bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effect of Enhancing Students’ Learning Motivation on Bilingual Class with Integrating Augmented Reality in a Multimodality Learning Environment
Authors: LO Yi-Fang , SUN Jerry Chih-Yuan
Published: January 2025

The purpose of this study was to explore the effect of enhancing students’ learning motivation on bilingual integrative activities classes with augmented reality (AR) creative video. One hundred and six 5th grade elementary students filled in the motivation questionnaire with Likert 5-point scale after the bilingual classes were conducted, those assigned to the non- augmented reality (AR) group, and an individual AR group. For the non-AR group, students discussed the outdoor activities orally with the traditional teaching method, and the individual AR group used group work with the “AR Makr” APP to make outdoor activities videos which were created on iPad. The results showed that integrating AR enhances students’ learning motivation and self-efficacy in bilingual classes in a multimodality learning environment. It is suggested that teachers can incorporate multimodalities instruction in bilingual and language teaching in order to enhance students’ learning motivation on language.
The Course Learning Experience as a Forerunner in IT Internship: Assessment from the Student and the Host Training Establishment
Authors: CELESTIAL-VALDERAMA Arlene Mae
Published: January 2025

On commencing internship, undergraduates are required to perform core and professional skills under the supervision of external host training establishments. The variability of knowledge and skills experience can provide and identify placement in the Information Technology industry following graduation. This study presents intern assessments on how well the internship program supports development and demonstration of essential listed attributes. It explores perspectives on how well their course learning experience acts as a forerunner to develop skills/competencies. It seeks to validate how course categories from degree programs prepared them for training and which of the categories have been utilized and applied. This article includes the assessment of the interns from their immediate supervisors. The internship enabled the interns to develop or demonstrate essential attributes where 52% implied that they gained skills to apply in their future career, 35% appreciated the importance of continuing personal and professional development, and 10% inferred that the internship program was a substantial contribution in preparing them for professional career upon graduation. On the learning experience, more than 75% of the responses reflected seven listed skills and competencies. More so, 85% of the students have chosen the Internship course has prepared them substantially. Moreover, the course was highly utilized among other course categories as responded by 83% of the interns. The host training establishment response to the intern’s performance has a high of 77%-92% in the range of 1-1.87 under the Excellent top box score. These results all contribute to the overwhelmingly positive perception of student intern performance.
Examining Different Affective Factors in Learning with Virtual Reality
Authors: TU Hsing-Ying , LEE Silvia Wen-Yu , HSU Ting-Yueh
Published: January 2025

This study aims to examine how prior knowledge and affective factors of virtual reality environments predict science learning achievement through the mediation of learning engagement. Ninety-two sixth-grade students in Taiwan were recruited in this study. Data were analyzed through partial least squares structural equation modeling (PLS-SEM). The results showed that prior knowledge negatively predicted presence and control and active learning. Presence, control and active learning positively predicted learning engagement (behavioral engagement, cognitive engagement, emotional engagement). Cognitive fatigue was found to negatively predict emotional engagement and science learning achievement. Implications and suggestions for future research were addressed in the study.
Designing a Sorting System using Machine Vision Training Kit for Mechatronics and Robotics Engineering Students
Authors: MUANGSUK Pakorn , HOWIMANPORN Suppachai , CHOOKAEW Sasithorn
Published: January 2025

Machine vision is even more critical for sorting workpieces in quality control of automated production systems for Industry 4.0. Use the image processing system principle to inspect the workpiece with a high-speed vision sensor camera. It results in high accuracy of sorting performance and can replace labor in automated production processes. The learning process of vision sensors used for inspecting workpieces with industrial cameras for mechatronics and robotics engineering students is concerned. Most barriers need more teaching materials due to the relatively high cost of the training kit. That is causing students' lack of knowledge and practical skills during the professional training experience in the workplace. This study proposed designing a sorting system using a machine vision (SSMV) training kit for mechatronics and robotics engineering students. It can be a learning tool to promote the students' learning in related fields, automated manufacturing systems, and engineering education.
Design of a Pneumatics System Learning Material with AR Technology for Vocational Education Students
Authors: PHETCHAKAN Tanit , HOWIMANPORN Suppachai , CHOOKAEW Sasithorn
Published: January 2025

