bluuebunny/crossref_metadata_2025 huggingface dataset:
AIED in K-12 Classrooms: Challenges and Opportunities from an Ethics Lens
Authors: MUNSHI Anabil
Published: January 2025

The recent growth of the AIED field has made available several new tools to facilitate personalized learning and teaching. However, this has also raised concerns over the ethical principles and inclusivity guidelines that should monitor the use of AI tools in the classroom, especially in K-12 settings. This paper looks at some primary use cases of AI in education and reviews the concerns and challenges raised by AIED researchers over applying AI in K-12 educational spaces. We then discuss the ethical guidelines suggested by global agencies, government organizations, and AIED scholars, to direct educational policies that can help integrate AI tools in K-12 classrooms in a more ethical and inclusive manner.
Game-Based Learning of AI Image Recognition on Computational Thinking and Self-Efficacy of Undergraduates
Authors: HSU Ting-Chia , HSUEH Hui-Wen
Published: January 2025

With the rise of artificial intelligence (AI), cultivating the AI abilities of the new generation of learners has become crucial. However, currently, there is a lack of tools in education found to effectively enhance students' AI abilities. To discover effective ways to foster students' understanding of AI concepts and skills, this research developed an Artificial Intelligence Image Recognition Robot unit, which was conducted through a series of AI courses in conjunction with educational robots. There are total of 41 undergraduates participating the instructional experiment. The first half of the experiment focused on introducing AI concepts and implementing image recognition models, while the second half involved robot programming and a robot board game competition. At the beginning and the end of the nit, students completed the AI Learning Achievement Test, the Programming-Oriented Computational Thinking scale and the Robot Self-Efficacy Scale to assess differences before and after the unit. The research found that by taking the AI Image Recognition Robot unit, students' AI learning achievements, programming-oriented computational thinking, and robot self-efficacy were significantly enhanced. This suggests that students will be better equipped to evaluate and understand the effectiveness of algorithms in programming and problem solving in the future. It is recommended that robots be incorporated as educational tools when engaging in AI learning.
Using a four-step learning activity in a programming course: classroom participation, learning performance, and attitude
Authors: LI Liang-Yi , HUANGb Wen-Lung , CHUNG Chien-Ping
Published: January 2025

In a programming course, a teacher has to teach not only programming concepts and syntax but also how to implement programs. However, teachers do not have enough time to complete the tasks. To address the problem, this study developed a four-step learning activity in a flipped classroom, which consists of testing, task assigning, collaborative problem-solving, and presentation. This study also conducted an instructional experiment for examining the effects of the four-step learning activity on students’ classroom participation, learning performance, and course satisfaction. The results showed that the students with the four-step learning activity and the students with the traditional instruction demonstrated the same level of classroom participation. While the students with the four-step learning activity had higher learning performance than the students with the traditional instruction. The students perceived that the four-step learning activity can facilitate learning motivation, classroom interaction, learning performance, and help-seeking. They hoped that the four-step learning activity can be used in other programming courses in the future.
MEGA World – A Platform of Multiplayer Educational Game for All
Authors: CHANG Maiga
Published: January 2025

This paper presents MEGA World, a platform that allows teachers to create their own virtual worlds for their course so their students can learn required knowledge and skills as well as be stealth assessed in a non-pressure environment. Students will walk through the learning and assessment activities by wondering and exploring the virtual world via interest driven. In this paper, various quest types will be introduced, and their use cases will be explained with the content from four graduate level and undergraduate level courses in Athabasca University. At the end, users’ experiences and their preliminary perceptions toward the use of MEGA World will be summarized – users spent on average 70 to 81 minutes on the gameplay activities even they were introduced MEGA World and its activities in the last second week of the term/semester; moreover, they had very positive opinions on the use of it to replace the supervised exam.
An Exploratory Study on Effects of WeChat-Assisted Self-Regulated English Writing Instruction on EFL Learners’ Motivation
Authors: ZHAO Ying , ZOU Di , XIE Haoran , LIU Pei
Published: January 2025

Scholars suggested using social networking to assist second language and foreign language learning. Researchers in the present study designed and developed a Mini Program on the WeChat platform to assist university EFL learners in improving self-regulated English writing. This exploratory study aims to investigate the effects of WeChat-assisted Self-Regulated English Writing Instruction on university EFL learners’ motivation in English writing. A quasi-experimental design with forty-five second-school-year university students was conducted for two weeks. Participants were randomly divided into the control group, the traditional group and the WeChat-assisted group. They took a pre- and post-questionnaire survey on English writing motivation before and after the intervention. Descriptive statistic analysis was used to show the status quo of the participants’ motivation, paired sample t-test was used to analyze the differences in motivation within each group before and after the intervention, and ANOVA analysis was used to compare differences in motivation between groups after the intervention. Both the traditional approach and the WeChat approach generated positive impacts on students’ overall motivation, self-efficacy and interest. Students using the traditional approach outperformed those using the WeChat-assisted approach but the results did not reach a significant level. Implications for future studies are suggested.
A Step toward Characterizing Student Collaboration in Online Knowledge Building Environments with Machine Learning
Authors: LEE Alwyn , TEO Chew , ONG Aloysius
Published: January 2025

