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In inclusive education, students with different needs learn in the same context. With the advancement of artificial intelligence (AI) technologies, it is expected that they will contribute further to an inclusive learning environment that meets the individual needs of diverse learners. However, in Japan, we did not find any studies exploring current needs in an actual special needs context. In this study, we used the learning and evidence analysis framework (LEAF) as a learning analytics-enhanced learning environment and employed Active Reading as an example learning task to investigate the challenges and possibilities of applying AI to inclusive education in the future. Two students who attended a resource room formed the context. We investigated learning logs in the LEAF system while each student executed a given learning task. We detected specific learning behaviors from the logs and explored the challenges and future potential of learning with AI technology, considering human involvement in orchestrating inclusive educational practices.

Self-direction skill (SDS) is an essential 21st-century skill that can help learners be independent and organized in their quest for knowledge acquisition. While some studies considered learners from higher education levels as the target audience, providing opportunities to start the SDS practice by K12 learners is still rare. Further, practicing such skills requires a concrete context and scaffolding during the skill acquisition. This article introduces the Goal Oriented Active Learner (GOAL) system that facilitates SDS acquisition in learners utilizing daily activities as context. The GOAL architecture integrates learning logs from online environments and physical activity logs from wearable trackers to provide a data-rich environment for the learners to acquire and practice their SDS. The GOAL users follow DAPER, a five-phase process model, to utilize the affordances in the system while practicing SDS. We implemented the GOAL system at a K12 public institution in Japan in 2019. Learners used the online environments for extensive reading and smartwatches for tracking walking and sleeping activities. This study analyzes detailed interaction patterns in GOAL while learners planned and monitored their self-directed actions. The results illustrate the strategies for DAPER behaviors that emerge in different activity contexts. We discuss the potentials and challenges of this technology ecosystem that connects learners' learning logs and physical activity logs, specifically in the K12 context in Japan and, more generally, from the learning analytics research perspective to provide a context to practice SDS.

We aim to identify the engagement strategies that higher education students, engaging in emergency online learning in low-resource settings, perceive to be effective. We conducted a sequential mixed-methods study based on Moore’s interaction framework for distance education. We administered a questionnaire to 313 students engaging in emergency online learning in low-resource settings to examine their perceptions of different engagement strategies. Our results showed that student–content engagement strategies, e.g., screen sharing, summaries, and class recordings, are perceived as the most effective, closely followed by student–teacher strategies, e.g., Q and A sessions and reminders. Student–student strategies, e.g., group chat and collaborative work, are perceived as the least effective. The perceived effectiveness of engagement strategies varies based on the students’ gender and technology access. To support instructors, instructional designers, and researchers, we propose a 10-level guide for engaging students during emergency online classes in low-resource settings.

Collaborative learning in the form of group work is becoming increasingly significant in education since interpersonal skills count in modern society. However, teachers often get overwhelmed by the logistics involved in conducting any group work. Valid support for executing and managing such activities in a timely and informed manner becomes imperative. This research introduces an intelligent system focusing on group formation which consists of a parameter setting module and the group member visualization panel where the results of the created group are shown to the user and can be graded. The system supports teachers by applying algorithms to actual learning log data thereby simplifying the group formation process and saving time for them. A pilot study in a primary school mathematics class proved to have a positive effect on students’ engagement and affections while participating in group activities based on the system-generated groups, thus providing empirical evidence to the practice of Computer-Supported Collaborative Learning (CSCL) systems.

Peer feedback can be described as the act of one learner evaluating the performance of another learner. It has been shown to impact student learning and achievement in language learning contexts positively. It is a skill that can be trained, and there have been calls for research on peer feedback training. Mobile microlearning is a type of technology-enhanced learning which is notable for its short duration and flexibility in the time and place of learning. This study aims to evaluate how an asynchronous microlearning app might improve students’ skills for providing peer feedback on spoken content in the context of English as a foreign language (EFL) education. This study used convenience sampling and a quasi-experimental single-group pre-/post- research design. Japanese university students ( n  = 87) in an EFL course used the Pebasco asynchronous microlearning app to practice peer feedback skills. The students’ app usage data were used to identify five behavioral profiles. The pattern of profile migration over the course of using Pebasco indicates that many participants improved or maintained desirable patterns of behavior and outcomes, suggesting a positive impact on the quality of peer feedback skills and second-language (L2) skills, as well as the ability to detect L2 errors. The findings also suggest improvements that can be made in future design iterations. This research is novel because of a current lack of research on the use of no-code technology to develop educational apps, particularly in the context of microlearning for improving peer feedback skills in EFL.