Nowadays, education in industrial engineering places a significant emphasis on pneumatic systems due to their widespread application in conveyor belt systems and various automated industrial processes. Components within pneumatic systems include compressors, air preparation units, directional control valves, actuators, and gripping devices. Therefore, understanding pneumatic systems necessitates having a solid understanding of how to employ these components efficiently. In educational management, the development of learning materials holds paramount importance. These materials play a pivotal role in facilitating enhanced comprehension among students. Integrating augmented reality technology further enriches the learning experience, specifically in understanding pneumatic systems. This technology provides students with a comprehensive system overview before engaging with actual equipment in practical tasks. This study proposed the design of a pneumatics system learning material with AR technology for vocational education students. It encourages the student’s motivation in the concept of pneumatic systems. We created a model using 3D and AR application programs to generate learning material to display on mobile devices. The circuits of the function of the cylinder work in various conditions. It has been used to simulate the use of training skills for students or interested people to apply such knowledge to work in the workplace in the future.
Designing an IoT-based Biorobotic Complex Board Game
Authors: CHEN Song-Lin , SHIH Ju-Ling , CHEN Shaun-Wen
Published: January 2025

 integrates Internet of Things (IoT) technology to create a captivating and intricate complex board game experience for learners. Set against the backdrop of ancient China's Silk Road, the game offers players a diverse range of tasks while delving into the historical knowledge of the Silk Road. The role of IoT technology is crucial, facilitating the transmission of a wide array of data between objects and servers, enabling the study of learners' historical thinking performance during their engagement with the game. This game also places significant emphasis on interaction and entertainment, skillfully blending elements from large-scale complex board gaming to enhance the learning process.  seamlessly merges education, technology, and entertainment, opening a gateway for learners to explore both ancient history and modern technology in an engaging manner.
Resistance Training Support System with Pose Estimation
Authors: YAMADA Koki , GOTODA Naka , AKAGI Ryota
Published: January 2025

Squat training is highly effective for improving lower limb muscle function. However, when this type of training is performed alone without a personal trainer or physical therapist, it can lead to inefficiencies and injuries among trainees. Thus, we aim to develop a system that provides visual and real-time feedback on the correct posture of a lone squat trainee using pose estimation. In this study, we propose a function that demonstrates the correct posture for each squatting discipline in the form of a line-segment posture representation and warns the trainee when he/she assumes an incorrect posture. Squatting motion in the sagittal plane was captured using a camera connected to a personal computer, and the coordinates of the acromion, hip, knee, and ankle joints were detected using MoveNet. The joint angles were calculated from the detected coordinates and the correct posture was determined according to the individual's body shape. The color of the formed line-segment posture representation changed to indicate the difference between the trainees’ actual and correct postures. In future research, we plan to assess the displacement of key points detected by MoveNet using optical technologies to confirm the reliability of this system’s pose estimation. Afterwards, we will evaluate the effectiveness of this system in determining whether a trainee can move appropriately.
App-Infused Preschoolers’ Storyline Concept-Driven Numerical Curriculum Design
Authors: YEN Ruei-Cheng , CHANG Ben
Published: January 2025

For preschoolers, Apps can facilitate them to learn mathematical concepts, such as number recognition, counting, comparison, mixture problem, combination, etc. However, most of the mathematic Apps currently are drill-and-practice design which provides numerous questions and answers matching exercises for preschoolers, that leads them paying attention on doing exercises instead of concepts acquisition. We will argue that for the preschoolers, mathematical concept acquisition is more important than drill-and-practice exercises. Therefore, to response, for three to five-year-old preschoolers, we developed a mathematical concept learning App named Kid+ which aims to build up their conceptual understanding and cognitive ability. By integrating storyline design with situated learning, the preschoolers can use the App in a concept- embedded role-play story. As three preliminary preschool experts’ using feedbacks, the result revealed that the App might help the preschoolers navigating through the storyline to learn the mathematical concepts embedded. However, further evaluation of preschoolers is needed.
Investigating Players' Social Interactions in IOT Board Games
Authors: Wang Yueh-Chi , Shih Ju-Ling , Chen Shaun-Wen
Published: January 2025

This study aims to investigate the impact of integrating Internet of Things (IoT) technology with board games to enhance social interaction among players. Given the widespread application of IoT in various domains, incorporating it into board games can offer more intricate gameplay mechanisms and a broader array of interactions. Subsequent to gameplay, interviews will be conducted to gather players' reflections on the gaming process and their motivations for engaging with different players. This study anticipates that the interdisciplinary board game, combined with IoT technology integration, will further augment the diversity and richness of social interaction among players.
Method to Promote Social Facilitation of Learners by Presenting Writing Sounds
Authors: UEDA Tatsuya , HAMADA Tatsuya , EGI Hironori
Published: January 2025

We propose a method to induce the presence of other learners using writing sounds without interfering with self-guided study. For learners who are more likely to progress when they feel the presence of others, it can be difficult to continue independent self-guided study alone at home. In addition, in certain environments, e.g., libraries and learning commons, where other learners are present, the presence of others may positively influence the continuation of learning. Thus, the proposed system attempts to induce the presence of others according to the learner's level of concentration, which is evaluated by the learner’s leg movements. The proposed system was introduced experimentally to determine whether it can induce the presence of others in an environment with no other learners engaged in self-study activities.
The Impact of Metaverse Worlds on International Collaborative Learning for Cross-Cultural Understanding
Authors: HAYASHI Masako , SUZUKI Takehiro , KAWATA Yuki , GOTO Keisuke
Published: January 2025

