bluuebunny/crossref_metadata_2025 huggingface dataset:
Pyzzles: Towards the Design of a Zugzwang-Inspired Learning Tool for Novice Programmers and its Effect on Debugging Skills and Self-Perceived Debugging Confidence
Authors: CALLANTA Elijah Justin
Published: January 2025

This paper presents the design and preliminary results of Pyzzles (Python Puzzles), a web-based application inspired by Zugzwang principles from chess to enhance novice programmers' debugging skills. In Zugzwang puzzles, a player must decide on the move that most benefits their position. Pyzzles replicates this concept by providing learners with code snippets containing debugging errors and defining the "best debugging move" as the minimal number of lines edited to correct the code. Partial points are awarded for suboptimal corrections. Pyzzles was tested on a small cohort of 16 participants from introductory programming classes over 14 days. While Pyzzles significantly increased users' self-perceived debugging confidence, there was no significant improvement in actual debugging skills, defined as the ability to identify erroneous lines of code. The study's small sample size limits the generalizability of these findings, and the paper concludes with suggestions for future research to better integrate Pyzzles into the broader literature on debugging tools and to explore enhancements that could support both self-perceived confidence and actual debugging proficiency.
Designing Recommendations for Productive Learning Habit-Building from Learning Logs
Authors: HSU Chia-Yu , HORIKOSHI Izumi , LI Huiyong , MAJUMDAR Rwitajit , OGATA Hiroaki
Published: January 2025

This study looks at learning habits of temporal regularity in learning activities. Building such habits involves learners' regulation of their behaviors and requires learning strategies for time management, which is a cornerstone of self-regulated learning (SRL). Given the importance of habit-building in education, Learning Analytics (LA) techniques have been applied to various long-term supports by monitoring learners' habitual behaviors from the trace data. However, building a learning habit does not always mean the productive use of time. Scant supports attend to recommending learners by building which habit can improve their learning productivity. Hence, this study proposes recommendations for productive learning habit-building from learning logs. We focus on the context of English reading in a Japanese junior high school and design an algorithm to compute a recommended learning time slot. Furthermore, we collect learners' perceptions of their productivity and learning status at different times of the day. The comparison between self-report and log data presents that learners are not aware of their learning as the detection from their learning logs. This implies the potential of the proposed recommendations for facilitating learners to build productive learning habits. Specifically, our study can suggest an optimal time in learning plans and provide learners with a sustainable cue to automate learning behaviors from long-term perspectives. By building productive learning habits, learners can become more engaged in their studies as well as lead more balanced lives.
Proficiency Modeling in Junior High Math: Adapted Cognitive Statistical Models to E-Book Learning Contexts
Authors: LIANG Changhao , TAKII Kensuke , OGATA Hiroaki
Published: January 2025

Digital learning platforms equipped with behavior sensors have provided abundant educational data. Utilizing this data, learner modeling can identify assorted learner characteristics from their behavior logs for learning analytics and dynamically update them in real time. There is a growing demand for knowledge-level modeling, moving beyond behavioral logs to assess knowledge proficiency. Based on item response theories and cognitive statistical models, existing studies estimate learning rates across various knowledge elements in each learning step in intelligent tutoring systems. However, these models, tailored to specific knowledge domains, offer limited flexibility across different knowledge units and scenarios. This paper introduces an adaptation of the basic additive factor model underpinned by logistic regression, focusing on behavior indicators. Drawing upon authentic learning data from an e-book learning infrastructure for junior high math, we examine the feasibility of our adapted models and demonstrate their potential for flexibility across knowledge units and learning phases.
Optimizing Causal Inference Approach for Exploring Shallow Reading Behavior with Generative Adversarial Networks
Authors: BAI Yu , ZHAO Fuzheng , WANG Wenhao , YIN Chengjiu
Published: January 2025

The prevalence of shallow reading in online digital learning is steadily increasing, which has sparked interest in revealing the mechanisms behind shallow reading behavior, especially analyzing the causal relationship between its constituent features and learning performance. However, current causal analysis methods have many limitations in terms of experimental conditions, data independence assumptions, and analysis costs. Drawing on the application experience of Markov chain theory in the field of causality, this study adopts the structure-agnostic model (SAM) algorithm to design the structure, parameter loss, and learning process, and proposes an evaluation method for causal exploration based on generative adversarial neural networks (GANs). The study shows that the proposed maximum mean diversity (MMD) optimization method improves the stability of the model analysis results and clarifies that reading speed is a key factor in the occurrence of shallow reading behavior.
Exploring Linguistic Sophistication of Discussion Board Posts in University Learning Management Systems
Authors: BANAWAN Michelle P. , MONTEROZO Clarence James , RODRIGO Maria Mercedes T.
Published: January 2025