Existing research has substantial progress in uncovering outcomes of collaborative learning in recent years, but more attention can be directed towards the better understanding of collaborative learning processes via quantitative frameworks and methods. Through the use of knowledge building as a collaborative learning pedagogical approach, it is possible for researchers to glean deeper insights into aspects of students’ collaboration within authentic learning environments. In this paper, the multimodal approach of data collection and analysis was conducted with a proposed conceptual analytical framework that can characterize constructs of collaborative activities in a knowledge building classroom using machine learning methods. The application in a pilot is discussed along with how this conceptual development can offer a summary of new insights into students’ individual and group collaborative trajectories during learning tasks.
IOT-integrated SDGs Scenario-based Games in the Classrooms
Authors: LIN Ching-Chieh , SHIH Ju-Ling , LU Yu-Hao
Published: January 2025

The Internet of Things (IoT) technology has found widespread application in various facets of daily life, including education. Through the integration of IoT technology and authentic open data into educational materials, students can engage in more interactions and develop a sense of participation. This, in turn, enhances their learning interests and motivation. Additionally, students can gain a more lifelike and tangible learning encounter in the classroom, particularly through scenario-based gaming. This research endeavors to utilize the Sustainable Development Goals (SDGs) as content for the creation of IoT-integrated classroom games. These games aim to immerse participants in the intricate web of social and environmental conflicts. Employing an exploratory learning approach, the goal is to augment students' awareness of SDG issues and cultivate multiple representation literacies.
Ethical and Privacy Concerns in Artificial Intelligence Dialogue Systems: Do Students in Higher Education Really Care About Them?
Authors: ZHAI Chunpeng
Published: January 2025

This work-in-progress paper investigates on whether students in higher education really care about the ethical and privacy concerns in the use of artificial intelligence (AI) dialogue systems. Based on semi-structured interviews of 37 undergraduate students, this study uncovered a variety of viewpoints. The study found that despite their awareness of ethical and privacy concerns in the use of AI dialogue systems, the majority of students did not put much effort in addressing these issues. Around 75% of the students prioritized efficient and effective university assignment completion with the help of AI dialogue systems over these ethical and privacy concerns. The study also revealed that cultural differences influenced students’ attitudes towards ethical and privacy concerns. Specifically, students from high-context cultures, where a significant portion of communication relies on shared experiences, implicit cues, and non-verbal elements, showed less concern compared to those from low-context cultures, where their communication relies heavily on explicit messages, direct language, and precise information. This study recommends several strategies to address privacy and ethical concerns, including the development of comprehensive data management policies, the use of data anonymization and minimization techniques, and the enforcement of stringent security measures to prevent data breaches.
The Role of Students’ Help-seeking Profiles in the AI-assisted Game-based Learning
Authors: CHEN Ching-Huei
Published: January 2025

This study aims to investigate the impact of students' help-seeking profiles on their seeking help from AI in a digital game-based learning environment. The study involved 102 middle school students who played online game and sought help from ChatGPT while solving physics problems. The results revealed that students' help- seeking profiles such as instrumental help-seeking were related to their help-seeking behavior in the game. The findings highlight the importance of considering individual differences, such as help-seeking profiles in designing AI-assisted learning environments to better support students' learning and help-seeking behavior.
Unmanned Robotic Online Laboratory with an Intelligent Cloud Teacher
Authors: HAN Dongkun , LEUNG Martin
Published: January 2025

One-on-one student-instructor communication is essential in many aspects for science and engineering education. Especially under and post COVID-19, individual student-instructor interactivity becomes increasingly precious yet growingly difficult. To address this issue, this poster paper develops a so-called “cloud teacher”, which is an intelligent conversational agent for answering the questions from students in building and manipulating their robots online. The basic underlying idea is to train the agent by utilizing open-source artificial intelligence tools from Google’s DeepMind, such that the agent can understand and answer the questions raised by students in making their robots online. Specifically, distinct features of cloud teacher include: 1) Provide instant textual answers to students’ questions regarding robot assembly and computer programming (Arduino and SolidWorks: Two most common software in robot control and 3D printing). 2) Supply video demonstrations for helping students in building and manipulating their robots. 3) Automatic assessment of students’ online lab performance on controlling their robots. 4) Personalized quizzes based on big data analytics of students’ historical questions and performance. Based on the above features, an unmanned robotic online lab has been constructed with 11 sets of real robotic arms, controllers, touchable monitors and corresponding software. The approach of AI- powered conversational agent has demonstrated its effectiveness in 4 engineering courses with more than 300 students. Furthermore, we collaborated with an oversee university, and applied it in the distant robotics education with 50 students from U.K. This method has a high potential to be explored across different disciplines and countries for virtual experiential learning in the new normal.
Catalyzing Python Learning: Assessing an LLM-based Conversational Agent
Authors: SINGH Daevesh , NISHANE Indrayani , RAJENDRAN Ramkumar
Published: January 2025

