エビデンスに基づく指導を支援する学習分析基盤の開発

京都大学新制・課程博士博士(情報学)甲第24735号情博第823号新制||情||138(附属図書館)京都大学大学院情報学研究科社会情報学専攻(主査)教授 緒方 広明, 教授 伊藤 孝行, 教授 吉川 正俊学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDGA

Fostering Evidence-Based Education with Learning Analytics: Capturing Teaching-Learning Cases from Log Data

Evidence-based education has become more relevant in the current technology-enhanced teaching-learning era. This paper introduces how Educational BIG data has the potential to generate such evidence. As evidence-based education traditionally hooks on the meta-analysis of the literature, so there are existing platforms that support manual input of evidence as structured information. However, such platforms often focus on researchers as end-users and its design is not aligned to the practitioners’ workflow. In our work, we propose a technology-mediated process of capturing teaching-learning cases (TLCs) using a learning analytics framework. Each case is primarily a single data point regarding the result of an intervention and multiple such cases would generate an evidence of intervention effectiveness. To capture TLCs in our current context, our system automatically conducts statistical modelling of learning logs captured from Learning Management Systems (LMS) and an e-book reader. Indicators from those learning logs are evaluated by the Linear Mixed Effects model to compute whether an intervention had a positive learning effect. We present two case studies to illustrate our approach of extracting case effectiveness from two different learning contexts – one at a junior-high math class where email messages were sent as intervention and another in a blended learning context in a higher education physics class where an active learning strategy was implemented. Our novelty lies in the proposed automated approach of data aggregation, analysis, and case storing using a Learning Analytics framework for supporting evidence-based practice more accessible for practitioners

E-book-based learning activity during COVID-19: engagement behaviors and perceptions of Japanese junior-high school students

Recent spread of the COVID-19 forces governments around the world to temporarily close educational institutions. In this paper, we evaluated learning engagement, level of satisfaction and anxiety of e-book based remote teaching strategy on an online learning platform. The research involves 358 students at an urban junior-high school in Japan. Learning logs were analyzed to measure student engagement, whereas survey responses indicated their perception regarding the remote learning experience. Log analysis revealed that the average completion rate over 267 learning materials was 67%. We also observed a significant decrease in engagement 3 weeks after remote learning and different subjects and grades. Survey analysis showed students felt both satisfaction and anxiety about remote learning. However, there were significant differences in the level of satisfaction between different grades. The results indicated that (1) maintaining students' motivation is a challenge to remote learning in secondary schools, and (2) we need to relieve students' anxiety about their own progress in the class and their classes after the break. This study is the first to report trends in actual teaching-learning engagement, which were recorded during sessions of emergency remote teaching in Japanese schools. The results can inform the future implementation of remote learning in junior-high schools

Evidence Mining Using Course Schedule

Creating evidence from learning big data has become increasingly important as we can use eLearning infrastructure and store learning log digitally. On the other hand, we need to time and effort to create evidence because it is manual. In this paper, we proposed the method to make evidence easier. Especially, we focus on procedure to automatically select the duration of intervention and comparison data based on the course schedule information. We simulated the procedure and confirmed the making a case based on course schedule information. In the discussion part, we mentioned the points that should be further improved for practical use in the future. Through our method, we will democratize the evidence-based practice to all the teachers in schools

Automatic detection of major depressive disorder via a bag-of-behaviour-words approach

A paper in the Third International Symposium on Image Computing and Digital Medicine (ISICDM 2019

Flip & Pair – a strategy to augment a blended course with active-learning components: effects on engagement and learning

Blended learning technique has adapted many new digitized tools to facilitate students with flexible and self-phased learning opportunities. The flipped classroom strategy, one of the blended learning models has been limited by low engagement of students in the online component. In the present study, we augment a Flip and Pair (F&P), an active-learning strategy into the blended learning course. Following the AB type single group quasi-experimental design, we evaluated the effects of F&P strategy on the student’s engagement and learning while orchestrating it for an undergraduate engineering physics course. Our results highlighted that there is a positive correlation between the engagement (computed based on learning logs of TEEL (Technology-enhanced and Evidence-based Education and Learning) platform in the F&P activities with that of the performance score (knowledge quizzes and final exam). F&P strategy had a better contribution compared to Flip and Traditional Teaching (F&TT) strategy with respect to both engagement and performance. Also, students exhibited a positive perception of learning and engagement. Based on our findings, we identified the key instructional measures that an instructor can follow to increase student engagement while using the F&P strategy

LA Platform in Junior High School: Trends of Usage and Student Performance

[LAK’20: 10th International Learning Analytics and Knowledge] 23-27 March 2020; The University of Frankfurt, Frankfurt, GermanyThis study reports the initial trends found in the pilot phase of a Learning analytics (LA) platform adoption at a Junior high school in Japan. The LA platform includes a Learning Management System (LMS), e-Book reader, and analytics dashboard that is accessible to both teachers and students. The interaction logs of those learning tools and mid-term test score for the third-year junior high school mathematics class with 120 students were analyzed. The result highlighted that a group of students who voluntarily explored the dashboard performed significantly better than the group of students who did not check the dashboards. However, both the groups’ e-Book interaction counts were not significantly different. This initial result was encouraging as the evidence was extracted from the data collected without any specific interventions. The findings also motivated further investigation in the usage pattern of the LA platform and design of interventions

Impact of School Closure during COVID-19 Emergency: A Time Series Analysis of Learning Logs

28th International Conference on Computers in Education, 23-27 November 2020, Web conference.Recent spread of the COVID-19 forces governments around the world to have temporarily closed educational institutions. Although many studies were published to announce the best practice under the school closure, we need to understand the impact of school close on students’ learning before that. In this paper, we evaluate the impact of the school closure on our online teaching-learning environment. We use CausalImpact model to infer the impact on our learning analytics system using the learning log stored in the system. The results show that the school closure increased the number of logs on LMS by 163%, but decreased the number of logs on e-book reader by 77%. However, focusing on a particular course, we found that students’ learning engagement on online system increased both in LMS and e-book reader. We discussed that it is caused by the following reasons: 1) Changes in major users on our online learning platform, and 2) Limited functions of our e-book reader which was developed for face-to-face learning, not online learning. Further, the results also suggested that CausalImpact model is useful for evaluating the effectiveness of a specific event from learning logs collected by learning analytics systems