This study characterizes linguistic sophistication within university-based online courses using Learning Management Systems (LMS) across various academic disciplines. The research employs natural language processing tools to extract detailed linguistic features from student discussion posts and utilizes Principal Components Analysis (PCA) to identify distinct linguistic profiles. These profiles are analyzed to understand how linguistic sophistication varies across different educational contexts, specifically among various schools and courses. Subsequent cluster analysis reveals statistically significant distinct groups based on linguistic attributes. Despite the comprehensive analysis, the study did not establish significant predictive models linking linguistic sophistication to any direct educational outcomes. Instead, the findings highlight significant differences in language use across disciplines, suggesting that each academic field may have unique linguistic norms. The study emphasizes the need for further research to explore the underlying factors that influence these linguistic characteristics and their implications for educational practices.
Boosting Course Recommendation Explainability: A Knowledge Entity Aware Model Using Deep Learning
Authors: YANG Tianyuan , REN Baofeng , MA Boxuan , HE Tianjia , GU Chenghao , KONOMI Shin'ichi
Published: January 2025

Course recommender systems can assist students in identifying suitable or appealing courses by leveraging user interaction data. However, a prevalent issue with existing course recommender systems is their tendency to prioritize accuracy over explainability. To address this limitation, we propose a novel Knowledge Entity-Aware Model for course recommendation called KEAM, which supports explicit user profile generation based on detailed information from a knowledge graph to enhance comprehension of the students. Specifically, we exploit the information within knowledge graphs using neural networks. Then, KEAM captures students' preferences and creates profiles for explainable recommendations. Comprehensive experiments are conducted on two datasets to verify the effectiveness and explainability of KEAM.
Utilization of Japanese Public Educational Data by Retrieval Augmented Generation for Policy Research
Authors: TAKAMI Kyosuke
Published: January 2025

Public educational data, including government-conducted national surveys and research cases, are widely available to the public and intended for use in municipal policymaking. However, some of this data has been published in PDF format and remains underutilized. Therefore, this study leverages new tools in the era of generative Al, such as Large Language Model (LLM) and Retrieval Augmented Generation (RAG), to process 705 public educational document PDF files in Japanese. This process involves extracting text, vectorizing it, and generating responses, thereby presenting a case study of methods for effectively utilizing public educational data. This study revealed that without using the RAG, the outputs from GPT-3.5 and GPT-4 were verbose, while the use of the RAG led to more specific answers based on the retrieval results. Furthermore, GPT-4 can be used to evaluate the quality of retrieval results. These results demonstrate that LLMs can be applied to local educational knowledge in countries with local languages, such as Japanese, and suggest that previously underutilized educational data can be leveraged to aid in formulating educational policies.
Early Detection of At-risk Students Through Leaning-Activity Forecasting
Authors: OZAKI Yuya , DEGUCHI Daisuke , KYUTOKU Haruya , MURASE Hiroshi
Published: January 2025

With the widespread adoption of digital technologies such as digital textbooks, it has become feasible to collect daily logs of students' learning activities. Accordingly, there has been a growing trend in research using these logs. One of these areas is focusing on predicting grade of each student based on these learning activity logs. However, previous research focused on detecting At-risk students when learning activity logs for all lectures are available. This is not applicable for detection at the first few lectures (i.e. weeks) required in practical usage scenarios. We call this scenario as "early detection" in this paper. However, in early detection, the accuracy of at-risk detection tends to decrease. To solve this problem, we propose a Learning-activity Forecasting Network (LFNet) that improves the accuracy of early detection by aligning the embedding of the first few lectures with that of all lectures. Through experiments on learning activity logs of actual lectures, we confirmed that the proposed method could achieve high At-risk detection accuracy even from the first few lectures of learning activity logs.
Exploring the Relationship Between Assignment Submission Behavior and Final Grade of Information Literacy Education Using Big Data
Authors: OE Yuki , KUMAMOTO Etsuko , LI Huiyong , YIN Chengjiu
Published: January 2025

This study aims to investigate the relationship between students' assignment submission behavior and final grades in information literacy education using a large volume of learning logs stored on the LMS. A total of 12,516 freshman students participated in this study from the year 2018 to 2022, across the COVID-19 pandemic. The students were divided into high, medium, and low performance groups using k-means clustering. The results of the characteristics analysis show a significant early submission behavioral trend and a late submission behavioral trend in high and low performance groups, respectively. The on-demand class format during the COVID-19 pandemic resulted in more consistent early submission behavior for high performance students and late submission behavior for low performance students, respectively. The findings suggest that time management skill is a critical factor in both blended and online learning environments, affecting weekly submission behavior and final grades.
Marrying Physical and Virtual Realms: An Embodied, Multi-Modal Approach to Situational Learning in Digital Reality
Authors: AL HAKIM Vando Gusti , CHEN Yao-En , LIN Meng-Heng , CHANG Chia-Ying , WANG Jen-Hang , CHANG Chih-Kai , ZHUANG YungYu , YANG Su-Hang , CHEN Gwo-Dong
Published: January 2025