The rapid rise of digital learning platforms has ushered in an era of educational transformation. While these platforms offer the advantage of scalability, they often fall short in facilitating meaningful interaction, which is pivotal for effective learning. Addressing this concern, our study introduces PyGuru 2.0, an innovative online learning environment for Python programming that aligns with the ICAP framework with an advanced conversational agent. We further investigate the interactions between students and a chatbot, employing a qualitative approach to comprehensively explore the diverse ways in which students interact with the chatbot. The interaction categories encompass a wide spectrum, including code assistance, error resolution, and conceptual explanation. In future, we plan to further elaborate on this coding scheme and see its impact on students’ learning outcomes.
Designing Educational Personas using Generative AI
Authors: TERZIC Ivan , DROBNJAK Antun , BOTICKI Ivica
Published: January 2025

In this paper, the use of generative AI for creating educational personas is presented. Prior to the appearance of generative AI, conversational AI was used to enhance educational environments with the use of technologies such as chatbots. Generative AI brings a new dimension as it allows for the creation and use of personas mimicking real people, historical figures, and even fictional characters. Such designs potentially bring a new immersive and interactive dimension to learning, but further research is required to understand the full potential of these technologies. In this paper, the experiences and challenges in appropriating language learning models to act as potential educational personas are described.
Urban Living Lab Enhanced by a Mobile Application as a New Way to Educate Towards Green and Inclusive Cities
Authors: DUDA Ewa , GONTAR Łukasz , KOCHAŃSKI Maksymilian , KORSBREKKE Mari
Published: January 2025

Amidst rapid technological advancements and escalating environmental crises, there is a pressing need for innovative educational approaches addressing these challenges. This study introduces the Urban Living Lab (ULL) concept as an educational environment to engage households in sustainable food production enhanced by a mobile application. Leveraging a learning-by-doing approach and new technologies, the study focuses on the "SmartFood” project, which aims to reduce the environmental footprint by promoting self-produced food and fostering pro- environmental behaviors in urban communities. The research employs co-design workshops with future ULL participants to understand their needs and expectations. Utilizing thematic analysis, the study identifies both enablers and barriers to the project's implementation. The SmartFood mobile app emerged as a pivotal tool, providing users with valuable information on plant growth conditions and facilitating communication among participants. The study reveals that ULL enhanced by mobile application can serve as effective platform for educating households on sustainability. It also highlights the need for more frequent and detailed educational sessions to maximize the sustainability impacts. Overall, the study contributes to the limited body of research on ULLs as educational environments beyond higher education and offers practical insights for the co-design of sustainable urban food systems.
An Empirical Study of the Computational Thinking Learning Game, “Online Robot City”
Authors: HSU Ting-Chia , HSU Tai-Ping
Published: January 2025

Having strong computational thinking (CT) skills is key for students studying computer science (CS) and STEM-related fields. However, relying too heavily on programming languages can hinder students' understanding and application of CT concepts. Therefore, to effectively promote the learning of CT, this study conducted a quasi-experimental design to integrate two different questioning strategies into the Online Robot City game for teaching purposes. The study aimed to explore the impact of using the 5W1H strategy and the context-based concept mapping strategy for information gathering and problem decomposition on students' achievement in CT and on their learning behavior patterns. The results are expected to respond to the importance of different learning approaches, in particular the context-based thinking method like the context-based concept mapping strategy proposed in this study, which may make relatively more contributions to CT learning achievement and self-efficacy.
An Interactive Robot Lecture System for Attention and Understanding Recovery
Authors: SHIMAZAKI Toshiyuki , KASHIHARA Akihiro
Published: January 2025

