This article explores future directions in addressing two key challenges for the advancement of Information and Systems in Education (iSE) research: the systematization of research questions and the development of research area maps. To address these challenges, we have used ontology engineering knowledge to construct an ontology for iSE research. This ontology centered on the learner’s learning. Based on this ontology, we created two research maps: one structured around the foundation-application axis of iSE research and the other organized by perspectives and approaches aimed at advancing the frontiers of iSE research. The article also provides concrete examples of the use of these ontologies and maps, based on existing research that has been evaluated in JSiSE. It then outlines how the maps can be used for external entry, joint research with other disciplines and as a simple textbook for young people. Finally, contribution and limitations in this project are being discussed.

This paper proposes a framework and maps for cross-boundary knowledge creation that transcends the inside and outside boundaries of the Japanese Society for Information and Systems in Education (JSiSE), facilitating intellectual exchange among all stakeholders in the field of information and systems in education. By translating the universally understood “5W1H” question framework into the context of this field, we aim to design maps that serve as a foundation for mutual understanding and collaboration. Additionally, this paper organizes and clarifies the concept of research questions, as well as how they are perceived within the field. The proposed maps and framework are intended to provide a basis for future stakeholder dialogues and discussions that emphasize the importance of research questions.

This paper provides readers with discussion concerning the organization of research questions focused on disaster education. First, the paper conducts literature surveys on all papers about disaster education published and reported since 2018 in JSiSE and on a disaster education paper that won best paper award. The analysis of the paper indicated the need to distinguish between “Winsome questions” and “Witty questions”, and between these questions and “Working questions”. In addition, the analysis cleared that two explanatory papers about disaster education published in 2018 have potential to organize research questions about disaster education. Second, the paper reviews these papers and proposes a new evacuation drill model referring to the difficulty of learning in these papers. Finally, the paper discusses the possibility of the model playing a role in organizing research questions through mapping disaster education papers in JSiSE on the model.

Drawing is one of fundamental skills for art professional education. To acquire this skill in traditional art class is very hard work for students. Therefore, the purpose of this study is to develop an educational system for acquiring drawing skills and to evaluate the effectiveness of the methodology. We have developed a drawing learning support system for beginners in drawing in a networked environment. Learners can receive advice and assessments from art experts and our system without time and/or place constraints. In this paper, firstly we briefly describe our drawing learning support system. Then we discuss the relationship between drawing work and strokes in drawing process. Based on this discussion, we show some support functions to foster improvement of number of drawing strokes of novice learners. Finally, we try to clarify the effectiveness of these functions.

Non-randomized controlled trials (non-RCTs) are easier to conduct than randomized controlled trials (RCTs), but differences in background information such as sex ratio and performance levels between the groups being compared are problematic. In this study, data from a previous non-RCT were used and adjusted using propensity scores. The differences between groups were reduced. The results suggest that non-RCTs can be made to resemble RCTs by using causal inference, and that high-quality evidence can be extracted in an easy way.

Students need to acquire competency through reflecting on their learning and applying their insights to the subsequent learning. Utilizing written reflections is expected to provide students with adaptive facilitation that aligns both with the reflection itself and with the context of the learning. However, it is difficult to provide adaptive facilitation according to the context. The purpose of this study is to provide adaptive reflection facilitation according to the learning context. Specifically, we developed a reflection support method that is adaptive to the learning context utilizing text-generative AI, along with a reflection support system that operates based on this method. Evaluation of the proposed system showed that it can generate reflection prompts composed of phrases that are appropriate for the learning context. And it was showed that providing these generated reflection prompts can help encourage students to further focus on and delve deeper into the content of their own reflection.

Small class in special needs schools allows one-on-one tutoring considered for each student’s disability. However, students who take these classes tend to be nervous because there are few classmates of the same age, in addition, if no student is available to answer questions, the teacher tends to give one-sided explanations. We developed AI-Friend to improve such difficulties in small classes in special needs schools. AI-Friend has built-in text generation AI and speech recognition functions, and handles to previously inputted prerequisites for conversation which are such as learning content and expected responses from AI-Friend. So, it can flexibly engage in conversation without being constrained by templates. We’ve analyzed the teacher’s and a student’s speech in class with and without AI-Friend. As a result, we confirmed that AI-Friend contributed to reduce monotonous question-and-answer style dialogue in the class and enabled flexible class development including chit-chat.

We have already confirmed that EBS (Error-Based Simulation) is useful for learning from errors in mechanic problems. However, it has been also known that there are learners who cannot improve their errors only with EBS. Considering that one of the reasons for the inability to correct errors is the learner’s incorrect recognition of the motion state of mechanic problems, in this study, we proposed a Structural Description Exercise (SDE) of motion state in a mechanic problem as a table. We have implemented the SDE and conducted an experiment using it with EBS. The results of the experimental use suggested that the learning effect is built up by conducting this exercise after the use of the EBS.

It is important to activate meta-learning, in which learners learn metacognitive knowledge from their own learning experiences. In this study, we address the research question, “What is the appropriate learning scheme that promotes the learning experience of deepening understanding, which is a requirement for the discovery of metacognitive knowledge to read what is not written explicitly, and leads to discovery construction of metacognitive knowledge from the experiences?”. We propose an educational program to raise awareness of meta-learning by promoting learning experiences that deepen understanding through adaptive interventions that direct learners' attention to the needs for further reading, and by promoting metacognitive knowledge construction through abstraction and generalization of their own learning experiences. Through practice of the program, we confirmed three points. First, the program promotes learning experiences of deepening understanding and encourages metacognitive knowledge constructing activities. Second, the learners make the self-evaluation of meta-learning activities stricter with self-efficacy through this program. Third, the learners’ awareness of meta-learning was maintained even after three months of the program.