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This paper explores co-design in Japanese education for deploying data-driven educational technology and practice. Although there is a growing emphasis on data to inform educational decision-making and personalize learning experiences, challenges such as data interoperability and inconsistency with teaching goals prevent practitioners from participating. Co-design, characterized by involving various stakeholders, is instrumental in addressing the evolving needs of technology deployment. Japan's educational context aligns with co-design implementation, with a learning and evidence analytics infrastructure facilitating data collection and analysis. From the Japanese co-design practice of educational technologies, the paper highlights a 6-phase co-design framework: motivate, pilot, implement, refine, evaluate, and maintain. The practices focus on data-driven learning strategies, technology interventions, and across-context dashboards, covering assorted learning contexts in Japan. By advocating for a co-design culture and data-driven approaches to enhance education in Japan, we offer insights for education practitioners, policymakers, researchers, and industry developers.

One trending theme within research on learning and teaching is an emphasis on artificial intelligence (AI). While AI offers opportunities in the educational arena, blindly replacing human involvement is not the answer. Instead, current research suggests that the key lies in harnessing the strengths of both humans and AI to create a more effective and beneficial learning and teaching experience. Thus, the importance of 'humans in the loop' is becoming a central tenet of educational AI. As AI technology advances at breakneck speed, every area of society, including education, needs to engage with and explore the implications of this phenomenon. Therefore, this paper aims to assist in this process by examining the impact of AI on education from researchers' and practitioners' perspectives. The authors conducted a Delphi study involving a survey administered to N = 33 international professionals followed by in-depth face-to-face discussions with a panel of international researchers to identify key trends and challenges for deploying AI in education. The results indicate that the three most important and impactful trends were (1) privacy and ethical use of AI; (2) the importance of trustworthy algorithms; and (3) equity and fairness. Unsurprisingly, these were also identified as the three key challenges. Based on these findings, the paper outlines policy recommendations for AI in education and suggests a research agenda for closing identified research gaps.

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.

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.

Self-directed learning (SDL) requires students to take initiative to learn and control their own learning process. Literature highlights the importance of SDL for lifelong learning. Yet, little understanding is known regarding how to support SDL at the school level, specifically for out-of-class learning context. To fill up this gap, this research developed a learning dashboard and integrated SDL process management in GOAL system. It was implemented to provide support for out-of-class online self-directed extensive reading (SDER) at a high school level. A two-group study conducted during a three-week spring vacation found the experimental group (N = 35, with SDL support) achieved significantly more reading outcomes than the control group (N = 12, without SDL support). Detailed GOAL interaction behavior analysis of the experimental group showed that viewing learning dashboard was significantly correlated with reading outcomes as well as interactions related to SDL process management. These findings highlights positive effect of SDL support in GOAL on students' out-of-class SDER outcomes as well as their SDL behaviors. The study provided implications for research related to extensive reading and SDL support for out-of-class learning.

In-class group work activities are found to promote the interpersonal skills of learners. To support the teachers in facilitating such activities, we designed a learning analytics-enhanced technology framework, Group Learning Orchestration Based on Evidence (GLOBE) using data-driven approaches. In this study, we implemented the algorithmic group formation and group work evaluation systems in a Japanese junior high school context. Data from a series of 12 collaborative learning activities were used to validate the difference in the measured heterogeneity of the formed homogeneous and heterogeneous groups compared to random grouping. Further, the peer rating and self-perception of the group work were compared for comparative reading and idea exchange tasks. We found algorithmically formed groups, considering the learner model data, either heterogeneously or homogeneously performed better than random grouping. Specifically, students in groups created by the homogeneous algorithm received higher peer ratings and more positive self-perception of group work in the idea exchange group tasks. We did not find significant differences in the comparative reading tasks. Along with the empirical findings, this work presents a paradigm of continuous data-driven group learning support by incorporating the peer and teacher evaluation scores as an input to the subsequent algorithmic grouping.

Data-driven platforms with rich data and learning analytics applications provide immense opportunities to support collaborative learning such as algorithmic group formation systems based on learning logs. However, teachers can still get overwhelmed since they have to manually set the parameters to create groups and it takes time to understand the meaning of each indicator. Therefore, it is imperative to explore predictive indicators for algorithmic group formation to release teachers from the dilemma with explainable group formation indicators and recommended settings based on group work purposes. Employing learning logs of group work from a reading-based university course, this study examines how learner indicators from different dimensions before the group work connect to the subsequent group work processes and consequences attributes through correlation analysis. Results find that the reading engagement and previous peer ratings can reveal individual achievement of the group work, and a homogeneous grouping strategy based on reading annotations and previous group work experience can predict desirable group performance for this learning context. In addition, it also proposes the potential of automatic group formation with recommended parameter settings that leverage the results of predictive indicators.