In the realm of digital education, traditional virtual reality setups often confine students to basic interactions through devices like a mouse and handheld controllers, limiting immersive learning opportunities, particularly in disciplines requiring multi- modal knowledge such as hospitality and teacher training. Multi-modal knowledge encompasses physical actions, gestures, facial expressions, and other non-verbal communication. This study investigates a novel educational approach that synchronizes physical settings with digital reality scenarios, aided by Al Stereo Cameras (e.g., Apple Vision Pro or MetaQuest), to create a more interactive learning experience. A three-group quasi-experimental design involved 103 Taiwanese students practicing hospitality tasks, such as table service in a simulated Japanese restaurant. The groups included: Group A, handling physical utensils in a real-world-like setting; Group B, controlling virtual utensils with a mouse; and a control group, learning through conventional teaching. Findings reveal that Group A reported significantly better learning outcomes and reduced cognitive load compared to Groups B and C. The reduced cognitive load was attributed to the use of real physical utensils, which minimized the need for students to learn how to navigate virtual utensils using a mouse. The findings underscore the benefits of incorporating physical objects into VR environments, bridging the gap between traditional and digital educational modalities. This approach offers a practical framework for situated learning in educational settings, incorporating embodied and multi-modal learning, and suggesting its broad applicability in improving educational practices and outcomes in the digital age.
Classifying Self-Reflection Notes: Automation Approaches for GOAL System
Authors: WANG Zixu , HSU Chia-Yu , HIROKOSHI Izumi , LI Huiyong , MAJUMDAR Rwitajit , OGATA Hiroaki
Published: January 2025

Self-directed learning (SDL) is considered a crucial skill for 21st-century learners, promoting personalized and responsive educational experiences. This study explores the untapped potential of self-reflection, particularly in e-learning environments. The research focuses on self-reflection notes, which contain strategies past students adopted when facing different situations or challenges. These notes can help current or future students facing similar situations. In this study, students take tests weekly and leave their self-reflection notes after tests. In these notes students recorded their feelings and issues, offering perspectives and insights that experts might overlook or misunderstand in some details, thus failing to provide appropriate assistance. Extracting and categorizing information from self-reflection notes is crucial to further utilize this data. Our research introduces a machine learning-based approach that effectively classifies these self-reflection notes such as cognitive, metacognitive, experiential, and irrelevant text. We compare the performance of BERT, based on the transformer architecture, with traditional machine learning classifiers such as Support Vector Machines (SVM) and Random Forests (RF). Additionally, we enhanced the BERT model by training it on synthetic data generated through GPT-4 and employing a hybrid loss combining Supervised Contrastive Learning (SCL) and Cross-Entropy (CE) to improve classification capabilities. Our results indicate that the BERT model, enhanced with advanced training techniques, outperforms traditional models in classifying learning strategies from self-reflection notes. This study not only advances the understanding of SDL in online learning environments but also demonstrates the potential of tailored machine-learning solutions to foster more effective and adaptive learning strategies.
Participatory Design of an AI Digital Textbook with Deaf and Hard-of-Hearing Students
Authors: LEE Ga Young , CHOI Jieun , NA Seonhee , SO Hyo-Jeong
Published: January 2025

Deaf and hard-of-hearing (DHH) children often encounter substantial hurdles in their language development and literacy due to their limited language exposure, which leads to lower print literacy levels compared to their hearing peers. The advent of Al technology offers a promising way to address these challenges when integrated into reading materials. This study examines the design prototype for a Korean Al digital textbook (AIDT) for DHH students, employing participatory design involving both teachers and DHH students. Through encompassing classroom observations, teacher interviews, and prototype drawing activities with students, the study identifies features essential for enhancing the learning experience of DHH students through AIDT. Teacher interviews revealed several critical needs, including the necessity to restructure existing textbooks for DHH learners, incorporate diverse visual materials, address unique linguistic challenges, provide personalized instruction, and support the comprehension of abstract concepts. Furthermore, the prototype drawings by DHH students revealed additional requirements such as the ability to draw pictures from written text, create and share visual materials with teachers and classmates, simplify complex reading materials, provide immediate feedback, and utilize visual aids. These findings underscore the potential of AIDT to individualized learning experiences and alleviate certain teaching burdens, thereby fostering improved learning outcomes for DHH students.
A TPB-TAM Approach to Identifying Adoption Factors of Hyflex Among Educators
Authors: VIZCONDE Elanie , ISAGUIRRE Joshua , LIWANAG Gabriel Luis , EBARDO Ryan
Published: January 2025