We examine factors that influence the students’ memorability of the presentations given in the Metaverse International collaborative learning class, where students work in groups to give presentations to promote cross-cultural understanding. The results show that the students chose the presentation of the group using Metaverse World as the most memorable, with the "Metaverse World" being the reason.
Designing an Online Course on Learning Analytics for Educators: Preliminary Insights from a Scoping Review
Authors: SHUKOR Nurbiha , NOOR Norah , Marina Aini , ABD HALIM Noor , MCLAIN Matthew
Published: January 2025

This paper presents a project to develop graduate digital future work skills in data analytics. We describe a review activity carried out to guide the collaborative development of content for an online Data Analytics Course for Educators in Malaysia and the United Kingdom. Our methodology for systematically searching online learning platforms for current courses is described. We also give an overview of our preliminary findings in relation to the duration of learning, the types of online materials used and the range of topics included. Finally we describe our future plans to review the quality of the courses in relation to the depth, scope and complexity of the learning they promote.
Learning Concentration on Virtual Reality Learning: Scale Development and a Pilot Study
Authors: WU Chun-Ping , Wang Yi Hsuan , PAN Kuan , HUI CHEN Yu
Published: January 2025

The purpose of this study was to develop and validate a multidimensional instrument for elementary and junior high school students’ learning concentration on learning using Virtual Reality Technology. Two studies were conducted. The VR Learning Scale (VRLS) was developed based on in-depth literature review and theories followed by a review of eight experts. Data from 259 students ranging from 4th grade to 7th grade with VR learning experience was collected and analyzed using the exploratory factor analysis, the VRLS was validated in four dimensions: immersion, behavior concentration, cognitive concentration, and emotional concentration. A pilot study of 37 elementary school students was conducted and the results indicated the significant correlation between the four factors of VR learning and, learning motivation, technology acceptance and satisfaction. Implication for future studies is provided.
Composite Score for ChatGPT Prompt Efficiency: A Computational Linguistic Analysis of Engineered Chatbot Prompts
Authors: BANAWAN Michelle
Published: January 2025

The use of chatbots has become increasingly popular in the field of education. Hence, the quality of the prompts used by chatbots can greatly influence the quality of the generated responses which in tum contributes to desirable outcomes related to the overall learning experience. However, even as known criteria for efficient prompts are prevalent, there is a dearth of measurable and concrete linguistic factors that guide prompt engineering. This paper presents a composite score for ChatGPT prompts which can be made applicable to other foundational generative Al chatbots. Through a computational linguistic analysis of known efficient prompts used in learning, emergent linguistic factors point to the relationship of linguistic features and the confidence of ChatGPT responses to well-structured prompts that use the said linguistic features. The linguistic features are the average collostructural strength, collostructural ratio diversity, specificity, and academic language use. These features depict the quality of prompts that pertain to the grammatical structure, specificity, and relevance to the task at hand, and academic language use. Further, these features constitute a composite score for prompts introduced in this study that represent linguistic efficiency and subsequently, correlates to perplexity or certainty estimates of the generated responses of ChatGPT.
Encouraging Primary Students' Environmental Awareness by Using STEM Inquiry-based Learning
Authors: PREMTHAISONG Sasivimol , CHAIPIDECH Pawat
Published: January 2025

The Circular Economy (CE) has emerged as an essential idea in the worldwide response to unsustainable resource consumption and waste management, and educators have incorporated this scenario into the theme in classroom. At the same time, STEM (Science, Technology, Engineering, and Mathematics) education is an essential approach for educating students with the necessary skills for a competitive workforce in the twenty-first century. Several academics are currently attempting to integrate inquiry-based learning, which is one such technique whose efficacy is higher in science-based education. Furthermore, technological integration has been used in scientific courses. This research examined the implementation of combining inquiry- based learning with STEM education using ICT technology in citizen science activities to promote the circular economy for fourth-grade students in a university-based demonstration school located in the northeastern part of Thailand. The results of this study include aspects of students' conceptual understanding about types of plastic and their awareness of the environment. We found that students had a significantly increased conceptual understanding regarding types of plastic. Moreover, the students expressed positive attitude towards plastic waste management awareness. These findings support the notion that ICT technology could assist students in better understanding science concepts and attitudes, especially when integrated with a STEM inquiry-based learning method in the citizen science activity.
Applying Macro- and Micro-scripts to Facilitate Undergraduates' Interdisciplinary Competence
Authors: SU Guo
Published: January 2025

