Search

Jigsaw learning is one of the popular cooperative learning methods which has been utilized in many English as Foreign Language (EFL) classrooms. In recent years, utilization of technology in education has accelerated. In this study, Jigsaw+, a new jigsaw learning approach with an e-book reader, was proposed to explore how the learning design can be applied for English vocabulary acquisition, reading comprehension, and students’ engagement in group activities. A quasi-experimental study was conducted at a high school in Japan for advanced and standard class students. Participants read and comprehend a story in English through BookRoll, an e-book platform with an analysis tool embedded. Two research questions were investigated; 1) to what extent did Jigsaw+ affect students’ English vocabulary knowledge and reading comprehension? and 2) to what extent did Jigsaw+ promote students’ reading engagement in the jigsaw group activities? The results revealed that Jigsaw+ learning tasks assisted to improve students’ vocabulary and reading comprehension for both the advanced and standard groups. Moreover, it facilitated students’ engagements in activities. Some limitations of the study are also discussed.

Self-regulation in learning involves planning and utilizing different shared resources. This study investigates learning strategies of different student groups when they are accessing course materials in digital medium – one group is the students with high academic performance, the other is the students with low academic performance. We analyze data of 116 students from a mathematics course in a junior high school. Using the differential pattern mining technique, we highlight underlying course content accessing patterns from the learning log collected by an e-book system, BookRoll.

In this paper, we propose an interactive drawing based activity in BookRoll, an e-book-based teaching-learning platform. Participants who were researchers drew an overview of their research as a handwritten memo. We demonstrate the affordances of the learning analytics (LA) enhanced platform to facilitate practice and research of such a drawing activity focused on a higher-order thinking task to organize knowledge (research overview in this case). We share the multi-modal data of 4 participants to illustrate the outcomes of the activity and their perception of the activity. An initial analysis of the handwritten memo is visualised. Further reflections show how the use of learning analytics-based platforms can enrich the learning design of a knowledge organisation task.

In recent years, machine learning of increasing complexity is being applied to problems in education. However, there is an increasing call for transparency and understanding into how the results of complex models are derived, leading to explainable AI gaining attention. The application of machine learning to automated group formation for collaborative work from learning system logs and other data has been progressing. Building on previous research in this field, we propose a group formation method that is based on a combination of course knowledge structures, reading behavior, and assessment analysis to create optimal heterogenous and homogeneous working groups using a genetic algorithm. The characteristics of each group are presented for explanation as a visualized knowledge map showing the strengths and weaknesses of each group, and are in the structure form of curriculum. We also present a case study of applying the method to junior high school mathematics log data, and provide explanation in a visualized form of standardized curriculum of group characteristics that are often referenced for learning design by teachers.