In lecturing, lecturers need to control the attention of learners to maintain while monitoring their learning states. We have claimed that a robot as lecturer can properly conduct nonverbal lecture behavior to control learners’ attention and enhance their engagement compared with human lecturers. Although attention control is possible in shorter lectures, it would be more difficult for the robot to maintain learners’ attention in a longer lecture. This prevents them from becoming aware of an important part of the lecture contents to understand. In this work, we have been developing an interactive robot lecture system that interacts with learners to recover their attention and understanding when they overlook the lecture contents. To recover learners’ attention, the system first estimates their states of attention and understanding from their posture and gaze during lectures. Second, the system reconstructs a lecture scenario to generate an interactive lecture behavior, which combines non-verbal behavior about pause, walk, and repeat with paralanguage. The interactive behavior is implemented using a humanoid robot NAO. Third, the system provides questions about the slide contents for recovering their understanding when they lose their attention in spite of attention recovery.
ExGen: Ready-To-Use Exercise Generation in Introductory Programming Courses
Authors: TA Nguyen Binh Duong , NGUYEN Hua Gia Phuc , GOTTIPATI Swapna
Published: January 2025

In introductory programming courses, students as novice programmers would benefit from doing frequent practices set at a difficulty level and concept suitable for their skills and knowledge. However, setting many good programming exercises for individual learners is very time-consuming for instructors. In this work, we propose an automated exercise generation system, named ExGen, which leverages recent advances in pre-trained large language models (LLMs) to automatically create customized and ready-to-use programming exercises for individual students on- demand. The system integrates seamlessly with Visual Studio Code, a popular development environment for computing students and software engineers. ExGen effectively does the following: 1) maintaining a set of seed exercises in a personalized database stored locally for each student; 2) constructing appropriate prompts from the seed exercises to be sent to a cloud-based LLM deployment for generating candidate exercises; and 3) implementing a novel combination of filtering checks to automatically select only ready-to-use exercises for a student to work on. Extensive evaluation using more than 600 Python exercises demonstrates the effectiveness of ExGen in generating customized, ready-to-use programming exercises for new computing students.
Development of Estimation Method for Learner’s Emotional Concealment During Learning Using Biometric Information and Feedback Model
Authors: SHINOHARA Koichi , MURAMATSU Keiichi , MATSUI Tatsunori
Published: January 2025

Although it is important to understand learners’ emotional situations in teaching and learning processes, learners sometimes do not express negative emotions intentionally. This study has termed this intentional non-expression of negative emotions “emotional concealment,” and examined its estimation from learners’ biometric information. In particular, we focused on learners’ facial features, and attempted to clarify their relationship with emotional concealment. Using these results, we attempted to quantify emotional concealment from the time-series data of biometric information and detect it as an outlier. Additionally, we attempted to develop a feedback generation system to inform teachers about emotional concealment.
Development of a Learning Support System for playing Ryuteki in Gagaku for Beginners
Authors: UENO Yasushi , SOGA Masato
Published: January 2025

Ryuteki is one of the wind instruments in gagaku, a traditional music of Japan since ancient times. This instrument is difficult for beginners to acquire playing skills. The best way to learn how to play ryuteki is to learn from an expert, but the opportunity is limited due to time or cost constraints in gagaku. Therefore, in this study, we developed a system that automatically judges whether the pitch and timing of the sound are correct when a beginner plays ryuteki using sound processing technology, and presents feedback on the results.
Tackling Unserious Raters in Peer Evaluation: Behavior Analysis and Early Detection with Learner Model
Authors: LIANG Changhao , HORIKOSHI Izumi , MAJUMDAR Rwitajit , OGATA Hiroaki
Published: January 2025

Peer evaluation of individual or group work is often adopted in team-based learning design. However, some raters may not take the evaluation process seriously and exhibit behaviors such as using the same score, rushing through evaluations, or not evaluating during the presentation. This study investigates the issue of unserious peer evaluation in group presentations, focusing on their behavior patterns. Using evaluation behavior analysis indicators, we identified unserious raters who exhibited low reliability in the peer evaluation process. Further, we conducted a preliminary analysis to detect unserious raters based on learner model data available before the peer evaluation process. This information can assist teachers in providing personalized prompts and interventions prior to the peer evaluation process, thus enhancing the evaluation quality of these students with timely prompts to them.
The Development and Evaluation of the Platform for Online High-Level Cooperative Games
Authors: LIU Feng-Lung , HONG Geng-De , SHIH Ju-Ling , Ghinea George
Published: January 2025

The design of educational games plays an increasingly important role in modern education. By combining learning with games, it can attract students' attention and making learning more interesting. This study describes the development of a platform designed specifically for the online high-level cooperative game in which students are encouraged to achieve their own goals as well as common goals to reach win-win situation. Technology Acceptance Model (TAM) was used to analyze students' overall perceptions of the platform. The results show that the design of the game platform had high perceived usefulness and perceived ease of use, and players also showed positive attitudes and usage intentions. Therefore, a good game platform should have interactivity, educational value, problem-solving settings, and usability to provide the best gaming experience and have a positive impact on learning.
Assessing college students’ sense of community for advancing community knowledge
Authors: SEET Chih-Hui , TSAI Yi-Ning , HUNG Guo-Tsai , HONG Huang-Yao
Published: January 2025

