Using Machine Learning Approaches to Explore Non-Cognitive Variables Influencing Reading Proficiency in English Among Filipino Learners FINAL REPORT

Filipino students ranked last in reading proficiency among all countries/territories in the PISA 2018, with only 19% meeting the minimum (Level 2) standard. It is imperative to understand the range of factors contributing to low reading proficiency, specifically variables that can be the target of interventions to help the students with poor reading proficiency. We used machine learning approaches, specifically binary classification methods, to identify the variables that best predict low (Level 1b and lower) vs. higher (Level 1a or better) reading proficiency using the Philippine PISA data from a nationally representative sample of 15-year-old students. Several binary classification methods were applied, and the best classification model was derived using support vector machines (SVM), with 81.2% average test accuracy. The 20 variables with the highest impact in the model were identified and interpreted using the socioecological perspective of development and learning. These variables included students’ home-related resources and socioeconomic constraints, learning motivation and mindsets, reading classroom experiences with teachers, reading self-beliefs, attitudes and experiences, and social experiences in the school environment. The results were discussed with reference to the need for a system perspective to address poor proficiency that requires interconnected interventions that go beyond the students’ reading classroom

Alex: A collaborative and interactive storyteller

Alex is an interactive and collaborative storyteller who accompanies the user as he/she travels through a story adventure. Along the way, the user will be presented with a task that he needs to accomplish in order to finish the story adventure. During the adventure, the user can interact with the different character and objects in the world. Interaction with the NPCs can be in the form of engaging the user in a conversation or an activity (e.g., play). For the objects, the story would provide a narrative text describing the object that has been selected. For the NPCs, option would be presented to the user whether he wants to talks to the character or do something with the NPC (e.g. play). Throughout the adventure, the users input is based on the option he would choose. Since the system is emergent, every option chosen by the user would affect the outcome of the story. While the use progresses with the adventure, he is guided by Alex the virtual peer. Every option that the user chooses will be commented by Alex. For now, Alex is capable of taking on two roles, namely facilitator and critic. If the user makes a bad decision, Alex would take on the role of a critic and critic on the users choice. Together with Alex the virtual peer, the user is able to do collaborative storytelling. The system was developed using the MonoGame Game Engine and was tested on 34 students whose ages range from 6-8. The test results revel that the 2D environment provided was user-friendly for their age group but they were not able to grasp that the system was telling a story. Instead, they thought that it was more of an adventure game. It was noted that the dialogues provided by the system were understandable, but to some, they thought that the sentence were too long

Using Machine Learning Approaches to Explore Non-Cognitive Variables Influencing Reading Proficiency in English among Filipino Learners

Filipino students ranked last in reading proficiency among all countries/territories in the PISA 2018, with only 19% meeting the minimum (Level 2) standard. It is imperative to understand the range of factors that contribute to low reading proficiency, specifically variables that can be the target of interventions to help students with poor reading proficiency. We used machine learning approaches, specifically binary classification methods, to identify the variables that best predict low (Level 1b and lower) vs. higher (Level 1a or better) reading proficiency using the Philippine PISA data from a nationally representative sample of 15-year-old students. Several binary classification methods were applied, and the best classification model was derived using support vector machines (SVM), with 81.2% average test accuracy. The 20 variables with the highest impact in the model were identified and interpreted using a socioecological perspective of development and learning. These variables included students’ home-related resources and socioeconomic constraints, learning motivation and mindsets, classroom reading experiences with teachers, reading self-beliefs, attitudes, and experiences, and social experiences in the school environment. The results were discussed with reference to the need for a systems perspective to addresses poor proficiency, requiring interconnected interventions that go beyond students’ classroom reading

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

Abstract 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

Contrasting Profiles of Low-Performing Mathematics Students in Public and Private Schools in the Philippines: Insights from Machine Learning

Filipino students performed poorly in the 2018 Programme for International Student Assessment (PISA) mathematics assessment, with more than 50% obtaining scores below the lowest proficiency level. Students from public schools also performed worse compared to their private school counterparts. We used machine learning approaches, specifically binary classification methods, to model the variables that best identified the poor performing students (below Level 1) vs. better performing students (Levels 1 to 6) using the PISA data from a nationally representative sample of 15-year-old Filipino students. We analyzed data from students in private and public schools separately. Several binary classification methods were applied, and the best classification model for both private and public school groups was the Random Forest classifier. The ten variables with the highest impact on the model were identified for the private and public school groups. Five variables were similarly important in the private and public school models. However, there were other distinct variables that relate to students’ motivations, family and school experiences that were important in identifying the poor performing students in each school type. The results are discussed in relation to the social and social cognitive experiences of students that relate to socioeconomic contexts that differ between public and private schools

Which Filipino Students are Being Left Behind in Mathematics? Testing Machine Learning Models to Differentiate Lowest-Performing Filipino Students in Public and Private Schools in the 2018 PISA Mathematics Test

Filipino students performed poorly in the PISA 2018 mathematics assessment, with more than 50% obtaining scores below the lowest proficiency level. Students from public schools also performed worse compared to their private school counterparts. We used machine learning approaches, specifically binary classification methods, to model the variables that best identified the poor performing students (below Level 1) vs. better performing students (Levels 1 to 6) using the PISA data from a nationally representative sample of 15-year-old Filipino students. We analyzed data from students in private and public schools separately. Several binary classification methods were applied, and the best classification model for both private and public school groups was the Random Forest classifier. The 10 variables with the highest impact on the model were identified for the private and public school groups. Five variables were similarly important in the private and public school models. But there were other distinct variables that relate to students’ motivations, family, and school experiences that were important in identifying the poor performing students in each school type. The results are discussed in relation to the social and social cognitive experiences of students that relate to socioeconomic contexts that differ between public and private schools