This study aims to develop principles for designing CSCL scripts that can guide interdisciplinary collaboration and knowledge-creation practices to facilitate undergraduates' interdisciplinary competence. Design-based research (DBR) will be conducted in an undergraduate course to investigate if the macro- and micro-scripts have the potential to improve students' performance, and why and how these designs make a difference.
Factors Affecting Sustainable Use of Minecraft-based Lessons
Authors: MANAHAN Dominique Marie Antoinette B. , RODRIGO Maria Mercedes T.
Published: January 2025

This paper examines the factors that affect the sustainable use of Minecraft-based lesson plans. We focus specifically on teachers who received training on using What-If Hypothetical Implementations in Minecraft (WHIMC), a set of Minecraft worlds designed to teach astronomy, geosciences, and ecology. We commissioned a group of teachers to develop lesson plans using at least one of the WHIMC worlds and pilot their implementation in their classes. Another group of teachers received training on developing learning modules with WHIMC but had the freedom to choose when and how to implement their lesson plans during the school year. The teachers cited logistical impediments, scheduling impediments, lack of technical resources, lack of curricular alignment, changes to teaching modalities, limited time for practice, and anxieties about classroom management as barriers to sustainable use of WHIMC. They suggested the training of additional teachers to create a community of practice whose members can collaborate and support each other's use of WHIMC for STEM education.
LLM-Generated Personalized Analogies to Foster AI Literacy in Adult Novices
Authors: CAO Cassie Chen , CHEN Eason , FANG Zoe , CAO Lydia Y , LIN Jionghao , LI Ruizhe
Published: January 2025

Broad Al literacy is essential in today's rapidly advancing technological landscape, extending beyond Al specialists to encompass the general public. However, the complexity of Al concepts poses significant barriers to learning for individuals without prior Al knowledge. While teaching through analogies is a well-recognized method to simplify complex information by connecting it to familiar concepts, adapting these analogies to match individual learner profiles remains a substantial challenge. This paper addresses this gap by proposing a novel method for personalizing educational analogies, enhancing the accessibility and engagement of AI concepts for a diverse audience. Our approach uses Large language models (LLMs) to dynamically tailor content to each learner's cognitive and cultural contexts, grounded in educational theories and practices. Utilizing a crowdsourced AIB testing framework through Prolific (N-60), this research contrasts conventional instructional methods with content incorporating LLM-enhanced personalized analogies. Data collection comprised pre- and post-tests, activity logs, and surveys featuring Likert-scale and open-ended questions. Quantitative analysis of key learning outcomes revealed significant improvements in comprehension and retention, evidenced by enhanced pre-and post- test scores (p < 0.01 and p < 0,05, respectively) and motivation, as indicated by increased engagement in survey responses (p < 0.05). Qualitative analysis revealed a need for more examples and visual aids to complement analogies and a preference for balancing analogies with detailed technical content. This study demonstrates the potential of Al-generated analogies to make complex Al concepts more accessible and engaging. Future research should refine analogy generation. incorporate multimedia elements, and explore long-term and cross-cultural impacts to further enhance Al education.
Facilitating Holistic Evaluations with LLMs: Insights from Scenario-Based Experiments
Authors: ISHIDA Toru , LIU Tongxi , WANG Hailong , CHEUNG William K.
Published: January 2025

Workshop courses designed to foster creativity are gaining popularity. However, even experienced faculty teams find it challenging to realize a holistic evaluation that accommodates diverse perspectives. Adequate deliberation is essential to integrate varied assessments, but faculty often lack the time for such exchanges. Deriving an average score without discussion undermines the purpose of a holistic evaluation. Therefore, this paper explores the use of a Large Language Model (LLM) as a facilitator to integrate diverse faculty assessments. Scenario-based experiments were conducted to determine if the LLM could integrate diverse evaluations and explain the underlying pedagogical theories to faculty. The results were noteworthy, showing that the LLM can effectively facilitate faculty discussions. Additionally, the LLM demonstrated the capability to create evaluation criteria by generalizing a single scenario-based experiment, leveraging its already acquired pedagogical domain knowledge.
Evaluating the Performance of Copula-Based Item Response Theory Models for Interpretable Assessment
Authors: GUZMÁN Eduardo , MILLÁN Eva
Published: January 2025

This paper describes a study evaluating the performance of copula-based Item Response Theory in real-world settings. To achieve this, we used a dataset containing information about 152 students who took a test on first-degree equations. This dataset had previously been employed to assess the performance of a Bayesian Network model in diagnosing 12 concepts related to first-degree equations. Both copula-based Item Response Theory and Bayesian Networks are explainable techniques that can be utilized for educational assessment. In this study, we compare the results of both data-driven methods against the actual state of knowledge of the students, which is a hidden variable, estimated using an expert-driven approach that involved averaging three independent assessments made by experienced primary school teachers. The results show that both methods can be used to obtain reliable estimations of students' knowledge.
Availability and Effectiveness of Generative AI for Web-Based Investigative Learning
Authors: WATANABE Yutaka , KASHIHARA Akihiro
Published: January 2025