This case study aims to examine students’ sense of community (SOC) during online learning. Participants were college students data came from the “Classroom Community Scale” and students’ online discussion in a platform designed based on knowledge building theory and pedagogy. The results showed that students were able to develop a strong sense of community and perceived learning as a knowledge creating endeavor as guided by knowledge building pedagogy.
Teachers developing more creative learning views via online knowledge building activities
Authors: Chen Mei-Ju , Guo Chao-Yu , Hong Huang-Yao
Published: January 2025

This study investigates how teachers who were engaged in online knowledge building via guided perspective-taking (PT) activities to discuss their critical teaching incidents (CTIs) helped them to develop creative learning views that see learning not merely as knowledge acquisition and exchange, but also as knowledge creation. Data were mainly from online discussion. The main findings indicate that PT-based KB activities in a technological platform can guide teachers to reflect deeply on the CTIs they experienced before and on how they might be able to help students learn better in a more constructivist and creative manner.
Unveiling University Students' Data Literacy: A Case Study on Modeling Reasoning in Data Mining Projects
Authors: ZHANG Tianqi
Published: January 2025

In the era of big data, cultivating students' data literacy is of paramount importance. Data literacy encompasses the abilities to collect, manage, analyze, and apply data. As data science is a superset composed of mathematics and statistics, computer science, and specific application fields, it is crucial to investigate data literacy development through the lens of statistical education. An essential component of statistical reasoning is modeling reasoning, which is also fundamental to data literacy. However, limited research exists on the manifestation of data literacy among university students in real-world tasks. Therefore, this study explores the forms of modeling reasoning exhibited by university students in a data mining project. The findings reveal that, in a real teaching setting, university students' modeling reasoning ability follows a spiral progression. Considering these insights, modeling reasoning, as the core of data mining activities, plays a pivotal role in fostering students' data literacy. To cultivate data literacy among university students in the age of big data, we recommend implementing project-based learning, incorporating ill-structured problems and real, complex, massive datasets as project backgrounds.
The role of individual preparation for knowledge construction in collaborative argumentation: An Epistemic Network Analysis
Authors: Chen Wenli , Su Junzhu , Lyu Qianru , Chai Siew Cheng Aileen , Li Xinyi , Su Guo , Ng Eng Eng
Published: January 2025

Through collaborative argumentation, students gain in-depth understanding of learning content when they build on one another’s knowledge. Although individual preparation (IP) is found to be effective to foster collaborative learning, the mechanism of how IP influence the knowledge construction behavior is underexplored. This study investigated how IP influenced secondary school students in relation to knowledge construction behavioral patterns when participating in online collaborative argumentation activities. 20 students participated in two computer-supported collaborative argumentation lessons with one group with IP, and the other group without. Screen video recordings of students constructing arguments in groups during two lessons were collected and analyzed. Epistemic Network Analysis was conducted to examine students’ knowledge construction behaviors in the two lessons with and without IP. The results show that there were significant impact on students’ knowledge construction characteristics between the two lessons. Students who did not go through the IP phase tended to exhibit behaviors related to ideas refinement more than the students who went through the IP phase. The implications of how to design and implement effective knowledge construction are discussed.
Students know more than they can tell: Understanding learners’ ideas of heat transfer via model revision activities
Authors: PRIYADARSHINI Rajashri , DASGUPTA Chandan , MURTHY Sahana
Published: January 2025

Students' preconceptions significantly influence learning as they bring prior experiences and ideas alongside formal knowledge. Considering only explicit or conscious conceptual models can misinform teachers' efforts. Framing the problem using both conscious (verbal-symbolic knowledge, conscious models) and unconscious (implicit models, core-intuitions) conceptual resources offer a powerful framework. This study interprets the ideas of heat transfer of two grade 11 students in depth (from a total N=9), through this framework. We look at how Nash and Payal use different conceptual resources to build an explanatory model of heat transfer in a cup through the model revision activities. We found different ideas of heat (heat as a substance, steam as unique property of heat) to be unconsciously influencing how students understand the concept. Harnessing core-intuitions into conscious models enabled Nash to build better explanatory models, whereas Payal extensively relied on her verbal-symbolic knowledge. In the macroscopic activities, both students tended to use their judgments from their sensory perceptions, going against their verbal-symbolic knowledge. Using the metacognitive prompts on Knowledge Forum also supported student’s critical analysis about their ideas before uploading them on the forum.
Discussion Support Agent System to Promote Equalization of Speech among Participants
Authors: NISHIMURA Ryunosuke , IHARADA Risa , SUGAMOTO Yuya , ISHII Yutaka , MOCHIZUKI Toshio , EGI Hironori
Published: January 2025