Hybrid flexible learning facilitates simultaneous instruction for in-person and remote students through technology. Students benefit from enhanced flexibility in class delivery beyond the limits of the physical classroom. However, post-pandemic, the adoption of Hyflex learning decreased due to cost, complexity, and training requirements. This research integrates the Theory of Planned Behavior and the Technology Acceptance Model, alongside monetary incentives, to examine factors influencing Hyflex adoption among 161 Philippine teachers. Our analysis indicates that social influence significantly impacts the intention to utilize Hyflex flexible learning. In contrast, attitude, usability, and perceived usefulness do not significantly affect adoption intention. Additionally, the construct of monetary incentives was found to have no impact on the acceptance of the Hyflex model within the context of this study.
Actions and Interactions at Collaborative Engineering Design Hackathon: Looking Through the Lens of Embodied Cognition
Authors: NARAYANAN Soumya , KAUR Navneet , MAJUMDAR Rwitajit
Published: January 2025

Abstract: Engineering design is an important aspect of engineering education. The essence of engineering design process has been conveyed to students in diverse ways such as formal capstone and cornerstone projects or informal processes such as hackathons. The interdisciplinary nature of engineering design projects often prove challenging to students in multiple ways. As informal opportunities, hackathons have the potential to acquaint students with several skills key to interdisciplinary engineering design. This paper investigates the contribution of one such hackathon — a medical device innovation hackathon, in supporting student understanding and learning of engineering design process. Specifically, the examines the influence embodied cognition plays on supporting student understanding of an engineering design problem that cuts across multiple disciplines. In the case study, we describe an episode where a team of students go through the gradual process of comprehending the design problem along with the accompanying design complexities through descriptive narration, and simulation.
Designing an AI-Enhanced Timeline for Monitoring Multimodal Interactions in Embodied Learning Environments
Authors: FONTELES Joyce , SRIVASTAVA Namrata , DAVALOS Eduardo , S Ashwin T , BISWAS Gautam
Published: January 2025

Embodied learning represents a natural and immersive approach to education, where the physical engagement of learners plays a critical role in how they perceive and internalize concepts. This allows students to actively embody and explore knowledge through interaction with their environment, significantly enhancing retention and understanding of complex subjects. However, researchers face significant challenges in exploring children's learning in these physically interactive spaces, particularly due to the complexity of tracking multiple students' movements and dynamic interactions in real-time. To address these challenges, this paper introduces a Double Diamond design thinking process for developing an AI-enhanced timeline aimed at assisting researchers in visualizing and analyzing interactions within embodied learning environments. We outline key considerations, challenges, and lessons learned in this user-centered design process. Our goal is to create a timeline that employs state-of-the-art AI techniques to help researchers interpret complex datasets, such as children's movements, gaze directions, and affective states during learning activities, thereby simplifying their tasks and augmenting the process of interaction analysis.
Development and Evaluation of a Hybrid Mobile-Learning App Using Design Science Research (DSR) Framework
Authors: VILLAMIN John Lorence , VILLANUEVA Joselito Christian Paulus , TUMOLVA Joshua
Published: January 2025

This paper details the development and evaluation of a hybrid mobile-learning application designed through design science research (DSR) to address educational software challenges in accessibility, flexibility, and cost-effectiveness, especially during the COVID-19 pandemic in one of the schools in the Philippines. Following the DSR framework, the study encompasses four phases: problem explication, design and development, demonstration and implementation, and learning experience assessment. Through literature reviews and stakeholder surveys, the mobile-learning app aims to enhance distance learning, focusing on science education in Philippine schools. An agile-kanban facilitated app development. Comprehensive evaluations by experts, users, developers, and researchers highlight the app's effectiveness in diverse educational settings. Implemented in a Grade-7 class at Occidental Mindoro State College (OMSC) resulted to highly satisfactory feedback. While the app's accessibility, appeal, cost-effectiveness, and pedagogical efficacy are noted, challenges such as optimization and technical complexities are still evident. These findings suggest that the developed app and similar hybrid mobile-learning apps can provide scalable, cost- effective solutions for improving education in remote and disadvantaged areas, narrowing the digital gap.
Impact of Online Video Dubbing Activities on Grade 5 Students’ Pronunciation, Accuracy, and Fluency in English Speaking: An Experimental Research
Authors: XIE Min , TSE Alex Wing Cheung
Published: January 2025