In Web-based learning, learners are expected to investigate information through gathering and navigating Web resources, and to construct knowledge. In our previous work, we designed a model of Web-based investigative learning, which represents a learning process as a cycle of phases. We also developed a cognitive tool called iLSB (interactive Learning Scenario Builder) for scaffolding learning process as modeled. At present, on the other hand, learners can explore information by chatting with generative Al, which generates texts in a dialogue format. However, it is unclear about the context where generative Al is available for Web-based investigative learning, and about the effective use of generative Al. In order to examine the availability and effectiveness of generative Al, we have had case studies including the comparison between the use of iLSB and the use of generative Al. The results suggest that generative Al prevents learners from investigating sufficiently compared with iLSB, and the Web-based investigative learning model contributes to the proper use of generative Al for investigative learning. Furthermore, it is suggested that the use of generative Al is effective in exploring background knowledge about questions investigated and in organizing information for knowledge construction.
Developing a LLMs-Driven System Based on Human-AI Progressive Code Generation Framework to Assist Mathematics Learning
Authors: SIT Chun Yan Enoch , YANG Yin , YEUNG Wing Kei , KONG Siu Cheung
Published: January 2025

This paper proposed a system interface based on a novel progressive code generation framework to produce verified programming codes using natural language for mathematics learning. With the latest advancement of large language models such as GPT-4, people can ask GPTs to generate programming code using natural language. However, the codes generated may not be directly executable or aligned with the user's desired goal or purpose. Furthermore, different people may express their goals and purpose in different ways. Hence, there are many possible ways to map natural languages and code. Many of the code evaluation frameworks such as pass-ratio@n, BLEU and CodeBLEU evaluate large language models (LLMs) code generation ability based on their actual execution results on the first prompt without giving feedback of the code execution error. With this in mind, a framework that allows code generated from any large language model to progressively improve was proposed. The idea is to re-prompt the LLMs with the error feedback to generate codes until the code is executable and achieve the user's desire goal or purpose with a predefined number of iterations. If a certain number of iterations has been reached and the code is still not executable or cannot achieve users' intended goals or desire, the prompt would be useful to include in the training dataset for fine-tuning for that large language model. In this study, the application of the proposed framework to enhance the accuracy of mathematical problem-solving problems was tested and reported. This framework may be useful to improve any LLM code generating ability continuously. Discussions were made.
Image-Based Pili (Canarium ovatum, Engl.) Fruit Variety Classifier App: An Approach to Enhancing Teaching Biodiversity and Crop Science
Authors: BELLO Leo Constantine , MARTINEZ Joshua
Published: January 2025

This paper introduces an image-based Pili fruit variety classifier application for teaching biodiversity and crop science courses. For this study, the project uses Pili (Canarium ovatum, Engl.) as the pilot species in developing a non-invasive image-based classifier. The application not only provides insights into biodiversity but also showcases Al's potential in agricultural education.
Quality Criteria Acquisition Support System of Product by Explaining It with Components
Authors: KUNORI Kota , KOJIRI Tomoko
Published: January 2025

In creative activity, creators evaluate products whether they satisfy the criteria. Some criteria such as criteria of novelty are implicit and intuitive, so that it is difficult for novice creators to understand them. Since some criteria can be regarded as the existence of particular components, we can define the implicit criteria as the existence of components that the target product has. We propose a method for understanding implicit criteria by explaining it using existing components of the target product and construct system that supports proposed activity. Based on the evaluation experiment, it is found that proposed system can support enough explanation of criteria.
Enhancing Engagement in Distance Learning: Overcoming Learner Isolation Through ICT Tools
Authors: AOKI Kumiko , KANEKO Itaru , KURIYAMA Ken , TATSUMI Takeo , MIYAJIMA Takahiro
Published: January 2025

The COVID-19 pandemic accelerated the adoption of online distance learning, bringing challenges such as learner isolation and decreased engagement. This paper explores strategies to address these issues through the use of Information and Communication Technology (ICT) tools. It reviews existing studies on learner isolation and proposes the integration of social networking services (SNS) and video annotation tools to create simulated synchronous experiences (SSE) and social interactions (SSI). Additionally, the paper discusses the role of large language model (LLM)-based Al chatbots in providing personalized feedback and enhancing learner motivation. The proposed study will utilize the PlatJam platform, aiming to reduce transactional distance and foster a collaborative online learning environment.
Navigating Europe’s Artificial Intelligence Act: Application of LLMs in classrooms
Authors: DASGUPTA Upasana , MAJUMDAR Rwitajit
Published: January 2025