Reducing the imbalance of speech length in discussions is expected to improve decision making and collaborative learning. In this paper, we propose a facilitation system based on the cumulative percentage of utterances in discussions. We surveyed the effectiveness of this system on the collaborative learning from the perspective of standard deviation of utterance length and percentage of utterances throughout discussions. we collected 20-minutes discussion data under the topic “What is a thoughtful consideration?” and “What is happiness?” from seven groups of three participants. Each group had two discussions, one with the system and one without the system. The results revealed that the facilitation system decreased subjective and objective imbalance of speech length. Additionally, turn-taking was active in the group which facilitation by the system was effective.
Exploring Group Formation Strategies in Computer-Supported Collaborative Learning: A Systematic Review
Authors: TANG Jiamin , ZHOU Huihan , TAN Yajing , CHEN Guang
Published: January 2025

In this study, three independent reviewers conducted a systematic review of articles on group formation strategies in computer-supported collaborative learning (CSCL) and synthesized and structuralized the involved grouping attributes and evaluation indicators with some other features. The study aims to contribute to efficient and effective group formation in educational practices in CSCL environments.
A Comparative Analysis on the Effects of Cognitive Tools in Data Inquiry Cultivation
Authors: ZHANG Hui , WU Bian , HU Yi-Ling , XU Yu-Jie
Published: January 2025

The advent of the big data era has elevated the importance of understanding, analyzing, and mining data, necessitating data science education with cognitive tools aiding data inquiry. However, limited appraisals exist, especially regarding diverse tools' impact on data inquiry cognitive aspects. This study addresses this void by examining cognitive tool impact on data inquiry. The study collected discourse and questionnaire data generated during online collaborative tasks. Epistemic network analysis and difference testing unveiled cognitive pattern disparities, learning motivation, cognitive load, and self-efficacy variations between groups. Block- based group displayed robust cognitive connections in data understanding and preparation. Text-based group focused on modeling and optimization iterations. Motivation and load didn't differ significantly, yet block-based group showed higher self- efficacy. Study concludes by acknowledging limitations and suggesting future research directions.
Preparation for Future Lockdowns: A Comparison of Student LMS Activity During and After COVID-19
Authors: LAGMAY Ezekiel Adriel , RODRIGO Maria Mercedes
Published: January 2025

We use CausalImpact analysis to compare student online activity during the COVID-19 pandemic, when all classes were online, and post-pandemic, when classes were offered in a mix of modes. Student activity was operationalized as activity logs from the Canvas Learning Management System (LMS). When compared with student activity in pandemic fully online classes, we found that, contrary to our hypotheses, student activity in post-pandemic fully online classes decreased while activity in post- pandemic fully onsite classes increased. We attribute the decrease in participation in fully online classes to factors such as Zoom fatigue and feelings of loneliness and prolonged isolation. The increase in student online activity in fully onsite classes might be explained by students’ general preference for face-to-face interactions. They may have felt more connected to teachers and peers, leading to greater productivity overall. The discourse about the transition from online learning brought on by the pandemic is dominated by survey research. By examining student online activity in an LMS, this paper contributes an analysis of empirical data that supports the findings of prior studies about student perceptions of the online learning experience and their adjustments to the next normal.
Using Augmented Reality to Facilitate Music Learning for Preschool Children
Authors: WEI Xiaodong , QIU Rui , LIU Ruixue
Published: January 2025

New technologies such as Augmented Reality (AR) have been widely used in education fields. AR’s deep integration of virtual objects and real environments meets the diverse learning needs of learners. However, relatively few studies have explored the application of AR in preschool music education. Therefore, this study developed a mobile AR application for preschool children in learning music and investigated its impact on children’s learning achievement and enjoyment. We used a mixed research method, measuring academic performance through pre-test and post- test, a questionnaire for children’s enjoyment, and interviews to determine children’s views of AR usage in music learning. Sixty participants in two kindergarten classes participated in this study. The children in the experimental group used mobile-based AR applications for music learning, while the control group learned the same content through the traditional teaching method. The results showed that compared with the traditional teaching method, mobile AR has significantly improved children’s academic performance and enjoyment. In addition, the interview results also showed that the children considered AR learning activities to be very interesting and that they are willing to use AR for music learning in the future. The conclusions provide a concrete practical case on how to effectively integrate AR into preschool music education.
Visual Attention Patterns in Processing Compiler Error Messages
Authors: TABLATIN Christine Lourrine , RODRIGO Maria Mercedes
Published: January 2025