The increased adoption of dubbing software, which gives students a chance to imitate native speakers' pronunciation of English, has led to a surge in research on its application in foreign language learning. Some reported effects on students' oral English, particularly in terms of intelligibility and comprehensibility, can be identified. However, the related studies on students' spoken English, in terms of pronunciation, accuracy, and fluency stressed in the Nativeness Principle, are still in their nascent stages. To address this issue, this study adopted an experimental approach to figure out whether dubbing activities affect grade 5 students' English speaking in these three factors. Two classes served as the experimental group (n=104) to learn English speaking with online video dubbing activities for 12 sessions while the other two classes (n- 102) participated as the control group to learn the same content traditionally without the dubbing activities. For pre-tests and post-tests, a 15-item, 3-factor English Speaking Proficiency Questionnaire was adopted (i.e. pronunciation, accuracy, and fluency). The results of ANCOVA showed a significant difference in the overall English- speaking proficiency of students between the two groups (F = 8.291, p = 0.004;
Enhancing Chinese Language Education Through AI-Assisted Project-Based Learning: A Qualitative Study on Learning Values and Multimedia Skills Development
Authors: SUGIE Satoko
Published: January 2025

The purpose of this study is to explore the value of Al-assisted project-based learning design in Chinese language education. Through instructional design that integrates foreign language, cross-cultural understanding, and multimedia expression, we aim to enhance learners' proactive attitudes and improve learning outcomes using Al and ICT. Conducted the tourism Chinese course at a Japanese university, the research employed mixed methods to assess the effectiveness of integrated PBL. The course utilized Al and ICT tools such as an online whiteboard, a pronunciation practice tool, generative Al for writing assistance, and a video editing application. Data from student surveys, feedback, and teacher observations indicated that approximately half of the students found the Al-supported PBL effective for their learning needs, especially in pronunciation practice and video editing, though it didn't fully address speaking skill inaccuracies. Al and ICT tools were effective in supporting cultural knowledge and intercultural communication skills, but student evaluation of Al's impact on learning were polarized. The value of the CFL approach using Al and ICT was discovered in creating immersive, adaptive learning environments—a value that had gone unnoticed in traditional teaching and learning methods—while also underscoring the need for proper support and training from teachers. Future challenges include integrating Al literacy into language courses and addressing varying technological proficiency levels among students.
Investigation on the Usage Status of a Support System for Writing English Paragraph Outlines in English Classes
Authors: ILHAM Afifah , KUROKI Tomohiro , NAKANO Akira , KUNICHIKA Hidenobu
Published: January 2025

In order to write a persuasive English paragraph, it requires appropriate logical development and related knowledge. But it is difficult especially for English beginners who do not have sufficient knowledge. Therefore, a support prototype system was developed to help learners in writing paragraph outlines. This research aims to clarify issues in use of the support system through user usage and instructor evaluation as part of the evaluation of the system in the classroom environment. Using the cluster diagram diagnostic function and consistency diagnostic function in the system, we were able to diagnose both cluster diagrams and outlines which were drawn and written by students. We were also able to examine the problems or errors that occurred. Based on the results, further investigation, analysis, and system improvements are required. Especially system improvements for the beginners, it is necessary to enrich the explanations of the meanings and roles of labels when drawing the labeled cluster diagrams.
Minecraft as a Tool for Digital Game-Based Learning: Enhancing Conceptual Understanding and Attitudes in Mathematics Learning
Authors: CHALEEPLIAM Sakda , PREMTHAISONG Sasivimol , CHAIPIDECH Pawat
Published: January 2025

One of the teaching approaches most used to enhance mathematics learning by educators is digital game-based learning. With advancements in technology, many games have been integrated into educational methods. This research employed a quasi-experimental design investigated students' conceptual understanding and their attitude toward mathematics learning using Minecraft. In this study, the forty-five of third grade students from primary school in the northeastem region of Thailand were divided into two groups. The findings of the study indicate that the control group scored higher than the experimental group on the pre-test. However, after the intervention, the experimental group, which utilized Minecraft as an educational tool, demonstrated significant progress, showed significant improvement, effectively decreasing the performance gap between the two groups. Furthermore, the results demonstrate that Minecraft effectively fosters positive attitudes and enhances academic achievement in mathematics. Regarding the limitation, the relatively small sample size in both groups may limit the generalizability of the findings, indicating a need for future studies with larger samples.
The Effect of LINE Chatbot with Escape Game Design on English Learning Achievement, Situational Interest, and Student Engagement
Authors: LO Elva Yi-Fang , SUN Jerry Chih-Yuan
Published: January 2025