In 2018, OpenAl introduced the first version of the Generative Pre-trained Transformer (GPT), revolutionizing the future of Large Language Models (LLMs). This model demonstrated the potential of pretraining large-scale models with vast text data and then fine-tuning them for specific tasks to the public. LLMs have quickly penetrated educational environments, aiding students from various disciplines in tasks ranging from initiating research to drafting essays. While the latter may breach academic integrity, the former is highly beneficial, especially for exploring new areas or ideas. Comparatively, the user interactions with GPT might resembles initially with that of search engines, despite technological differences, as both provide answers to queries, often reflecting archived as well as mainstream views. The historical evolution of search engines, from Archie's database matching to Google's relevance-based ranking, highlights similar ethical considerations faced by both technologies. The development of search engines underscored the importance of accessible information, a principle equally relevant to GPT and LLMs today. This paper is written in light of recent coming into force of European Union's legal framework on artificial intelligence for the purpose of examining adoption of LLMs in classrooms, and argues for balanced regulations across jurisdictions that acknowledge both the immense educational potential of LLMs and the need for adherence to legal and ethical standards.
Automated Recommendations for Revising Lecture Slides Using Reading Activity Data
Authors: LOPEZ Z. Erwin D. , TANG Cheng , TANIGUCHI Yuta , OKUBO Fumiya , SHIMADA Atsushi
Published: January 2025

The use of digital textbooks in education provides valuable data on student reading behavior that can help educators refine their course materials and instructional design for future iterations. Previous studies have explored methods for extracting important evidence from this data, but they require manual intervention. By automating these methods, this paper introduces an end-to-end system capable of extracting evidence from e-book data and providing recommendations for slides' content review based on this evidence. Our system incorporates information about reading preferences into the evidence-extraction process and implements Large Language Models (LLMs) for automatic interpretation. Six teachers evaluated our proposed system indicating a promising level of effectiveness, while also highlighting areas for future improvement to ensure a successful classroom implementation. These include considerations for improving the actionability of recommendations, improving the identification of content that needs refinement, and improving the performance of LLMs.
Enhancing Vocational Training Through Immersive Technology: A Study on Digital Magic Mirrors
Authors: WANG Jen-Hang , TSENG Hung-Wei , YANG Su-Hang , CHANG Chih-Kai , ZHUANG Yung-Yu , CHEN Gwo-Dong
Published: January 2025

This study explores the impact of digital magic mirrors on student engagement and skill development in vocational education, specifically in the hospitality sector. Traditional classroom-based instruction often fails to connect theoretical knowledge with practical application, leading to insufficient preparation for industry demands. To address this, the research integrates a digital magic mirror into a digital theater room to enhance learning through immersive experiences. A quasi- experimental design involved 60 hospitality students divided into an experimental group using the digital magic mirror and a control group using a general digital learning approach. Learning achievement, engagement, and self-efficacy were measured through pre- and post-tests and a questionnaire. Results showed that the experimental group had significantly higher learning achievement compared to the control group. The digital magic mirror also improved student engagement and skill development. However, no significant differences were found in perceived learning motivation and self-efficacy, with both groups reporting positive experiences. These findings suggest that digital magic mirrors effectively bridge the theory-practice gap in vocational education by providing dynamic learning environments. The study concludes that the proposed digital magic mirror approach can significantly enhance student outcomes and recommends further research on their long-term impacts and broader applicability.
Code Tracing Support Environment Based on Visualization of Cooperative Behavior of Multiple-Flows
Authors: NOGUCHI Yasuhiro , SUNAMA Kotaro , KOGURE Satoru , YAMAMOTO Raiya , YAMASHITA Koichi , KONISHI Tatsuhiro
Published: January 2025

Several studies have been conducted to improve program comprehension through code tracing. However, most of these studies have focused on a single flow of control, with insufficient proposals to support program comprehension of multiple flows of control. It is difficult for novices to trace code with multiple flows in the correct order. In this study, we propose a code trace support environment for letting learners to trace the appropriate flow in execution with synchronization constraints. It also supports the component-based comprehension of synchronization mechanisms in multiple flows. The evaluation experiment confirmed the learning effect of understanding the appropriate order of steps. However, several issues regarding the component-based comprehension have been identified.
Exploring the Relationship of Personality Domains and Visual Attention Patterns in Novice Programmers
Authors: PACOL Caren , RODRIGO Maria Mercedes , TABLATIN Christine Lourrine
Published: January 2025

