipino students answered.

Predictors of science learning and achievement

Most studies on science education in the Philippines have focused on curriculum (Balagtas et al., 2019 ; Ely, 2019 ; Morales, 2017b ), knowledge, beliefs, and practices of science teachers (Bug-os et al., 2021 ; Macugay and Bernardo, 2013 ; Orbe et al., 2018 ; Walag et al., 2020 ), and beliefs and perceptions of science learning (Alonzo and Mistades, 2021 ; Bernardo et al., 2008 ; Magalong and Prudente, 2020 ; Montebon, 2014 ); typically such studies do not empirically establish any relationship with Filipino students’ science learning or achievement. But there are some studies that do identify some predictors of Filipino students learning and achievement in chemistry, biology, physics, or some specific science lessons. And these typically fall into two types of inquiries: (a) those that investigate the learning outcomes of particular instructional strategies (Antonio and Prudente, 2021 ; Francisco and Prudente, 2022 ; Magwilang, 2016 ; Morales, 2016 , 2017a ; Orozco and Yangco, 2016 ), and (b) those that looked into student motivations and other non-cognitive student level variables as predictors of learning and achievement (Bernardo, 2021 ; Bernardo et al., 2015 ; Ganotice and King, 2014 ; King and Ganotice, 2013 , 2014 ). In this study, we worked with variables from the student self-report questionnaire of PISA 2018, so we could not study instructional strategies (i.e., the first set of studies above), but we are able to study student-level variables similar to the latter group of studies that include motivation, self-beliefs and a host of other variables that relate to students family and home backgrounds, perceptions and attitudes related to their classroom and school experiences, and their goals and aspirations for after they finish high school. We consider what the research literature suggests about such variables below, starting with student-level variables that were included in the PISA 2018 self-report survey and that were found to be important predictors of science literacy in previous PISA research in different countries.

Student factors

Certain student characteristics have been shown to influence their performance in science or scientific literacy. Gender appears to be associated with scientific literacy, with boys performing better than girls in the 2015 PISA cycle (OECD, 2016 ), but the results of numerous other studies are mixed (Cutumisu and Bulut, 2017 ; Lam and Lau, 2014 ; Sun et al., 2012 ). Affective and motivational factors seem to be important correlates of science achievement in PISA; these factors include students’ enjoyment of science and perceived value of science (Ozel et al., 2013 ), positive motivations, interest, more sophisticated epistemic beliefs (Hofverberg et al., 2022 ; She et al. 2019 ), self-efficacy, intrinsic and instrumental motivations for learning science (Kartal and Kutlu, 2017 ; Mercan, 2020 ), having a growth mindset (Bernardo, 2021 , 2022 ; Bernardo et al., 2021 ), among others. Other motivation-related processes are also associated with science literacy achievement. These include students’ projective self-assessments of their own abilities and their future aspirations (Lee and Stankov, 2018 ), perseverance and willingness to solve problems (Cutumisu and Bulut, 2017 ), and use of metacognitive strategies (Akyol et al., 2010 ; Callan et al., 2016 ). Interestingly, students’ reading skills and reading strategies have also been associated with science achievement (Barnard-Brak et al., 2017 ; Caponera et al., 2016 ). The role of reading strategies is proposed to be important as science learning depends to an extent on students’ comprehension of scientific text (Cano et al., 2014 ; Kolić-Vrhovec et al., 2011 ) and this association seems particularly important when the students are learning science in a second language instead of their home language (Van Laere et al., 2014 ), which is the case with the Filipino students who participated in PISA 2018.

Family and home factors

The socioeconomic status (SES) of students’ families has been a consistent predictor of scores in PISA (Lam and Zhou, 2021 ; Lee and Stankov, 2018 ), and this is true in the domain of science (Sun et al., 2012 ). This variable has been unpacked and many other factors associated with SES have been identified as predictors of achievement in PISA. These SES-related factors include the educational attainment and occupation of their parents (Chen et al., 2021 ; Schulze and Lemmer, 2017 ). In one such study, researchers found that parents’ education had the largest indirect effect on children’s PISA test scores (Burhan et al., 2017 ). The influence of each parent’s education, however, appears to differ. A study that analyzed the PISA 2000 performance of 30 countries found that the mother’s educational attainment had a greater impact on students’ scores than the father’s educational attainment (Marks, 2008 ). Similar to education, parents’ occupations also predicted students’ learning outcomes. Students whose parents had a higher level of occupation were found to have higher scientific competencies than students whose parents were low-skilled workers (Chi et al., 2017 ). Another variable related to SES is the students’ access to information and communication technologies (ICT) at home, particularly ICT with access to the Internet. ICT availability and use positively predicted performance in various PISA assessments (Hu et al., 2018 ; Petko et al., 2017 ; Yoon and Yun, 2023 ). We also note that studies indicate SES seems to be associated with some student-level factors. For example, SES is strongly associated with feelings of school belonging (King et al., 2022 ).

Other than SES-related factors at home, parental involvement and family investment in children’s education also appear to influence students’ academic performance (Ho and Willms, 1996 ). Using data from a national survey of Chinese students’ science literacy, Wang et al. ( 2012 ) found that students’ high scores were associated with parents’ investment in their children’s education through the purchase of educational materials and other resources at home. A study of ninth-grade students in South Africa found that family experiences, such as the learning environment at home, were related to the student’s motivation to learn science (Schulze and Lemmer, 2017 ).

School factors

The school characteristics that have been shown to influence students’ scientific literacy performance include SES (or SES composition), school enrollment size, and location. Wang et al. ( 2012 ) found that certain school characteristics, namely school standing, having libraries and computer laboratories, good relationships between teachers and students, and funding for teacher training were associated with higher science literacy scores. School SES composition was found to be strongly associated with high scientific literacy scores of Australian students (McConney and Perry, 2010 ). Analysis of Hong Kong students’ PISA scores revealed that school SES composition partly explained differences in science achievement (Sun et al., 2012 ). Class size (Bellibaş, 2016 ; although see Lam and Lau, 2014 ) and school location (Topçu et al., 2014 ) are also predictors of science achievement.