The aim of the present research was to enhance students' English learning achievement, situational interest, and engagement through the use of a LINE chatbot with an escape room design. The participants in the study were 58 grade six students, with 29 students in the experimental group who engaged in the escape room design, and another 29 students in the control group who completed self-regulation tasks. The results indicated that students who participated in the LINE chatbot with escape room design were able to enhance their learning achievement, situational interests, and engagement with English learning, compared to students in the control group. However, the use of the LINE chatbot with escape room design did indeed have significant effects on students' learning achievement, situational interest, and engagement in three domains, due to its novel learning experience. For future research on chatbot design in English learning, it is suggested that the instruction of chatbot design should involve students at different levels and incorporate multiple learning content. Moreover, to gain a deeper understanding of the true effects of chatbot design on learning, it is suggested that future research should address more types of experimental groups.
Exploring Skills Enhancement in Student Teacher Through Implementation of Design Thinking in Unplugged Game Creation
Authors: LING Tian Wong , KHAMBARI Mas Nida Md. , MUHAMAD Mohd Mokhtar , ABDULLAH Sharifah  Intan Sharina Syed , RAMLI Saiful Hasley
Published: January 2025

Design thinking provides a comprehensive framework that encourages collaboration, empathy and which aligns well with the goals of education. While design thinking is recognised as an effective approach in education, previous research has focused primarily on its application in digital materials or project design tasks. This study addresses this gap by exploring the skills enhanced by student teachers through the implementation of design thinking in the creation of unplugged educational games. Using a qualitative case study design, a total of six groups with total 22 student teachers were selected through the purposive sampling technique to participate in a series of workshops focused on unplugged educational game design. Data was collected through group discussions, document analysis of design artefacts, weekly reflective e-portfolios and participant observation during the game creation process. The findings revealed significant improvements in the student teachers' problem-solving, creativity and communication skills as they engaged in design thinking. Through collaborative problem solving, iterative prototyping and creative ideation, the student teachers have improved their ability to communicate ideas clearly, and were able to tackle complex challenges and generate innovative solutions. These findings highlight the transformative potential of design thinking in teacher education and demonstrate its effectiveness in preparing future educators for the complexities of 21st century teaching and learning. Future research may examine design thinking's impact on teachers of different experience levels, use experimental designs for validation, and compare it with other instructional to identify effective strategies.
Enhancing Language Learning Through Multimodal AI-Driven Feedback on Picture Descriptions: An Eye-Tracking Study
Authors: ZHAO Ruibin , XIE Zhiwei , ZHUANG Yipeng , LI Huixian , YU Philip L. H.
Published: January 2025

To enhance English language learning through descriptive tasks based on everyday life scenes, we propose leveraging multimodal Al technologies. This involves utilizing multimodal large language models to automatically assess the quality of student responses and provide personalized, timely feedback in the form of Al-driven comments and suggestions for improvement. Furthermore, we will investigate students' perceptions of the feedback provided and its effectiveness in enhancing their writing skills. To achieve this, 30 participants were recruited to describe a set of daily-life pictures with the Al-driven automated assistance. During the experiment, eye-tracking technology was employed to capture students' eye movement data, enabling analysis of their visual perception and attention allocation. A survey questionnaire was administered to gather students' perceptions of this language learning approach and the effectiveness of the feedback. The experimental results indicate that students exhibit a positive attitude towards this language learning approach, as evidenced by their high levels of learning interest and motivation. Moreover, students demonstrate a willingness to actively engage with automatic feedback, with a particular inclination towards investing more attention and time in the suggestions generated by a large language model.
Question Generation Support System Using Others' Research Frames
Authors: MAEDA Daiki , KUNORI Kota , KOJIRI Tomoko
Published: January 2025

Answering questions about our own research helps to clarify our arguments and advance our investigation. To generate questions about one's own research, we must view the research from different perspectives. In this study, we propose a method to view research from an outside perspective. The perspectives of others are reflected in the papers they have written; therefore, we constructed a system that supports researchers in extracting the research elements of other researchers' papers that are not considered in their own research and in capturing those elements in their own research.
Who Is a Good Computational Thinker? Mapping Behavioral Dispositions of Middle-School Children Based on Real-Life, Algorithmic Tasks
Authors: PINNAMARAJU Shashaank V. , TONY Lazar , SRIVASTAVA Anveshna
Published: January 2025