Researchers have long studied how novice programmers interpret compiler error messages during debugging. This study investigates the link between personalities and visual attention patterns. We measured traits using the Big Five Personality Test (John and Soto, 2017) and eye-tracking data from 63 participants at two Philippine universities. They located bugs in five programs with syntax errors, each 15 to 35 lines long, using constructs from the first 6 to 8 weeks of a programming course. Each program had one syntax error, either literal or non-literal. Participants viewed images of the programs and error messages, marking errors with a custom viewer. We found that students with high agreeableness and conscientiousness showed minimal attention to compiler error messages and error lines, respectively. The lack of correlation between these traits and performance scores makes it uncertain if these students with high agreeableness and high conscientiousness are also high performers. This study suggests educators might develop strategies potentially leveraging agreeableness and conscientiousness, such as collaborative learning. Future work will investigate run time errors, analyze patterns, and create strategies to help novices address them effectively.
Effect of Re-Composition Concept Mapping for Sharing Reference Maps on Serial Concept Mapping: A Preliminary Study
Authors: FITRIANSYAH Rian , SANTOSO Harry Budi , SADITA Lia , CENKA Baginda Anggun Nan , NURHAYATI Syifa , HAYASHI Yusuke , HIRASHIMA Tsukasa
Published: January 2025

In serial concept mapping, a concept map created during one lecture is extended in subsequent lectures, resulting in a comprehensive map over time. The quality of the initial maps directly influences the quality of future maps, making it crucial to enhance the concept maps created in each lecture. Teachers often develop reference concept maps beforehand to evaluate learners' maps and provide feedback based on these reference standards. Incorporating a reference map sharing phase after individual concept mapping is anticipated to improve the quality of learners' maps in each lecture and positively influence subsequent ones. However, the scratch- building method, where learners freely create all components of the map, poses challenges for effective reference map sharing due to discrepancies between the reference and learners' maps. To address this, we explored the re-composition method, where learners construct concept maps by reorganizing pre-provided components, allowing them to concentrate on structural aspects rather than creating the components themselves. This paper compares the effects of reference map sharing between individual concept mapping using the re-composition method and the scratch- building method. In the re-composition method, components are provided by decomposing the reference map, enabling learners to focus on structural differences during the reference map sharing phase. The study was conducted in university lectures, involving three map-building sessions and two reference map sharing sessions across three lectures for both groups. Results showed that the quality of each map (submap) improved incrementally for the re-composition group but not for the scratch-building group. This suggests that reference map sharing in serial concept mapping is more effective when using the re-composition method.
Forest/CR: Critical Paper Reading Support System
Authors: KII Tomoya , SETA Kazuhisa , HAYASHI Yuki
Published: January 2025

This study presents the development and evaluation of a critical paper reading support system named "Forest/CR." The system aims to enhance learners' critical reading skills by facilitating the formation of reading strategies. It achieves this by recognizing questions that learners were unable to generate independently and by allowing them to compare their interpretations with those of others. The system features functions such as paper content organization, comparison of reading content, and comprehensive evaluation support. An experimental study involving third-year undergraduate students demonstrated that the system effectively broadened students' reading activities, promoted the generation of critical questions, and supported the learning of new reading strategies.
Facilitating Thinking like a Historian in Open-Ended Learning Space: A White Box Approach
Authors: MATSUURA Aoi , HAYASHI Yuki , SETA Kazuhisa
Published: January 2025

In this study, we adopt the Thinking Like a Historian Approach (TLH), which systematizes the historical inquiry process of historians as a learning model. By expanding Linked Open Data (LOD) using a large language model (LLM) based on an ontology that defines TLH concepts, we construct a white-box-oriented learning resource that ensures comprehensiveness, reproducibility, learning controllability, and maintainability. The historical learning support system developed based on this learning resource has adaptive question generation function and historical information visualization function in an open-ended historical inquiry space.
Error Tolerance in Automatic Short Answer Grading with Large Language Models: The Case of Handwriting Recognition Errors
Authors: TAN Ziqi , ZHANG Yingbin , MU Su
Published: January 2025

Assessing students' answers is a labor-intensive task that could significantly burden educators. Technological advances have led to the development of automatic scoring systems. However, handwriting recognition errors significantly impacts the deployment of automatic scoring in the context of paper-pencil assessment, which is still the most widely used format of academic exams. Large language models (LLMs) have proven to be effective in scoring tasks and are promising for automatic short answer grading. Nevertheless, LLMs' capability to grade the short answers with handwriting recognition errors is underexplored. The current study addressed this issue in the context of paper-pencil Chinese tests in elementary schools. The LLM used was ERNIE 4.0 because of its outstanding capability in Chinese language comprehension. We compared the grading accuracy of the model on the raw data extracted from handwritten answers by optical character recognition with that on the preprocessed data where the recognition errors were corrected. We found a substantial accuracy difference between raw data and preprocessed data, indicating that LLMs have not yet achieved the precision for grading short answers with handwriting recognition errors. Nevertheless, LLMs showed an interesting characteristic during grading: awarding points to incorrect answers as an acknowledgment of student's effort.
Towards the Development of PIA 2.0: A Pedagogical Agent that Exhibits Synthetic Facial Expressions
Authors: DELA CRUZ John Lorenz , DELA CRUZ Paulyn Joy , GUADALUPE Joyce Antonette , MACARAEG Jiabiance , MONTESA Piolo Jose , RAMOS Mark Paul , BRINGULA Rex
Published: January 2025