Other than these school characteristics, students’ experiences and perceptions of their classroom and school environments also predict their achievement in PISA. In a study of the performance of Chinese students in the 2015 PISA, Huang ( 2020 ) found that reported experience of bullying in school was associated with achievement scores, and this relationship was medicated by the student’s sense of belonging in school. School disciplinary climate and quality of student-teacher relationship were significant predictors in particular countries (Shin et al., 2009 ); with the effect of disciplinary climate possibly having a more positive impact on students from low SES groups but the evidence across countries is mixed (Chi et al., 2018 ; Scherer, 2020 ).

The current study

The extant literature suggests that a wide range of factors at the student, family/home, and school level are likely predictors of science literacy, although some of these factors were shown to be important predictors in some countries but not all. In this study, we explore a range of such factors to inquire which best identifies the poor-performing Filipino students in contrast to the better-performing ones. The factors explored in the study are among those in the PISA 2018 student self-report survey that Filipino students answered.

Most education research that examines relationships among such variables applies statistical approaches. In such studies, correlations can show the linear relationship between each variable of poor and better-performing groups. In the current Philippines PISA 2018 dataset where we examine 85 variables as predictors, the possible correlations are over 7000 in number. For a more complex, nonlinear system with hundreds of variables that are not independent, we believe that it is best to use machine learning models. In contrast to the standard statistical approach, machine learning models capture the high-dimensional, possibly nonlinear, interrelations among a very large number of predictors (Hilbert et al., 2021 ; Yarkoni and Westfall, 2017 ), while identifying those most relevant to prediction. And, in order for the analysis to be more valid, we argue that the model should be optimal, in this case, the model with the best accuracy. For this study, we try out different machine-learning approaches to determine the best model to uncover the relationships between these variables.

The specific objective is to use machine learning approaches to determine the most accurate model that best identifies the Filipino students who performed at the lowest levels in the science domain of PISA 2018. We sought the best model that will indicate the factors that identify the students who are vulnerable to poor learning in science in the hope that the model will call the attention of Filipino educators to the non-instructional and non-curricular factors that contribute to poor learning in science among Filipino learners. The variables that were considered included student factors (e.g., motivations, self-beliefs, goals, aspirations), family/home factors (e.g., family SES, parents’ education and occupations, learning resources at home), and classroom/school factors (e.g., instruction time for science, teacher behaviors, perceived school environment, self-reported social experiences in school).

Our methodology for determining the best model that features the most important variables that identify the low-performing Filipino student in science is summarized in Fig. 1 , which shows the different phases of our data analysis. The first step is data preparation which entails data cleaning, that is, removal of variables with 100% missing data, identification, and imputation of entries with missing values, and variable scaling. Next is feature selection which involves the careful refinement of the list of variables that may contain negative suppressors. Then, machine learning model training follows to search for the best nonlinear prediction model. Finally, the machine learning model evaluation describes quantitatively the model performance and reports variable importance.

To find the optimal computational model, the whole data processing pipeline is performed for different sets of hyperparameters, for different machine learning approaches.

The dataset

The data we used in the analysis were from the Philippine sample in the PISA 2018 data (publicly accessible at https://www.oecd.org/pisa/data/2018database/ ). PISA 2018 test items for the science subject measure the ability to engage with science-related issues as a thoughtful citizen (OECD, 2019 a, 2019 b). To assess this, the questions given are related to contexts , e.g. personal, local and global issues, both current and historical that require understanding in science and technology; to knowledge , e.g. content, procedural, and epistemic; and to competencies that exhibit the ability to explain phenomena scientifically, evaluate and design scientific inquiry, and interpret data and evidence scientifically. In addition to these, students answered background questionnaires about themselves, their homes, and the school and learning experiences. As discussed, these variables were considered in this study. The performance of students is estimated and reported as 10 plausible values with 0.88 reliability for the Philippine science data.

A two-stage stratified sampling design was followed to obtain the nationally representative sample: (a) 187 schools were randomly selected from the country’s 17 regions, with the number of schools selected proportional to the regional distribution of schools, (b) students were then randomly selected from each school. As mentioned in the introduction, the final sample was 7233 15-year-old students. From the database, we identified 85 variables that referred to student, home/family, classroom/school factors suggested by the extant literature as possible predictors of science literacy, which we measured using the first plausible value of science literacy (PV1SCIE).

Data preparation

As reported earlier, over 80% of the Filipino students who participated in PISA 2018 were found to have less than Level 2 proficiency in science. The detailed distribution of participants across the different proficiency levels is shown in Fig. 2a . Because our goal is to identify the variables that are potentially influential in identifying the extreme poor performers in science, we decided to train a binary classification model that identifies these students and to study the variables that are important in this model prediction. For the binary classification, the sample data was divided into two categories; the (1) poor-performing students, who have proficiency at Level 1b and below, and (2) better-performing students, who have proficiency at Levels 1a, 2, and above. The data distribution for the two groups is shown in Fig. 2b .

Normalized Science proficiency level distribution of students ( a ) and distribution of students with poor and better performance in science ( b ). Poor performance category is for those students who belong to Level 1b and below proficiency levels, otherwise, the students are assigned to the better performance category.

The samples were further trimmed down as part of the data preparation. Students with more than 50% of the total variables missing were dropped from the dataset, obtaining the final data distribution in Table 1 . Sampled randomly, around 80% of the data were used as the training samples for the model training while the unseen or remaining data were used as the test data.

To avoid bias in training the model, data balancing was conducted by oversampling the poor-performance samples and undersampling the better-performance samples. For the poor-performing category with 2419 samples, the Synthetic Minority Oversampling Technique or SMOTE algorithm (Chawla et al., 2002 ) was applied to increase the samples. The SMOTE first chooses a random sample from the minority class, for example, sample A. Then, it looks for its nearest neighbor of the same class, for example, sample B. The algorithm performs a convex combination of the two samples to produce the synthetic sample. For the better-performing category with 3297 samples, the Tomek Links (Tomek, 1976 ) algorithm is used to undersample the majority class. The algorithm removes the ambiguous samples from the majority class, which is the data from the majority class that is closest to a minority class data. The final number of training samples for each of the poor and better performance categories is 3214.