Computational Thinking (CT) is a critical thinking ability we want children to develop for efficient and effective problem-solving. Aligned with the recent unplugged, non-digital approach to developing CT, we show, through this ongoing study, in a low- resource, no-computer context, how behavioral dispositions of children map onto the established facets of being a 'good' computational thinker. Twenty middle-school children (Mean age 14.6 years, 12) volunteered to participate in an experimental study, where they were randomly grouped into one control (n-9) and two experimental groups (n=5, 6). The experimental groups were administered a 'sorting' task and a 'sorting + simulation' task, respectively, and all the groups were administered a standardized CT test (CTt) as the pre and post-test. We use a mixed-method research design to analyze our data. Our quantitative methods revealed no significant difference between the pre and post-CTt scores but better performance on 'arrow' questions than on 'visual-block' questions. Our qualitative methods provide insights into students' problem-solving and learning processes. They reveal their ability to 'persist', 'abstract', 'verify', and render a 'generic' solution mapped onto the established CT skills of iteration, abstraction, debugging, and algorithm generating.
Constructing Desirable Learning Habits: Evidence from an Instructional System Design Course Based on the IDC Theory
Authors: SRIVASTAVA Anveshna , YADAV Sandeep , MURTHY Sahana , IYER Sridhar
Published: January 2025

Habits are formed when people perform repeated actions in a certain context while pursuing a goal. In the context of a semester-long Instructional System Design course, we show how the goal of designing a real-life website for the sensitization of teachers and peers toward disabled students led to the construction of certain desirable learning habits. The participants in the course were the default IS designers and the Persons with Disability (PwD) cell of the institute was the client. Anchored in the Interest-driven creator (IDC) theory, this study provides evidence for the three conceptual components of the habit loop- cuing environment, routine, and harmony. We argue that the real-life goal provided a fertile ground for routinizing the behavioral and conceptual activities of the participants. We use a combination of deductive and inductive coding to analyze the data sources of interviews, class interaction observations, shared documents for class reflection, and class interaction records in a virtual space. Our findings suggest that the physical and virtual spaces for regular class interaction, learners' routine interaction with the group members, and the constant sharing-cum-discussion of ideas scaffolded desirable learning habits as they pursued the common goal of website design. This study has implications for fostering desirable learning habits in regular classroom interactions.
Exploring the Impact of Integrating Auto-Photography and Imagery Strategies into Computer-Supported Collaborative Learning: A Case Study in a General Education Course on Climate Change
Authors: HUANG Wen-Lung , CHANG Chia-Jung
Published: January 2025

This study investigates the impact of computer-supported knowledge building, auto-photography, and imagery on university students' cognition and engagement with climate change concepts in a general education course. The findings reveal that integrating Knowledge Forum (KF) with auto-photography and imagery strategies significantly enhances students' engagement and higher-order cognitive activities, such as analysis and creation, compared to using KF alone. However, no significant improvement was observed in the evaluation dimension, indicating the need for further qualitative research to explore the underlying reasons.
Investigating the Role of AI Book Talk Companion in Enhancing Student Performance: A Pilot Study on Self-Efficacy
Authors: TSAI Yi-Cheng , YANG Hsiao-Tung , LIAO Chang-Yen , YEH Yen-Cheng , CHAN Tak-Wai
Published: January 2025

This pilot study explores the potential of AI book talk companion to enhance students' self-efficacy, a critical factor in learning engagement, persistence, and academic achievement. The AI book talk companion system was designed to facilitate real-time interaction, providing immediate feedback and encouragement following students' reading activities. The study involved fourth to sixth-grade students, employing an exploratory design to assess the system's initial impact on their self- efficacy. While the findings did not indicate a significant increase in self-efficacy, there was a noticeable improvement in students' performance during book talk activities. This improvement suggests that the depth of interaction with the AI companion increased over time, with the quality of content output gradually approaching that of interactions with teachers. These preliminary results suggest that AI book talk companion hold promise for supporting student self-efficacy, meriting further investigation and application in educational settings.
Supporting Teacher-Student Book Talk and Book Wish Lists with AI-Driven Technology
Authors: KUO Chih-En , TU Hong-Min , LIAO Chang-Yen , CHAN Tak-Wai
Published: January 2025

This study explores the integration of artificial intelligence (AI) assistant in educational settings, focusing on two key teacher-student interaction activities: Teacher-Student Book Talk and Book Wish Lists. Conducted at an elementary school in northern Taiwan, semi-structured interviews to identify the main challenges and difficulties faced by teachers. The literature review highlighted the significant potential of AI as an educational assistant. Using a design-based research approach, several interviews with teachers led to the development and optimization of a digital system designed to address these challenges. The identified challenges included tracking classroom book availability, managing students' reading histories with paper-based wish lists, and recording book talk activities. The implemented system enabled teachers to efficiently track students' reading histories, manage book recommendations, and facilitate AI-assisted book talk. Results showed that the system not only streamlined the management of reading activities, significantly reducing teacher workload, but also enhanced the quality of teacher-student interactions by providing personalized guidance. While some teachers noted an increased workload in managing new content, overall feedback was positive, with a strong willingness to continue using the system. This study addresses previous challenges in managing reading activities and demonstrates that AI-assisted book talk can improve both the efficiency and effectiveness of educational activities. Future research will explore the integration of AI with learning companions to further support reading among parents, teachers, and students.
Teachers’ Perspectives on Integrating AI tools in Classrooms: Insights from the Philippines
Authors: SIBUG Vanessa B. , VITAL Vicky P. , MIRANDA John Paul P. , FERNANDO Emerson Q. , GAMBOA Almer B. , HERNANDEZ Hilene E. , BANSIL Joseph Alexander , PENECILLA Elmer M. , GONZALES Dina D.
Published: January 2025

