National Institute for Educational Policy Research

This study explores the effectiveness of data-driven pedagogical translation (DDPT) in enhancing the grammatical and collocational competence as well as the reading comprehension skills of first-year secondary school EFL students. The study also aimed to promote students’ critical thinking dispositions. The research investigates the role of translation as a pedagogical tool to address the academic challenge of improving language proficiency among students, particularly in Arab countries. While traditionally viewed as a hindrance in language learning, modern studies highlight translation's potential for enhancing cognitive processes by leveraging the mother tongue in a pedagogical context. The research uses a quasi-experimental design with two groups: an experimental group using DDPT and a control group undergoing conventional instruction. Data was collected through pre- and post-tests assessing grammatical, collocational, and reading comprehension skills, as well as a critical thinking disposition scale. The results demonstrate statistically significant improvements in the experimental group's grammatical and collocational competence and inferential reading comprehension. Furthermore, most critical thinking disposition dimensions improved subsequent to the treatment, compared to the control group. This highlights the beneficial role of pedagogical translation in EFL instruction. The study suggests that integrating machine-assisted translation and translation activities into the EFL curriculum can significantly support language acquisition and foster deeper cognitive engagement among students.

ABSTRACT This study examines the interplay between motivational factors, self-explanation quality, and mathematics achievement among 164 junior high school students in an online learning environment. We hypothesized that intrinsic motivation would positively correlate with self-explanation quality (H1) and that self-explanation would predict academic achievement (H2). Using factor and regression analyses, we found that engaging in challenging tasks significantly enhances self-explanation quality, while excessive metacognitive reflection detracts from it. Notably, reading confidence does not consistently translate into effective self-explanation, underscoring the importance of domain-specific self-efficacy. In terms of mathematics achievement, clear learning goals and math confidence emerge as positive predictors, whereas external pressures and high engagement coupled with stress negatively affect performance. Interestingly, while mathematics grades strongly predict self-explanation quality, the reverse is not observed – self-explanation skills do not directly predict grades. These results highlight the critical role of tailored motivational strategies, goal-setting, and cognitive engagement in optimizing online learning outcomes, providing actionable insights for educational interventions and system design.

Educational recommender systems have been supporting personalized learning in various ways. However, less discussion is conducted about whether and how to personalize the strategies to generate recommendations based on student differences. In this study, we aim at investigating how students judge recommendations based on different strategies, and how these judgments relate to student characteristics. We conducted a large-scale questionnaire survey to measure students’ Big-Five personality traits, confidence in the subjects, and their judgments on six types of recommendations. The answers collected from 735 high school students in Japan indicate that students had different judgments across different recommendation strategies, but similarly for English and mathematics. Furthermore, the correlations between student characteristics and their judgments on recommendations were stronger if the subject to learn was inconsistent with the subject they preferred. The results provide insights on how to design educational recommendations that not only cater to students’ traits, but also help foster and enhance their traits for better learning.

This paper explores co-design in Japanese education for deploying data-driven educational technology and practice. Although there is a growing emphasis on data to inform educational decision-making and personalize learning experiences, challenges such as data interoperability and inconsistency with teaching goals prevent practitioners from participating. Co-design, characterized by involving various stakeholders, is instrumental in addressing the evolving needs of technology deployment. Japan's educational context aligns with co-design implementation, with a learning and evidence analytics infrastructure facilitating data collection and analysis. From the Japanese co-design practice of educational technologies, the paper highlights a 6-phase co-design framework: motivate, pilot, implement, refine, evaluate, and maintain. The practices focus on data-driven learning strategies, technology interventions, and across-context dashboards, covering assorted learning contexts in Japan. By advocating for a co-design culture and data-driven approaches to enhance education in Japan, we offer insights for education practitioners, policymakers, researchers, and industry developers.

This research introduces the self-explanation-based automated feedback (SEAF) system, aimed at alleviating the teaching burden through real-time, automated feedback while aligning with SDG 4’s sustainability goals for quality education. The system specifically targets the enhancement of self-explanation, a proven but challenging cognitive strategy that bolsters both conceptual and procedural knowledge. Utilizing a triad of core feedback mechanisms—customized messages, quality assessments, and peer-generated exemplars—SEAF aims to fill the gap left by traditional and computer-aided self-explanation methods, which often require extensive preparation and may not provide effective scaffolding for all students. In a pilot study involving 50 junior high students, those with initially limited self-explanation skills showed significant improvement after using SEAF, achieving a moderate learning effect. A resounding 91.7% of participants acknowledged the system’s positive impact on their learning. SEAF’s automated capabilities serve dual purposes: they offer a more personalized and scalable approach to student learning while simultaneously reducing the educators’ workload related to feedback provision.

In the realm of mathematics education, self-explanation stands as a crucial learning mechanism, allowing learners to articulate their comprehension of intricate mathematical concepts and strategies. As digital learning platforms grow in prominence, there are mounting opportunities to collect and utilize mathematical self-explanations. However, these opportunities are met with challenges in automated evaluation. Automatic scoring of mathematical self-explanations is crucial for preprocessing tasks, including the categorization of learner responses, identification of common misconceptions, and the creation of tailored feedback and model solutions. Nevertheless, this task is hindered by the dearth of ample sample sets. Our research introduces a semi-supervised technique using the large language model (LLM), specifically its Japanese variant, to enrich datasets for the automated scoring of mathematical self-explanations. We rigorously evaluated the quality of self-explanations across five datasets, ranging from human-evaluated originals to ones devoid of original content. Our results show that combining LLM-based explanations with mathematical material significantly improves the model’s accuracy. Interestingly, there is an optimal limit to how many synthetic self-explanation data can benefit the system. Exceeding this limit does not further improve outcomes. This study thus highlights the need for careful consideration when integrating synthetic data into solutions, especially within the mathematics discipline.

In the realm of mathematics education, self-explanation stands as a crucial learning mechanism, allowing learners to articulate their comprehension of intricate mathematical concepts and strategies. As digital learning platforms grow in prominence, there are mounting opportunities to collect and utilize mathematical self-explanations. However, these opportunities are met with challenges in automated evaluation. Automatic scoring of mathematical self-explanations is crucial for preprocessing tasks, including the categorization of learner responses, identification of common misconceptions, and the creation of tailored feedback and model solutions. Nevertheless, this task is hindered by the dearth of ample sample sets. Our research introduces a semi-supervised technique using the Language Learning Model (LLM), specifically its Japanese variant, to enrich datasets for the automated scoring of mathematical self-explanations. We rigorously evaluated the quality of self-explanations across five datasets, ranging from human-evaluated originals to ones devoid of original content. Our results show that combining LLM-based explanations with mathematical material significantly improves the model's accuracy. Interestingly, there's an optimal limit to how much synthetic self-explanation data can bene-fit the system. Exceeding this limit doesn't further improve outcomes. This study thus highlights the need for careful consideration when integrating synthetic data into solutions, especially within the mathematics discipline.