Language：Others   Publishing type：Research paper (scientific journal)   Publisher：Smart Learning Environments  

With the increasing use of digital learning materials in higher education, the accumulated operational log data provide a unique opportunity to analyzing student learning behaviors and their effects on student learning performance to understand how students learn with e-books. Among the students’ reading behaviors interacting with e-book systems, we find that jump-back is a frequent and informative behavior type. In this paper, we aim to understand the student’s intention for a jump-back using user learning log data on the e-book materials of a course in our university. We at first formally define the “jump-back” behaviors that can be detected from the click event stream of slide reading and then systematically study the behaviors from different perspectives on the e-book event stream data. Finally, by sampling 22 learning materials, we identify six reading activity patterns that can explain jump backs. Our analysis provides an approach to enriching the understanding of e-book learning behaviors and informs design implications for e-book systems.

DOI： 10.1186/s40561-021-00183-6

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Language：Others   Publishing type：Research paper (scientific journal)  

The abundance of courses available in a university often overwhelms students as they must select courses that are relevant to their academic interests and satisfy their requirements. A large number of existing studies in course recommendation systems focus on the accuracy of prediction to show students the most relevant courses with little consideration on interactivity and user perception. However, recent work has highlighted the importance of user-perceived aspects of recommendation systems, such as transparency, controllability, and user satisfaction. This paper introduces CourseQ, an interactive course recommendation system that allows students to explore courses by using a novel visual interface so as to improve transparency and user satisfaction of course recommendations. We describe the design concepts, interactions, and algorithm of the proposed system. A within-subject user study (N=32) was conducted to evaluate our system compared to a baseline interface without the proposed interactive visualization. The evaluation results show that our system improves many user-centric metrics including user acceptance and understanding of the recommendation results. Furthermore, our analysis of user interaction behaviors in the system indicates that CourseQ could help different users with their course-seeking tasks. Our results and discussions highlight the impact of visual and interactive features in course recommendation systems and inform the design of future recommendation systems for higher education.

DOI： 10.1186/s41039-021-00167-7

Language：Others   Publishing type：Research paper (scientific journal)  

Recommendation systems need a deeper understanding of users and their motivations to improve recommendation quality and provide more personalized suggestions. This is especially true in the education domain, the more about the student is known, the more useful recommendations can be made. However, although many studies on the course recommendation exist, studies on the students’ course selection motivations in universities are limited. This study investigates the factors that contribute to students’ choice when selecting courses in universities to better understand student perceptions, attitudes, and needs and leverage data-driven approaches for recommending and explaining the recommendations in university environments. A qualitative interview for university students (N = 10) comprised of open-ended questions as well as a questionnaire for students (N = 81) was conducted, aiming to investigate the main reasons behind their choices. The results of this study show that students highly value the course contents and the benefits of the course towards their future careers. Furthermore, students are influenced by other reasons such as the possibility of obtaining a higher grade, the popularity of professors, and recommendations from peers. Next, we extract the main categories of students’ motivations and analyzed the questionnaire data by employing statistical analysis methods as well as the k-means clustering algorithm to identify different types of students in terms of course selection. Based on our findings, we discuss implications for designing more personalized course recommendation systems.

DOI： 10.1186/s40561-021-00177-4

Language：Others   Publishing type：Research paper (other academic)  

Authorship：Lead author,　Last author,　Corresponding author  

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Authorship：Last author,　Corresponding author  

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DOI： 10.1007/978-3-031-98459-4_19

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DOI： 10.1007/978-3-031-98462-4_30

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DOI： 10.1007/978-3-031-98417-4_13

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Publishing type：Research paper (scientific journal)   Publisher：Applied Sciences Switzerland  

This study explores the factors influencing turn-taking coordination in virtual reality (VR) environments, with a focus on identifying key interaction dynamics that affect the ease of gaining speaking opportunities. By analyzing VR interaction data through logistic regression and clustering, we identify significant variables impacting turn-taking success and categorize typical interaction states that present unique coordination challenges. The findings reveal that features related to interaction proactivity, individual status, and communication quality significantly impact turn-taking outcomes. Furthermore, clustering analysis identifies five primary interaction contexts: high competition, intense interaction, prolonged single turn, high-status role, and low activity, each with unique turn-taking coordination requirements. This work provides insights into enhancing turn-taking support systems in VR, emphasizing contextually adaptive feedback to reduce speaking overlap and turn-taking failures, thereby improving overall interaction flow in immersive environments.

DOI： 10.3390/app142412071

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Language：Others  

This study investigates the effects of generative AI on the knowledge comprehension of university students, focusing on the use of ChatGPT strategies. Data from 81 junior students who used the ChatGPT worksheet were collected and analyzed. Path analysis revealed complex interactions between ChatGPT strategy use, e-book reading behaviors, and students' prior perceived understanding of concepts. Students' prior perceived understanding and reading behaviors indirectly affected their final scores, mediated by the ChatGPT strategy use. The mediation effects indicated that reading behaviors significantly influenced final scores through ChatGPT strategies, indicating the importance of the interaction with learning materials. Further regression analysis identified the specific ChatGPT strategy related to verifying and comparing information sources as significantly influenced by reading behaviors and directly affecting students' final scores. The findings provide implications for practical strategic guidance for integrating ChatGPT in education.