The difference in the visual attention of subjects while performing a debugging task can be measured using fixation count and fixation duration metrics. Thus, this paper investigated the visual attention patterns of high and low performing students engaged in a defect-finding task on multiple programs using these metrics. We performed statistical tests on the proportional fixation durations and fixation counts on the error lines and the compiler error messages to determine the difference in the visual attention patterns between the groups. The results of the statistical analysis revealed a significant difference between the high and low performing students across all programs. This implies that high performing students were associated with significantly higher visual attention on the error lines of the programs than the low performing students. However, the analysis of the proportional fixation duration and fixation count on the compiler error messages revealed no significant difference between the groups. The results suggest that both groups showed similar visual attention to the compiler error messages. The findings of this study provide insights into the visual attention patterns of student programmers in processing compiler error messages. High and low performing students could be distinguished based on their visual attention patterns on the error lines but not on the compiler error messages. Further, high performing students prefer a more analytical processing approach and pay attention to relevant code elements in debugging to correctly identify the errors while low performing students choose a more holistic approach.
Analyzing Learning Patterns and Potential Interventions in First-Year Compulsory Course at an Online University
Authors: KATO Yasuhisa
Published: January 2025

Online universities are becoming increasingly popular for their flexibility, especially for adult learners. However, the autonomy of online learning can lead to challenges in maintaining motivation and engagement. This study at Tokyo Online University examines the learning patterns of first-year students in a mandatory academic literacy course. It analyzes the relationship between students' learning history and grades to identify patterns of success and failure in order to develop targeted intervention strategies. The literature review discusses MOOC attrition rates, self-regulated learning strategies, and the relationship between learning patterns and academic performance. The analysis clusters students based on engagement and performance, reveals distinct patterns between successful and unsuccessful students, and highlights early course engagement as critical. The study recommends introducing information on successful study strategies during new student orientation and sending targeted messages to disengaged students.
A Comparative Study of Traditional and Augmented Reality-Based Engineering Drawing Instruction: Effects on Visualization Skills and Cognitive Load
Authors: BHAGAT Kaushal Kumar
Published: January 2025

Augmented reality (AR) is utilized in education to enhance students' skills and learning experience. We developed an AR-based application for engineering drawing (ED) courses, evaluating its impact on 3-D visualization skills and cognitive load. The study involved 392 first-year engineering students, with a control group using conventional methods and an experimental group using the AR application. Results showed that the experimental group outperformed the control group in 3-D visualization skills and experienced lower cognitive load. Students responded positively to the application, indicating that AR can enhance learning performance and experience in engineering education.
The Impact of Instructional Videos Supported by AI-driven Tutoring System on EFL Listening and Speaking
Authors: TAN Xiangyu , ZUO Xiuyuan
Published: January 2025

Recent studies highlight the beneficial effects of instructional videos (IVs) and intelligent tutoring systems (ITS) on the oral development and learning attitudes of learners of English as a Foreign Language (EFL). Despite these advances, studies investigating the combined impact Of Al-driven tutoring systems with instructional videos on EFL learners' listening and speaking skills remain sparse. This study aims to fill this research gap by employing a 15-week quasi-experimental design with 43 adult participants, divided into two groups. The experimental group (Group IVIT) utilized instructional videos integrated with an Al-driven tutoring system (iTutor), whereas the control group (Group IV) engaged with instructional videos alone. Comparative analysis demonstrated significant enhancements in listening and speaking proficiencies within Group IVIT. which notably outperformed Group IV in speaking skill development. Additionally, learners in Group IVIT reported more positive perceptions of the opportunities for speaking provided by IVIT and experienced less anxiety related to speaking tasks. These findings emphasize the significant potential of integrating intelligent tutoring systems with traditional learning modalities to accelerate language acquisition in adult EFL learners.
Authorship Forensics Portal
Authors: SCHMIDT Robert , CHANG Maiga , CHENG Hsiang-Han , FREDIN Greg , HAGHIGHAT Kevin , KUO Rita
Published: January 2025

This paper presents the research outcome, Authorship Forensics Portal, leveraging both Statistical Natural Language Processing (SNLP) and Convolutional Neural Networks (CNN) techniques to differentiate documents written by humans and ChatGPTs. The portal allows teachers to (1) upload labeled data that contains written text and its author; (2) configure parameters that are required for training models, e.g., 2-class (i.e., human and ChatGPT) or 3-class (i.e., human, ChatGPT 3.5, and ChatGPT as well as the train/test set split ratio, validation set ratio, and validation accuracy threshold for stopping the training process; (3) review the details of a trained model, e.g., the train/test set, the time spent, the prediction results like numbers, true positive, false positive, precision, recall, and f-value, etc.; (4) make their own trained models be private so only themselves can see and use or be public so other teachers can also see and use; and, (5) ask a chosen trained model for its opinion on whether a piece of text written by human or generative Al (e.g., ChatGPT for 2•class prediction and ChatGPT 3.5 or ChatGPT 4 for 3-class prediction). The results demonstrate a significant ability of the models to distinguish between human and Al-written text, with highest precision 0.9868 (Fo_5 score 0.9647) for the 2-class (human and ChatGPT) testing subset and highest precision 0.9875 (Fo.5 score 0.9753) for the 3-class (human, ChatGPT 3.5, and ChatGPT 4) testing subset.
Designing Learner-Centered Collaborative Learning by Incorporating AI-Based Teacher/Learner Agents with a Cognitive Model
Authors: HAYASHI Yugo , SHIMOJO Shigen , KAWAMURA Tatsuyuki
Published: January 2025