The list of variables was further refined by removing variables with 100% missing values (i.e., the questions were not included in the set of questions asked for Filipino students). Those remaining variables with missing values were imputed using the k -nearest neighbor algorithm, where k  = 7. Also, initial experiments showed the occurrences of negative suppressors. To minimize the number of suppressors, we removed variables with high correlation with other factors, i.e. | ⍴ | > 0.75. Finally, normalization by scaling was performed per variable. In summary, 13 variables were removed because these variables have missing values only and 20 variables were removed because they have high correlation with other variables. The final number of variables is 72 plus the scientific literacy score of PV1SCIE.

Machine learning modeling

Our approach to determining the key variables that identify Filipino students with poor performance in science used machine learning, aiming to come up with a computational model that relates the input variables to the target variables. The design for the computational model is evaluated in terms of training and test accuracy to measure the model performance in both seen and unseen data and the Area under the Region-of-Convergence curve (AUC) to determine how well the separation of data is in the model space.

Exhaustive hyperparameter search on the following computational models: support vector machines (SVM), logistic regression, multilayer perceptron (MLP), decision tree, and random forest (RF). Performance in terms of accuracy revealed that the best model is the RF classifier, having 500 estimators, maximum decision tree depth equal to 20, and maximum features equal to ceiling(log 2 71) = 7 variables per individual tree, which is the best classifier. (Please refer to Supplementary File for the performance summary of the machine learning models considered.) To illustrate the RF model, please refer to Fig. 3 . The RF model is composed of several independent decision trees that are trained independently on a random subset of data. To measure the quality of a split, entropy is used to measure the information gain.

It is composed of n  = 500 decision trees called estimators with a maximum tree depth of 20. Each input to the estimator uses only a subset of variables equal to ceil(log 2 71) or 7 variables. This minimizes the model overfitting due to the original large number of variables.

Model performance

The summary of the model performance is shown in Fig. 4 . The positive class for this study refers to the poor-performing class while the negative class refers to the better-performing class. Since the test dataset is not balanced, three performance metrics were observed: classification accuracy, precision, and recall. Accuracy is the ratio of correctly classified students, whether poor-performing or better-performing students, over the total number of students. Precision is defined as the ratio of the number of correctly predicted poor-performing students and the total number of predicted poor-performing students. Recall is the ratio of correctly predicted poor-performing students and the total number of poor performing. High precision and recall show that the model is returning accurate results (high precision), and returning a majority of all positive results (high recall).

The confusion matrix ( a ) and the ROC ( b ) summarizing the performance of the RF model in classifying the PISA 2018 Science Proficiency of Filipino students. The average accuracy is 0.74 and the area under the ROC being equal to 0.83.

The RF Classifier returned a good balance of precision and recall on the training data with values equal to 0.74, and 0.79, respectively. In addition to this balance, among the different classifiers considered, the grid-search accuracy (see Fig. 5 ), shows that the RF classifier returned the best performance with final accuracy equal to 0.74, considering the precision and recall balance. The final precision, recall and accuracy using the test data are 0.73, 0.66, and 0.74, respectively. The area under the receiver operating curve (AUC) is 0.83 which implies a fairly good-fit model. A perfect classifier has AUC = 1.0 which implies that the model was able to separate the two classes, i.e. positive and negative, of data. The worst classifier, i.e. chance level accuracy, has AUC = 0.5.

The scatterplot illustrating the range of test accuracies during the cross-validation on best machine learning models shows that the RF returned the best accuracies.

Model Interpretation

We investigated the feature importance learned during training by the RF classifier. We used Shapley additive explanations (SHAP) which is a scheme based on cooperative game theory to interpret the contributions of features in the prediction. For the RF classifier in this study, these top 15 key features or variables are shown in Fig. 6 . Footnote 1 The important variables can positively affect or negatively affect the prediction of poor performance class ( y  = 1). Particularly, one student with higher values for the variables BELONG, WORKMAST and BEINGBULLIED will negatively affect the prediction of identifying the poor performers in science. Similarly, for students with high ST164Q05IA, BSMJ, and HISEI values, the prediction of identifying poor performers in science is higher since these values positively impact this prediction. We describe the 15 variables in meaningful groupings below.

Blue bars represent variables that negatively affect the prediction of poor-performing students while red bars indicate that a variable positively affects the prediction of poor-performing students.

The largest cluster of variables relates to students’ metacognitive awareness in reading or their perceptions about the usefulness of particular metacognitive strategies when reading texts in their classes. These variables are related items, where students were asked to indicate whether the indicated strategy is useful for understanding and memorizing the texts they read. Three of the variables positively identified the poor-performing students: (ST164Q05IA) “I summarize the text in my own words,” (ST164Q04IA) “I underline important parts of the text,” and (ST164Q02IA) “I quickly read through the text twice.” These three reading strategies involve relatively low metacognitive skills and are often ineffective, and poor-performing Filipino science students tend to see them as useful. On the other hand, two of the variables negatively identified poor-performing students: (ST164Q01IA) “I concentrate on the parts of the text that are easy to understand,” and (ST164Q03IA) “After reading the text, I discuss its content with other people.” The poor-performing Filipino science students tend to perceive these strategies as not useful.

The next largest cluster of variables relates to the student’s classroom and school experiences. Sense of belonging (BELONG) and perceived cooperation among students (PERCOOP) both negatively identify poor-performing students; that is, students who perform poorly in science report a low sense of belonging and perceive less cooperation among students. These two variables suggest negative social relations experienced by poor performers in science. Fortunately, self-report of experiencing bullying (BEINGBULLIED) was also negatively identified as the poor performers in science, so they tended to report less experiences of bullying in school. The last variable related to classroom experiences was how often “Students don’t listen to what the teacher says” (ST097Q01TA), which negatively identified the poor performers in science. The poor-performing science students were less likely to say that students often do not listen to the teacher. We should clarify that the item refers to teachers who use English in their classes, which refers to teachers in several subjects including science, mathematics, and English.

Three variables relate to the students’ affective or motivational experiences. The student’s motivation to master assigned learning tasks (WORKMAST) negatively identify poor-performing students, which means they tend to have low work mastery motivation. On the other hand, the student’s expected occupational status (BSMJ) and feeling proud about the things they accomplished (ST188Q02HA) both positively identified the poor-performing students. So the students who scored very low scores in science also tended to report higher job aspirations and being proud of their accomplishments compared to others. It seems that the student’s low achievement in science is unrelated to their future occupational plans and their present sense of accomplishment.