This study focuses on the problem that the process of building learning habits has not been clearly described. Therefore, we aim to extract the stages of learning habits from log data. We propose a data model to extract stages of learning habits based on the transtheoretical model and apply the model to the learning logs of self-directed extensive reading to demonstrate the process of building learning habits. We uncover the various stages (i.e., the precontemplation, contemplation, preparation, action, and maintenance stage) that learners underwent, and different proportions of the maximum stage that they achieved during an 11-month, self-directed, extensive reading program-implemented in a Japanese junior high school. This study contributes to realizing a method to evaluate the learning process, by tracing the stages of learning habits in long-term, and continuous learning activities. Further, this study can help guide the development of evidence-based educational interventions to support the building of lifelong learning habits and self-directed learning, using data-driven methods.

This study investigates the impact of using the Pebasco app, a mobile microlearning tool, to train peer feedback skills within a mandatory university English as a Foreign Language (EFL) course employing a communicative language teaching (CLT) approach. Building on previous research, this study aims to assess how the app influences students' ability to transfer peer feedback training to a constructive CLT speaking activity. Utilizing a quasi-experimental pre-/post- design with 89 participants (n = 89), data were collected through the Pebasco app, Flipgrid videos, and end-of-class surveys. Key findings indicate that students who engaged with Pebasco reported significant self-perceived improvements in both peer and internal feedback skills. Notably, students with initially lower performance on the constructive CLT speaking activity showed substantial progress, achieving parity with their initially higher-performing peers, suggesting the app's effectiveness in bridging performance gaps. This research underscores the potential of mobile microlearning apps to enhance peer feedback skills and promote the transfer of learning to related communicative tasks, providing valuable insights for language education.

Digitized learning materials are a core part of modern education, and analysis of the use can offer insight into the learning behavior of high and low performing students. The topic of predicting student characteristics has gained a lot of attention in recent years, with applications ranging from affect to performance and at-risk student prediction. In this paper, we examine students reading behavior using a digital textbook system while taking an open-book test from the perspective of engagement and performance to identify the strategies that are used. We create models to predict the performance and engagement of learners before the start of the assessment and extract reading behavior characteristics employed before and after the start of the assessment in a higher education setting. It was found that strategies, such as: revising and previewing are indicators of how a learner will perform in an open ebook assessment. Low performing students take advantage of the open ebook policy of the assessment and employ a strategy of searching for information during the assessment. Also compared to performance, the prediction of overall engagement has a higher accuracy, and therefore could be more appropriate for identifying intervention candidates as an early-warning intervention system.

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.

Self-directed learning requires students to take the initiative to learn and control their learning process. Scholars have provided evidence for the positive effect of self-directed learning on academic achievement, and they emphasize the importance of self-direction for lifelong learning. Yet, a limited study has been conducted in K-12 education. Additionally, most research investigated students’ perceived competence in self-directed learning, but little has studied students’ behavior in self-direction practice. To fill the gap, we propose a technology-based approach to facilitate self-direction practice in the K-12 context. A GOAL system was developed to support several self-direction tasks: conduct Collection-Reflection step with self-data and Analysis-Evaluation step with accumulated self-data from two learning aspects of learning outcome and learning effort. We explored their behavioral patterns based on the interaction logs generated by 122 EFL high school students while performing those tasks across one semester. Four clusters were found to have different behavioral patterns in those tasks. Further analysis suggested a positive correlation between self-direction behavior and academic achievement. And we found that students who not only collected and reflected on self-data but also analyzed and evaluated their learning status got higher academic achievement than those who only collected and reflected on self-data or rarely engaged in any tasks. This study contributes to a technology-based approach that helps to track students’ self-direction behavior. This study extends self-direction research to the K-12 context and provides evidence for the positive impact of self-directed learning on academic achievement from a new perspective.