This paper presents collaborative concept-mapping tutor (CoCot ver.2), a collaborative learning support system that integrates concept maps with a conversational agent. CoCot ver.2 features two agents: a teacher agent and a student agent. The teacher agent acts as a human instructor, engaging in conversations with learners, aiding their metacognition, and summarizing the discussion content. The student agent learns from the learners' concept map creation and generates their own concept map knowledge. These agents are developed using (1) a cognitive architecture (ACT-R) for knowledge generation for the agents' concept map and (2) GPT 3.5 for part of the language processing for agent-based feedback.
A Proposal of Quality Assurance Programming Exercise
Authors: ISHIHARA Nobuya , HUDA Samsul , NOGAMI Yasuyuki
Published: January 2025

This paper proposes a new exercise format for programming assignments called Quality Assurance Programming Exercises (QAEx), which incorporates the use of generative artificial intelligence (Al). In QAEx, students are allowed to use Al tools to generate solutions for programming problems and are also required to submit comprehensive test cases to validate these solutions. Students can use Al to create these test cases as well. The inclusion of Al in both solution development and testing underscores the importance of effective software testing and encourages students to engage more deeply with programming and quality assurance practices. A preliminary trial of QAEx was conducted to assess students' engagement and performance.
Learning Support Environment with Fill-in-Blank Exercise Based on Program Visualization System
Authors: YAMASHITA Koichi , SUZUKI Shuya , KOGURE Satoru , NOGUCHI Yasuhiro , YAMAMOTO Raiya , KONISHI Tatsuhiro , ITOH Yukihiro
Published: January 2025

This paper describes a learning support system that allows learners to answer code fragments fitting blanks in target program code while observing program visualization (PV), and an evaluation experiment conducted to measure the effectiveness of the system. Most existing PV systems have functions to visualize changes in target domain world caused by each execution of statement and to allow learners to answer questions provided by the system, which is not sufficient in terms of learner engagement. In this study, we extended the PV system developed in our previous study by adding a function to provide fill-in-blank exercise to learners and allow learners to answer it, and a function to visualize the PV according to the learner's answer. The effectiveness of our system was measured by conducting a comparison experiment with a previous system. Although the sample size was not sufficient, the experimental results suggest that learning with our system could contribute to improve learners' understanding of program and that our system would be more effective than the previous system.
Verbal Interaction Patterns in Online Collaborative Learning Design: Comparison of High Performing and Low Performing Groups
Authors: CHEN Wenli , ZHENG Lishan , HO Mei-Yee Mavis , HU Hua , LYU Qianru
Published: January 2025

This study explores the differences in students' verbal behavior sequences between high-performing (HP) and low-performing (LP) groups in a computer-supported collaborative learning (CSCL) environment. Employing quantitative content analysis and Lag Sequential Analysis (LSA), this study analyzed the verbal interactions of these two groups. The findings reveal that HP groups frequently engaged in cycles of negotiation, clarity-seeking, and task coordination, leading to effective collaboration and problem-solving. In contrast, LP groups exhibited fragmented problem-solving approaches and frequent off-task behaviors. These insights highlight the importance of structured support and focused task management in enhancing collaborative learning outcomes. These findings suggest that educators should foster learning environments that promote continuous critical evaluation and seamless coordination to improve group performance.
Infrastructuring for Collective Cognitive Responsibility: A Case Study of Student Knowledge Building Design Studio
Authors: TEO Chew Lee , ONG Aloysius , LEE Alwyn , YUAN Guangji , LOO Kennedy
Published: January 2025

In this paper, we present the design of a two-day student programme called the student Knowledge Building Design Studio (sKBDS), intended to promote collective cognitive responsibility (CCR) by focusing on student interests in real sustainability- related problems and giving them opportunities to drive the collective inquiry. Participants included 36 primary students from three different schools (six interest groups). The design of sKBDS shows how CCR developed over time across interest groups. Our analytical approach included the use of theory building moves to code students' ideas and the use of an analytics tool called "Ideas-Building" to examine collaborative patterns from their online discussions. Our findings suggest a positive impact of the sKBDS design in supporting students to theorize, build, and improve ideas around their sustainability-related problem. However, we also found salient patterns in collaborative engagement across groups, suggesting that CCR development is non-linear with purposeful student activities. We then discuss the implications for CCR designs in practice.