This study reported the design and development of the initial prototype of a web-based virtual learning environment with a pedagogical agent. The pedagogical agent, named PIA 2.0 (subsequently referred to as PIA), exhibited synthetic facial expressions and body gestures. It could also provide textual feedback, hints, and encouragement messages. Grade 8, 9, and 10 students (n = 30) utilized the initial prototype of PIA. The interactions within the software were collected and analyzed through descriptive statistics and lag sequential analysis. It was revealed that students were engaged in the intervention and had a high probability of solving mathematics problems they were comfortable with, with a very small probability of changing the level of difficulty. These results were utilized to add a feature to PIA by recommending mathematics problems to be solved. The commercialization aspect of the software was also discussed. Conclusions and future research directions were provided.
Development of Laborer Digital Twin Generation and Visualization Function for Hazard Prediction in Off-Site Training
Authors: MINOHARA Kaito , MUGURMA Toshiki , KOMETANI Yusuke , GOTODA Naka , LEE Saerom , KANDA Ryo , IRIE Shotaro , HARAI Toru
Published: January 2025

We developed an MR-based system to enhance safety education for hazard prediction in off-site training by generating and visualizing digital twins of laborers. This system focuses on digitizing full-body movements, particularly foot movements and postures, to identify unsafe behaviors during training. Through trials with Vietnamese technical intern trainees, the system effectively identified these issues, highlighting its potential to improve safety training by providing detailed feedback on unsafe behaviors.
HyCode: A Code Similarity Assessment Tool Utilizing Recurrent Neural Networks
Authors: ABAWAG James Marcel A. , TORDILLA Aleczia S. , MARTINEZ Joshua C.
Published: January 2025

Academic dishonesty, particularly source-code plagiarism, poses significant ethical challenges in educational institutions and online coding platforms. It undermines the integrity of the learning and teaching process as well as the credibility of students and institutions. To address these challenges, this study developed a code similarity assessment tool utilizing deep neural networks, specifically character-level recurrent neural networks (char-RNN) and long short-term memory networks (LSTM), to detect source-code plagiarism. It leverages the strengths of both models while minimizing their weaknesses, allowing it to learn and capture both low-level and short-term patterns as well as complex and long-term dependencies in the source code. The dataset used was mainly from the "IR Plag Dataset", and data augmentation and various preprocessing techniques were performed. The final model configuration of the hybrid neural network architecture resulted in training and validation accuracy of 99% and 90% , respectively. Its evaluation was conducted using various metrics such as precision, recall, and Fl -score. The hybrid neural network architecture achieved a precision of 0.94, a recall of 0.935, an Fl -score of 0.94, and a final accuracy of 93.75% In addition, the tool was also evaluated on real-world data and discovered to be capable of identifying a range of code similarities, providing assurance that the tool can effectively differentiate authentic or original work from work that may have been plagiarized. However, the evaluation also revealed the presence of false positives and negatives, which leaves room for improvement.
Low vs. High Immersion in Metaverse-Based Learning: How Pre-Service Teachers Balanced Between Instruction and Assessment in Learning Design
Authors: DARMAWANSAH Darmawansah , PUSPITASARI Dani , HWANG Gwo-Jen
Published: January 2025

Emerging technology like the Metaverse has shown significant potential in expanding learning opportunities. This includes immersive environments, virtual student-teacher interactions, and diverse locations for learning and teaching. In particular, the Metaverse can enhance different modes of instruction and assessment, a crucial component of effective learning processes. This study explores how pre- service teachers design immersive learning environments through Metaverse-based learning. Eighteen pre-service teachers at a private university in Indonesia participated in a required course and were tasked with Metaverse-based learning projects. They worked in pairs and were divided into two groups. Four pairs designed learning in a low immersion Metaverse environment, while the other four pairs worked on a high immersion Metaverse environment. Their instructional and assessment strategies were graded using rubrics, and their project presentations were recorded for qualitative analysis. The findings revealed that engagement and interactivity, which facilitate understanding and application of learning objectives, were more aligned in the high immersion environment. Additionally, the quality of feedback, a key part of assessment, was rated higher in high immersion environments. From a qualitative perspective about the challenges in designing Metaverse-based learning, while high immersion environments offer advanced features that can enhance learning, they also present complexities that challenge the pre-service teachers' ability to manage and utilize these features effectively. Conversely, low immersion environments, although seemingly less technologically daunting, offer their own key challenges in terms of content integration and platform interconnectedness.