Language：Others  

The integration of ChatGPT as a supportive tool in education, notably in programming courses, addresses the unique challenges of programming education by providing assistance with debugging, code generation, and explanations. Despite existing research validating ChatGPT’s effectiveness, its application in university-level programming education and a detailed understanding of student interactions and perspectives remain limited. This paper explores ChatGPT’s impact on learning in a Python programming course tailored for first-year students over eight weeks. By analyzing responses from surveys, open-ended questions, and student-ChatGPT dialog data, we aim to provide a comprehensive view of ChatGPT’s utility and identify both its advantages and limitations as perceived by students. Our study uncovers a generally positive reception toward ChatGPT and offers insights into its role in enhancing the programming education experience. These findings contribute to the broader discourse on AI’s potential in education, suggesting paths for future research and application.

Publishing type：Research paper (international conference proceedings)   Publisher：International Educational Data Mining Society  

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Other Link： https://dblp.uni-trier.de/rec/conf/edm/2024

Language：Others  

Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge concepts and linguistic skills are hard to define, and diagnosis must be well-designed. Traditional approaches are widely applied for modeling knowledge in science or mathematics, where skills or knowledge concepts are easy to associate with each item. However, only a few works focus on defining knowledge concepts and skills using linguistic characteristics for language knowledge proficiency diagnosis. In addressing this, we propose a framework for language proficiency diagnosis based on neural networks. Specifically, we propose a series of methods based on our framework that uses different linguistic features to define skills and knowledge concepts in the context of the language learning task. Experimental results on a real-world second-language learning dataset demonstrate the effectiveness and interpretability of our framework. We also provide empirical evidence with comprehensive experiments and analysis to prove that our knowledge concept and skill definitions are reasonable and critical to the performance of our model.

Language：English  

Authorship：Lead author,　Last author,　Corresponding author   Publishing type：Research paper (international conference proceedings)   Publisher：Springer  

An emerging challenge in course recommendation systems is the need to explain clearly to students the rationale behind specific course recommendations. Consequently, recent research has transitioned from focusing primarily on the accuracy of these systems to prioritizing user-centric qualities, such as transparency and justification. This shift has led to an increased emphasis on methods that provide clear, understandable explanations for their recommendations. In response to this trend, our paper introduces an explainable recommendation framework. Utilizing this framework, we analyze existing course recommendation systems and explore the emerging research challenges and future prospects for explainable course recommendation systems.

DOI： 10.1007/978-3-031-60012-8_17

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Other Link： https://dblp.uni-trier.de/db/conf/hci/hci2024-36.html#MaYR24

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Authorship：Lead author,　Last author,　Corresponding author  

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Language：Japanese   Publisher：Special Interest Group on Web Intelligence and Interaction  

<p>Affective states play a crucial role in music, as music can influence both current emotions and long-term moods. We propose a novel <i>emotion-enhancing</i> hybrid music recommendation (EEMR) mechanism that finds the suitable criteria in selecting the best music playlist for improving user’s emotion by combining two recommendation techniques, i.e., content-based filtering and context-aware approach. This mechanism generates playlists that align with the user’s preferences and current emotion, while also supporting gradual improvement of the user’s emotion over time.</p>

DOI： 10.57413/wii.20.0_79

CiNii Research

Web of Science

Authorship：Lead author,　Last author,　Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)  

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Language：Others   Publishing type：Research paper (other academic)  

The COVID-19 pandemic has resulted in school closures all across the world, and lots of students have shifted from conventional classrooms to online learning. With the help of ICT technologies nowadays, learning online can be more effective in a number of ways. However, most of the online learning environments without instructors' attention may result in different learning patterns compared to the traditional face-to-face classroom. In this paper, we aimed at detecting the slide reading behaviors of the students by analyzing operational event logs from a digital textbook reader for a lecture offered in our university. We compared reading patterns between traditional face-to-face lectures and hybrid online lectures, our results show that online lectures lead to more off-task behaviors. Our analysis provides a rich understanding of e-book reading and informs design implications for online learning during the pandemic. The findings can also be used to improve the instruction designs and learning strategies.

Language：Others   Publishing type：Research paper (other academic)  

The abundance of courses available in university and the highly personalized curriculum is often overwhelming for students who must select courses relevant to their academic interests. A large body of research in course recommendation systems focuses on optimizing prediction and improving accuracy. However, those systems usually afford little or no user interaction, and little is known about the influence of user-perceived aspects for course recommendations, such as transparency, controllability, and user satisfaction. In this paper, we argue that involving students in the course recommendation process is important, and we present an interactive course recommendation system that provides explanations and allows students to explore courses in a personalized way. A within-subject user study was conducted to evaluate our system and the results show a significant improvement in many user-centric metrics.

Language：Others  

Language：Others  

Language：Others   Publishing type：Research paper (other academic)  

Course recommendation system is a useful tool that not only helps the students who have no sufficient experience to decide what they should study, but also leverages their full performance if they could study what they like or are interested in. Different from MOOCs, the selection and recommendation for hybrid learning environments such as university are relatively difficult. Students who enrolled in the same course may have completely different purposes and different interest. Employing the enrollment record data from Kyushu University, we conduct a systematic investigation on the course-taking pattern for recommendation. We then discuss the challenges to recommend suitable courses in university and propose a preliminary approach to address the challenges by designing a course recommendation mechanism based on association rule of previous course-taking pattern together with student interest.

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Type：Peer review 

Number of peer-reviewed articles in foreign language journals：6

Proceedings of International Conference Number of peer-reviewed papers：4

Type：Peer review 

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