Finally, the remaining variables relate to the student’s family and home learning resources. Having smartphones with internet access at home (ST012Q05NA) negatively identified the poor-performing students, which means they were less likely to have this learning resource. But interestingly, the mother’s education (ST005Q01TA) negatively identified the poor-performing students, but the parents’ occupational status (HISEI) positively identified the students. This means that having mothers with lower educational attainment but having parents with high-status occupations also identified the students who were performing poorly in science. We could be seeing a pattern where low achievement in science is probably not viewed or experienced as a hindrance to higher-status professions. We explore this point and other results in the discussion section.

We used machine learning approaches to explore the best model for identifying the poorest-performing Filipino students in science using the PISA 2018 data. The Random Forest model was found to have the highest accuracy performance and the SHAP analysis indicated 15 variables that identified the poorest-performing science students.

Caveats and limitations

Before we discuss the meaning and implications of the details of the results, we need to underscore some important limitations in our study. First, our study cannot speak to the instructional and curricular factors that are typically the subject of discussions on improving science education in the Philippines. Second, the predictors in the model were limited to the variables in the PISA student self-report questionnaire. While there was a wide range of variables in the student questionnaire, many of the questions referred to reading (because the 2018 cycle of PISA was focused on reading), and thus, could not be included in our study. We also did not include variables from the school-head questionnaire about school characteristics, resources, and practices; nor could we include other potentially important predictors of science achievement that were not included in the PISA. Thus, there are possibly other variables that identify poor-performing students that are beyond the scope of this inquiry.

One important caveat relates to the predictive nature of the machine learning approaches, which treat variables equally without any theoretical presuppositions. Machine learning approaches focus on prediction accuracy and is not used to test explanatory models that specify theoretical relationships among variables (Shmueli, 2010 ; Yarkoni and Westfall, 2017 ). As such, the variables identified in the most accurate model may not have any obvious theoretical connection. These caveats notwithstanding, there are useful insights revealed by the analysis, which we discuss below.

Reading strategies for learning science

Metacognitive awareness regarding five different strategies identified the poor performers in science. It may seem surprising that reading strategies play an important role in identifying poor science performers, but the results make sense if one considers that much of science learning might be based on reading science textbooks (instead of doing laboratory experiments or field projects). Research with Italian students, for example, showed a difference in science achievement between good and bad readers, regardless of whether the science items involved low or high reading demand (Caponera et al., 2016 ). There were similar associations between reading comprehension and science achievement in a study of Spanish (Cano et al., 2014 ) and Filipino students (Imam et al., 2014 ). We note that our results do not actually involve reading comprehension, but metacognitive awareness of reading strategies, similar to a study of Croatian students that established a relationship between students’ reading strategies and comprehension of scientific texts (Kolić-Vrhovec et al., 2011 ). It is plausible that poor achievers in science are those that might be adopting the wrong reading strategies in reading their science textbooks.

Families’ and students’ resources and aspirations

High social, cultural, and economic resources in the students’ families (Lam and Zhou, 2021 ; Sun et al., 2012 ) and higher professional aspirations (Lee and Stankov, 2018 ) are typically associated with better achievement. But in our results, the poor-performing students were identified by higher job aspirations and stronger pride about one’s achievements. It is as if low achievement in science was not a consideration when students think about their future occupations nor when they assess their self-worth and pride. If we consider that the lower educational attainment of the mothers and higher occupational status of parents also predicted the poor-performing, it may be that students view their poor achievement in science as not relevant to their future occupational prospects, as their parents enjoy good occupations, even if their mothers are not highly educated. This interpretation asserts that science achievement might not be valued in pragmatic terms by the students based on what they see in their elders, which might also explain the role of low work mastery in school in identifying poor-performing students. Indeed, it is possible that many high-status occupations in the Philippines do not require knowledge of science, and as such, persevering and doing well in science might not be an important motivation among the students. This interpretation will need to be verified in future studies.

Negative social experiences

It was interesting to note that experience of bullying was a negative factor in the model, so it was not the case that experience of bullying was positively linked to poorer science achievement, as was found in Chinese students (Huang, 2020 ). However, two factors that indicate relational issues in school are identified with the poor performers: reporting a low sense of belonging and low cooperation among students in school. These factors suggest that a lack of connectedness and a collective spirit might be associated with poor science performance. Trinidad ( 2020 ) found that school-level and student-level measures of school climate were predictors of Filipino students’ mathematics achievement; such social factors might also have similar roles in Filipino learners’ poor science achievement.

Access to ICT for learning

One factor that may be increasingly important in identifying poor science achievers is access to ICT devices with internet access. Studies on Filipino students; PISA achievement in reading (Bernardo et al., 2021 ) and mathematics (Bernardo et al., 2022 ) also found the same factor as a predictor of achievement, consistent with much of the research in other countries (Hu et al., 2018 ; Petko et al., 2017 ; Yoon and Yun, 2023 ; but see Bulut and Cutumisu, 2018 ). Presumably, access to the internet outside the school environment has become an important resource for learning science; perhaps not just for accessing relevant scientific knowledge available online but also as a means of communicating with classmates for information sharing, collaboration in learning activities, and supporting each other’s motivations and engagement in science learning. Filipino students without such access are disadvantaged in the domain of science.

Practical implications: Focusing on the lowest achievers

The current study provides some analysis that could inform reform efforts in the domain of science learning, particularly as it concerns the lowest-achieving Filipino students in science. The results and discussion focus on factors that seem to characterize these lowest-achieving science students, and as such provide entry points to identifying these students and designing interventions for this particular group of students. Our approach focuses on the sizable proportion (over 35%) of Filipino students who have been assessed as demonstrating extremely low competencies (levels 1b and below) in science. The Philippine educational system does not lack programs for the more gifted students in science such as special science schools (Faustino and Hiwatig, 2012 ), competitions, scholarships, and other forms of support for students pursuing advanced studies and careers in science (De La Cruz, 2022 ). But there is not much that is documented about what is being done for the students like the 35% who are demonstrating extremely low levels of scientific literacy, even if they have progressed to the high school levels of the country’s formal education system. The first important implication of our findings is that these students need to be identified and understood before their science learnings can be addressed.