This study explores the attitudes, reservations, readiness, openness, and general perceptions of Filipino teachers in terms of integrating Al in their classrooms. Results shows that teachers express positive attitude towards integrating Al tools in their classrooms. Despite reporting high level of reservations, teachers believed they are ready and very open in complementing traditional teaching methods with these kinds of technologies. Teachers are very much aware with the potential benefits Al tools can offer to their individual student learning needs. Additionally, teachers in this study reported high level of support from their institutions. Recommendations are offered.
Developing a Multimodal Learning Analytics Approach to Examine Students' Cognitive Presence and Metacognition in a Metaverse Environment
Authors: SONG Yanjie , TAO Lei , DENG Hao , FU Jiachen
Published: January 2025

The rise of the metaverse as an educational platform has introduced new opportunities and challenges in understanding students' cognitive processes. Traditional learning analytics methods often fail to fully capture the dynamic patterns of cognitive presence and metacognitive activities in immersive virtual environments, particularly when students interact with an artificial intelligence (AI)-powered digital human. To address this gap, this study aims to develop a multimodal learning analytics (MMLA) approach to analyse cognitive presence and metacognition in Learningverse - a metaverse platform. To address the limitations of traditional methods, this research integrated eye-tracking data with dialogue text from the AI-powered digital human to provide a more comprehensive understanding of these patterns. A pilot study involving undergraduate students was conducted to collect data, revealing specific patterns of cognitive presence and metacognition. The findings suggest that the MMLA approach could offer deeper insights into students' learning behaviours, providing valuable implications for the design of educational tools and the development of more effective learning strategies in virtual environments.
Competency-Based Assessment in the Era of Generative Artificial Intelligence: Perspectives of Selected STEM Educators
Authors: AGBO Friday Joseph , SMALLEY Heather Kitada , NYMAN Kathryn
Published: January 2025

Generative Artificial Intelligence (GenAI) has come to stay, and educators are exploring its usage in diverse contexts. One pertinent question begging for an answer is how educators integrating GenAI tools can equitably assess students' learning outcomes. This study explores the mixed-method approach, consisting of a rapid literature review and an analysis of experts' perspectives to address this question. We analyze data from the Scopus and Web of Science databases from the rapid review to understand how the use of GenAI is penetrating the STEM field. On the other hand, the thematic analysis of data generated from a ten-week-long group learning circle discussion among STEM professors regarding assessment in the era of the GenAI was used to gain understanding of educators' perspectives regarding how students' learning could be assessed in a classroom where GenAI tools are used. Our findings provide insights regarding how, where, and when to integrate GenAI in STEM classes and potential assessment strategies that could foster trust and transparency between educators and students. This study contributes to the growing body of literature on GenAI in STEM education. It offers implications from the perspective of contextual adoption of assessment strategy in the era of GenAI rather than the traditional approach of one-size-fits-all.
Effects of the Self-Regulated Learning and Motivation on Learning Achievements of the Programming Courses
Authors: CHEN Mu-Sheng , HSU Ting-Chia
Published: January 2025

This study investigated the impact of self-regulated learning (SRL) motivation and strategies on academic performance in a programming course using learning analytics techniques. Data were collected from 250 students, including their coding behaviors, SRL motivations, and strategies. K-means clustering was applied to categorize students into two distinct groups based on SRLmotivation, SRLstrategy, code_copy, code_execution, code_speed, code_paste, and code_length, with the silhouette coefficient confirming the optimal number of clusters. Subsequent analysis revealed significant differences in the academic performance of the two clusters. Structural Equation Modeling (SEM) was employed to examine the mediating role of SRL motivation between SRL strategies and academic performance. The model demonstrated a good fit (AGFI = 0.927, CFI = 0.964, RMSEA = 0.067), confirming the reliability of the proposed relationships. Bootstrap tests further validated the mediating effect of SRL motivation. Our findings indicate that SRL strategies positively influence academic performance both directly and through SRL motivation, establishing a partial mediation effect. This research contributes to the understanding of SRL in online learning environments, highlighting the importance of motivation and strategic learning behaviors for enhancing student outcomes. The implications for designing effective educational interventions and fostering self-regulated learning practices are discussed.