We should clarify that the characterization of poor-performing Filipino students in science should not be interpreted as the opposite characterization of better- or high-performing students. It is likely that there are qualitative differences between the experiences of poor and better science learners that are not captured by simply assuming a linear relationship between the factors that predict science learning. Indeed, if our machine learning approach was applied to identify the high-achieving students (i.e., Levels 4–6), it is likely that a different set of variables will be in the best machine learning model (and that can be explored in a different study). But by implication, the characterization of the poor-performing students in the results does not point to simple instructional or curricular interventions, and we do think there are some important policy implications that can be considered by stakeholders who are concerned with improving science education achievement among Filipino learners.

Instructional programs for poor achievers

Science educators have long noted that there are profound diversities in students of different ability levels, that simply assuming that one form of good teaching fits all types of learners is no longer tenable (Ault, 2010 ; Lynch, 2001 ; Yang et al., 2019 ). In this regard, the science education reform community of stakeholders should consider moving away from a one-size-teaching-fits-all approach that tends to be designed for students in the middle range of abilities using whole class instruction, and instead, move towards approaches that consider diverse adaptive learning approaches (Yang et al., 2019 ) and differentiated instruction (Pablico et al., 2017 ) that might be more responsive to (or at least that might not simply ignore) the needs of the low achievers.

Ensuring reading skills

There is a lot of evidence that good reading strategies and reading comprehension are strongly associated with science achievement (Cano et al., 2014 ; Caponera et al., 2016 ; Kolić-Vrhovec et al., 2011 ), but Filipino learners on average have extremely poor reading skills in English (Bernardo et al., 2021 ), which is the medium-of-instruction in science. Presumably, there are science learning activities that are more experiential and discovery-oriented and less dependent on students’ reading textbooks; but a previous study of students’ perception of science classes revealed a trend of decreasing science inquiry activities accompanied by an increase in self-learning, presumably involving reading textbooks and learning modules from Grade 5 to 10 (Bernardo et al., 2008 ). If Filipino science learners will be expected to do much of their learning through textbooks and learning modules in English, there should be strong efforts to strengthen the reading strategies and skills of Filipino learners.

Science in future professions and Philippine society

We interpreted part of the results as being associated with the view that science learning and achievement are irrelevant to higher future occupational aspirations. While these interpretations are speculative, there is probably a strong basis for the view that one does not need science to attain respectable occupations in the Philippines. There are many models of successful Filipino professionals and individuals who do not seem overtly display knowledge and use of science. In this regard, efforts to improve the science achievement of Filipino students might need to reckon with the perceived irrelevance of science in Philippine society. Scholars have problematized the lack of a science culture in the Philippines (Pertierra, 2004 ), perhaps vividly displayed in the recent COVID-19 pandemic, when there was widespread uncritical sharing of misinformation on vaccines, false cures, and other scientific matters through social media and social networks (Amit et al., 2022 ) and when scientific advice on pertinent issues was diluted and filtered before decisions were made by national leaders (Vallejo and Ong, 2020 ). Beyond schools, there should be efforts to change public perceptions of the importance of science in Filipinos’ social mobility and Filipino society’s development.

Improving school climate

The poor-performing students in science were identified by reports of a low sense of belonging in school and low perceived cooperation among students. These social experiences may be associated with lower achievement as they indicate a lack of meaningful sense of connectedness with students and teachers in the school, which is associated with lower engagement in the science classes, even if the social experiences are not specifically confined or referring to the science classes. The factors that contribute to these negative social experiences might vary across schools and communities and should be understood in proper contexts. Once the nature and causes of these social experiences are better understood, appropriate contextualized interventions can be developed.

Access to ICT devices and connectivity

Previous studies have documented how ICT availability and use positively predicted student achievement (Hu et al., 2018 ; Petko et al., 2017 ; Yoon and Yun, 2023 ), and similar results were also found in Filipino students’ achievement in reading (Bernardo et al., 2021 ), mathematics (Bernardo et al., 2022 ), and now in science. Together with improving access to the internet, there should be an effort to train teachers and students how to more effectively use the internet to deepen their learning of science concepts and processes, and in ways that adapt to students’ diverse abilities, interests, motivations, and circumstances (Yang et al., 2019 ).

Conclusions

Based on the assumption that science-for-all requires all Filipino citizens to acquire the scientific literacy required to effectively engage with and contribute to Philippine society in the 21st Century, we focused on the Filipino students with the lowest levels of science achievement in PISA 2018. We used machine learning to explore the variables that best identify the poor-performing Filipino students, as these variables could be used to better track and understand their learning needs. Our study points to a cluster of variables related to the student’s reading strategies, occupational aspirations, social experiences in school, and access to ITC and the internet. The variables depart from the typical focus of reform efforts on teachers’ competencies, curriculum, and instruction. But if we truly want to improve Filipino students’ science literacy, we need to understand the experiences of students who are failing to do so, as these point to problems that need to be addressed in their learning experiences in Philippine schools.

Data availability

The data analyzed in this study are available on the PISA 2018 Database page on the website of the Organisation for Economic Co-operation and Development at https://www.oecd.org/pisa/data/2018database/ , accessed on 17 Feb 2020.

For completeness, we also conducted a SHAP analysis for the best algorithm for each of the other machine learning approaches. A comparative summary of the top 15 variables that feature in the prediction models is shown in Supplementary File.

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Acknowledgements

This research was funded by a grant to the third author from the De La Salle University-Angelo King Institute for Economic and Business Studies (AKI Research Grants 2020–2021 Project No. 500-139), and a Research Fellowship to the first author from the National Academy of Science and Technology, Philippines.

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Allan B. I. Bernardo, Macario O. Cordel II, Marissa Ortiz Calleja, Jude Michael M. Teves, Sashmir A. Yap & Unisse C. Chua

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Conceptualization: ABIB, MOC II, MOC; Machine learning methodology, MOC II; Machine learning modeling and evaluation: JMMT; Data preprocessing and feature engineering: SAY; Data visualization: UCC; Writing—original draft preparation, review, and editing: ABIB, MOC II, MOC; Project administration: MOC II, MOC; Funding acquisition: MOC, MOC II, ABIB.

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Bernardo, A.B.I., Cordel, M.O., Calleja, M.O. et al. Profiling low-proficiency science students in the Philippines using machine learning. Humanit Soc Sci Commun 10 , 192 (2023). https://doi.org/10.1057/s41599-023-01705-y

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Reading Performance Declining in the Philippines

Reading was the main subject assessed in PISA 2018, so it provides a benchmark against measuring the learning attained across Elementary and Junior High School (JHS) levels.

The impact of students repeating a grade during elementary or secondary level on their reading performance was discussed in our previous article titled “ Transition Issues Between Learning Stages in the Philippines .”

Table of Contents

Declining Performance in Reading From Early Years Into High School

PISA 2018 data shows that 15-year-old students in Grades 9 and 10 who repeated a grade at least once at the elementary level scored at least 52 points less in reading than non-repeaters, while those who repeated a grade at least once in the junior high school level scored at least 71 points less in reading than non-repeaters. This illustrates that reading fundamentals must not have been learned in the early grades.

Another important outcome of the PISA 2018 data analysis showed that more than a third of students (37%) who spent only three years in early childhood programs attained at least Level 2 proficiency in reading.

In comparison, the vast majority (96 percent) of those who spent less than a year in ECEC fell below Level 2. Students who delay entering elementary school seem likelier to score lower on reading than those who entered at the right age.

A vital element of the K to 12 Basic Education Program reform was the use of the mother tongue as the medium of instruction and learning that was introduced through the Mother Tongue-Based Multilingual Education (MTB-MLE) program.

The program was evaluated using adaptations of the Early Grade Reading Assessment (EGRA) tools to assess the reading skills of Grade 3 pupils for SY 2017-2018.

The EGRA tool assessed pupils’ foundational literacy skills, which included the following: Identifying names and sounds of letters, identifying initial sounds of words, reading words that are commonly used in their language, reading non-words using their knowledge of phonics, fluently reading and comprehending a narrative text, answering comprehension questions about a short story after listening to it, and writing a dictated sentence following proper writing convention.

The EGRA tool was adapted and translated to five selected mother tongues from different regions in the Philippines from where the data were collected.

The findings provided a baseline of students’ reading performance in selected mother tongues (MTs). They provided a pre-cursor to the use of the Early Language Literacy and Numeracy (ELLN) program discussed below.

The main finding of this research was that there were significant regional variations across language groups and subtasks in the EGRA responses for MTB-MLE that require contextualized responses to address causes unique to each region and language group.

This has important implications for teaching reading in early grades and remediation beyond Grade 3 if children do not gain reading proficiency before proceeding to Grade 4.

The ELLN program is specifically targeted toward K to Grade 3 learners with specific objectives that all K to 3 learners will be equipped with (i) fundamental literacy and numeracy skills and (ii) competencies needed for academic success in later key stages.

The assessment of ELLN 2017-18 is shown in Figure 1 and reveals that students are performing at a low proficiency in the subjects designated as English and Filipino.

It is not surprising given that English is introduced in the latter part of Grade 3, but Filipino literacy is also low.

OECD defines reading literacy as understanding, evaluating, reflecting on, and engaging with texts to achieve one’s goals, develop one’s knowledge and potential and participate in society.

When ELLNA data is further analyzed in terms of mother-tongue, the proficiency levels do not change substantially to those found for English and Filipino (as shown across 18 different mother-tongues reported earlier).

Early Language, Literacy and Numeracy (ELLNA) Mean Raw Scores

Figure 1: Early Language, Literacy and Numeracy (ELLNA) Mean Raw Scores, by Gender

The foundations for reading comprehension were already low for Grade 3 pupils in 2013, and by 2019 the national averages for English and Filipino Oral Reading Fluency had declined by 10.5 and 8.3 percentage points, respectively, as shown in Figure 2.

Oral Reading Fluency in Grade 3 English and Filipino

Figure 2: Oral Reading Fluency in Grade 3 English and Filipino, 2013 and 2019

One element of the PISA 2018 assessment related to reading results was a questionnaire to gather student perceptions about their performance and feelings towards school.

Students’ Positive Feelings and Reading Performance

Figure 3: Students’ Positive Feelings and Reading Performance

Students who reported feeling positive emotions tended to perform better in reading, and a one-unit increase in the positive feelings index was associated with an 8-score point increase in reading (after accounting for the socioeconomic profiles of students and schools).

When the positive emotions are analyzed individually, reading scores increase with the frequency with which students reported feeling each emotion, except for “feeling proud” (figure 3). Students who reported sometimes or always feeling joyful scored at least 53 points higher in reading.

PISA 2018 also found that only 31 percent of students in the Philippines, as opposed to the OECD average of 63 percent, held a growth mindset, among the lowest proportions of all participating countries.

A growth mindset is a belief that one’s abilities and intelligence are malleable and that intelligence can be developed over time. This means that 60% of Filipino students tested at age 15 have a fixed mindset, believing that intelligence is an unchangeable trait that cannot be altered through experience.

Belief in a growth mindset was associated with better reading performance, particularly among girls and advantaged students . (Figure 4).

Across OECD countries, the relationship between a growth mindset and reading performance was stronger among disadvantaged students . However, the PISA data shows that the opposite was seen in the Philippines, where a 76-score point gap was observed in favor of advantaged students.

Both girls and boys increased their scores when they endorsed a growth mindset, but the score-point difference was significantly higher at 12 points in favor of girls.

Growth Mindset and Reading Performance

Figure 4: Growth Mindset and Reading Performance, by Student Characteristics

Although this data represents a narrow band of 15-year-old-students on the cusp between JHS and SHS, it may have significant implications for teaching reading across all age groups.

A positive school classroom learning climate and the need for students to have a growth mindset is essential. With this group of Filipino students, their fixed mindset was restricting their reading performance, which may be extending across all years of schooling.

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The Difficulty of Reading Comprehension and the Proficiency of the Grade 10 Students of Aloran Trade High School, Philippines

• Adones B. Cabural Department of Education, Misamis Occidental Senior High School Department, Aloran Trade HighSchool, Philippines
• Elmae Jean S. Infantado Aloran Trade High School Labo, Aloran, Philippines

In the 2018 Program for International Student Assessment (PISA), the Philippines ranked the lowest among the 79 countries participating in the assessment. This study aimed to determine the proficiency of the grade 10 students at Aloran Trade High School and the difficulty of reading comprehension. The instruments used in this study were adapted reading materials from PISA assessments with questions needing subjective answers, categorized according to the different comprehensions proposed in Barrett’s Taxonomy. The researchers collected data from 40 grade 10 students who were selected randomly using a random sampling technique. Item analysis (item difficulty and item discrimination) and descriptive analysis (mean and standard deviation) were used to analyze the data. After tabulating the data gathered, the researchers determined that the students have low proficiency in reading comprehension. Furthermore, no comprehension has an easy difficulty with the proficiency of grade 10 students. Literal and reorganizational comprehension have moderate difficulty, while inferential and evaluative comprehensions are difficult among the students. These imply that students need to be proficient in understanding a text’s explicit and implicit information. It is therefore recommended that interventions be made to address this issue.

Author Biography

Adones b. cabural, department of education, misamis occidental senior high school department, aloran trade highschool, philippines.

Adones B.  Cabural, MScieEdPhysics SHS - TII, DepEd Misamis Occidental General Science and Physics Laboratory - in charge

Alpitche-Bunda, J., & Pineda, J., PhD. (2023). Reading Difficulties and Reading Strategies of Grade 10 Students in the New Normal. International Journal of Social Science and Human Research, 06(03). https://doi.org/10.47191/ijsshr/v6-i3-38

Anggraini, S. (2017). The Correlation between reading Comprehension and Academic Achievement of English Education Study Program Students of UIN Raden Fatah Palembang (Doctoral Dissertation, UIN Raden Fatah Palembang).

Baker, C. (2017). Quantitative research designs: Experimental, quasi-experimental, and descriptive. Evidence-based practice: An integrative approach to research, administration, and practice, 155-183.

Bharuthram, S. (2012). Making a case for the teaching of reading across the curriculum in higher education. South African Journal of Education, 32(2), 205-214.

Bilbao, M., Donguilla, C., & Vasay, M. (2016). Level of reading comprehension of the education students. International Journal of Liberal Arts, Education, Social Sciences, and Philosophical Studies, 4(1), 343-353.

Decena, A. J. (2021). A Survey on the Reading Difficulties of K-12 Learners in Selected Tagalog-speaking Provinces: Basis for Intervention. International Journal of Arts, Sciences and Education, 1(2), 219-226.

De-La-Peña, C., & Luque-Rojas, M. J. (2021). Levels of reading comprehension in higher education: systematic review and meta-analysis. Frontiers in Psychology, 12, 712901.

Elleman, A. M., & Oslund, E. L. (2019). Reading comprehension research: Implications for practice and policy. Policy Insights from the Behavioral and Brain Sciences, 6(1), 3-11.

Grove, S. K., Burns, N., & Gray, J. (2012). The practice of nursing research: Appraisal, synthesis, and generation of evidence. Elsevier Health Sciences.

Js, E. (2014). The Barrett Taxonomy of Cognitive and Affective Dimensions of Reading Comprehension. Academia. https://www.academia.edu

Livingston, C., Klopper, B., Cox, S., & Uys, C. (2015). The impact of an academic reading programme in the Bachelor of Education (intermediate and senior phase) degree. Reading & Writing-Journal of the Reading Association of South Africa, 6(1), 1-11.

Marcelo, D. N., & Santillan, J. P. (2020). Comprehension and Motivation of ESL Learners: Basis for a Reading Intervention Plan. Universal Journal of Educational Research, 8(11), 5197-5202.

Ntereke, B. B., & Ramoroka, B. T. (2017). Reading competency of first-year undergraduate students at University of Botswana: A case study. Reading & Writing-Journal of the Reading Association of South Africa, 8(1), 1-11.

Muayanah, M. (2014). Reading comprehension questions developed by English teachers at senior high schools in Surabaya. Jurnal Sosial Humaniora (JSH), 7(1), 20-44.

Oakhill, J., Cain, K., & Elbro, C. (2014). Understanding and teaching reading comprehension: A handbook. Routledge.

Oseña-Paez, D. (2022). Why 9 out of 10 Filipino children can’t read. The Manila Times. https://www.manilatimes.net

San Juan, R. (2019). Philippines lowest in reading comprehension among 79 countries. Philstar.com. https://www.philstar.com

Schleicher, A. (2019). PISA 2018: Insights and Interpretations. In OECD Publishing eBooks. OECD Publishing.

Shea, M., & Ceprano, M. A. (2017). Reading with understanding: a global expectation. Journal of Inquiry and Action in Education, 9(1), 4.

Swinson, T. (2019). Importance of Reading Comprehension in High School. Math Genie. https://www.mathgenie.com/blog/importance-of-reading-comprehension-in-high-school Understanding Item Analyses. (n.d.). Washington University. https://www.washington.edu/assessment/scanningscoring/scoring/reports/item-analysis/

Villajuan M. A. (2020). Reading Proficiency, Comprehension Level of Grade 8 Students and Readability of Afro Asian Learning Module. European Journal of Humanities and Educational Advancements, 1(4), 33-41.

Viloria, E. (2016). Senior High School: How Can it Help Me? | Edukasyon.ph. https://www.edukasyon.ph/blog/senior-high-school-how-can-it-help-me

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READING PROFICIENCY OF JUNIOR HIGH SCHOOL STUDENTS: TOWARDS THE DEVELOPMENT OF INTERVENTION PROGRAM

2018, MAED-ELT

ABSTRACT Keywords: READING PROFICIENCY, LEAST MASTERED READING SKILLS, SCHOOL-BASED READING INTERVENTION PROGRAM The study sought to determine the reading proficiency of 296 Junior High School students of Calaitan National High School in relation to the following profiles: ethnicity, first language spoken, exposure to print and non-print reading materials, and grade level. The least mastered reading skills were determined through the validated Reading Proficiency Test that includes the skills on using phonetic analysis, using contextual clues, using idioms, getting the main idea and identifying facts and opinions, predicting outcomes, drawing conclusions, following directions, using parts of the book, and using dictionaries, encyclopedia and other reference materials and using the internet. Descriptive-developmental survey method was used in the study. The instrument used in the study has two parts: the first part determines the profile of the respondents; and the second part was the 50-item reading test of the thirteen skills being tested. The following statistical treatment were used in the study: (1) percentage and frequency to describe the profile of the respondents and (2) mean and mean percentage score (MPS) to determine the least mastered reading skills (below 60 % MPS) of the respondents. The study revealed that out of thirteen (13) reading skills, identifying facts and opinions fall under beginning level with a mean percentage score of 27.75% and the rest of the skills are under developing level. Their profiles particularly exposure to print and non-print reading materials and grade level influence their reading proficiency level. The results of the study were considered in the development of the reading intervention program and the reading materials that will be used in the conduct of the intervention. It is recommended that the school administrator adopt the proposed reading intervention to aid the least mastered reading skills of the junior high school students. Researchers are encouraged to validate the content and usability of the reading materials as well as to conduct an action research on the proposed intervention program.

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Filipino students falling behind in reading, writing levels in Southeast Asia

By Arjay L. Balinbin, Senior Reporter

FIFTH GRADE STUDENTS in the Philippines are falling behind their counterparts in some Southeast Asian countries in reading, writing and mathematics, with a signi fi cant percentage of students still performing at levels expected in early years of primary education, a regional study showed.

Data from the Southeast Asia Primary Learning Metrics (SEA-PLM) 2019 released on Tuesday showed the percentage of Grade 5 Filipino students who achieved minimum proficiency in reading, writing and mathematics was signi fi cantly lower than Vietnam and Malaysia. Fifth-graders in the Philippines were at par or sometimes even worse than those in Cambodia, but performed slightly better than those in Laos and Myanmar.

The study was conducted by the Southeast Asian Ministers of Education Organization and the United Nations Children’s Fund (UNICEF), with technical support from the Australian Council for Educational Research. It assessed the performance of Grade 5 learners from selected schools in six countries — Cambodia, Laos, Myanmar, the Philippines, Vietnam, and Malaysia — in three learning domains: reading, writing, and mathematics.

The study anchored its assessment on the United Nations Sustainable Development Goal (SDG) No. 4, which is “to ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomes” by 2030.

Majority of Grade 5 students in the Philippines had a reading pro fi ciency level equivalent to that in the first years of primary school, with 27% of the students still at the level (the lowest in band scale of 2 and below to 6 and above) where they can only “match single words to an image of a familiar object or concept.”

Only 29% of grade 5 learners in the country are at the level where they are able to “read a range of everyday texts, such as simple narratives and personal opinions, and begin to engage with their meanings.”

If ranked according to the percentage of Grade 5 students performing at or above the SDG indicator by country (band 6 and above), the Philippines is the second-worst performer with 10%, following Laos with only 2%.

Vietnam had the highest percentage with 82%, followed by Malaysia (58%), then Cambodia and Myanmar, with 11% of Grade 5 students having met the minimum reading standard.

In the band 6 and above level, students are able to “understand texts with familiar structures and manage competing information.”

WRITING, MATH In terms of writing, only 1% of Grade 5 learners in the Philippines achieved “higher levels” of proficiency, or those who met the highest level in the standards used by the study. These learners are those with the ability to “write cohesive texts with detailed ideas and a good range of appropriate vocabulary.”

Almost half or 45% of Grade 5 learners in the Philippines were in the lowest band, which means they have “limited ability to present ideas in writing.”

“In Cambodia, Lao PDR, Myanmar and the Philippines, a very limited number of Grade 5 students achieved higher levels of proficiency in writing… More than 60% of students were in the 3 lowest bands. The highest performers of this group can produce very limited writing, with simple, insu ffi cient ideas and limited vocabulary. The weakest students have only limited ability to present ideas in writing,” the study said.

For mathematical literacy, only 5% and 1% of Philippine Grade 5 students met the highest levels (band 8 and band 9 and above, respectively) in this area of learning.

Band 8 identifies learners as having the ability to “think multiplicatively and convert between units” while students in band 9 and above “can reason about triangles to find an unknown side length using information about the perimeter, and they can solve problems using frequency distributions.”

The study said 18% and 23% of Grade 5 learners in the Philippines are in the lowest bands (band 2 and below and band 3, respectively).

“In Cambodia, Lao PDR, Myanmar and the Philippines, modest percentages of Grade 5 students have achieved the mathematical literacy skills expected at the end of primary school, as indicated by a SEA-PLM 2019 mathematical proficiency of Band 6 and above. This implies that in these countries the majority of Grade 5 students are still working towards mastering fundamental mathematical skills,” the study said.

The main survey data were collected towards the end of the 2018-2019 academic year from a “nationally representative sample of the whole population of students enrolled at Grade 5.” Testing was done in November 2019, while data collection was conducted in February 2019.

“Schools were selected following a systematic procedure with selection probability proportional to the number of enrolled Grade 5 students from the targeted population. A minimum of 150 schools were sampled from each participating country. One Grade 5 class was selected at random within each sampled school. All students of the selected class were sampled,” the report explained.

The tests were in the o ffi cial languages of instruction in Grade 5 in each country.

In the Philippines, mother-tongue language is used as a medium of instruction from grades 1 to 3, while Filipino and English are used for Grades 4 and 5.

While countries are facing challenges due to the pandemic, the SEA-PLM study noted this has also given “opportunities to experiment with hybrid and fl exible learning, and organizational pathways in education delivery and services.”

Among its policy recommendations are prioritizing early learning; ensuring on-time enrolment for all students; and implementing progressive learning standards in the curriculum of basic education.

In an e-mailed reply to questions, College of Education Dean Jerome T. Buenviaje of the University of the Philippines-Diliman said the assessment describes the “challenging situation of our primary education and our education system in general.”

“Early education is a crucial stage in the development of learners, thus, teachers at this level must be skillful in imparting basic functional literacies,” he said.

Mr. Buenviaje recommended that “we should place the ‘best and the brightest’ teachers at the early education stage of the Filipino learners.” Teachers should also undergo research and capacity development, he added.

“Teachers in the early grades must undergo a standard training to address this issue. When I say standard training, this should be a sort of a short course that focuses on pedagogical approaches and strategies where speci fi c skills will be assessed for certi fi cation and a recerti fi cation after a few years.”

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