In many schools, teachers ensure that learners acquire academic knowledge and competences and promote their comprehensive psychological and social development through a process known as Guidance and Counseling (G&C). However, this process often involves dealing with difficult tasks and requires considerable effort from teachers. In recent years, as ICT tools have become more common, log data on daily use and learning log data have accumulated. These data have enabled teachers to understand leamer processes. Utilizing data across contexts is expected to deepen learners understanding, which is the basis of G&C. However, despite these needs and potential, the use of data for G&C has not yet been fully explored. Therefore, this study examines how the log data accumulated in the Goal-Oriented Active Learner (GOAL) system can be used by teachers to understand learners. Specifically, we extracted the characteristics of each learner's situation for various contexts from the log data, which can capture learning and daily routines. We then interviewed two teachers to determine how the visualized characteristics might be useful for G&C. The results suggest that the visualized characteristics are valuable for understanding learners' conditions both inside and outside of school, and these characteristics could support teachers' G&C activities, transcending specific subjects and activities.

This study investigates the transferability of Self-Direction behavior across different contexts and phases of learning using the GOAL system. Self-directed learning (SDL) is crucial for lifelong learning. It is significantly influenced by Self- Direction Skills (SDS), a meta-skill that is said to be transferable across different contexts, including the ability to identify learning needs, set goals, select strategies, and evaluate outcomes. Utilizing log data collected from Japanese junior high schools and analyzed using the iSAT system, we explored how Self-Direction behavior acquired in one context can be transferred to another and how these skills vary across the SDL phases. The results indicated that the Self-Direction behavior transferred between different activities and phases. In addition, the way of transfer is suggested to vary from phase and context. This study provides useful insights for the design and guidance of SDL support systems in educational programs. It suggests that it is important for educators to identify factors that facilitate the development and transfer of SDS.

This study investigates the relationship between students' scores in weekly tests and the unit exam. By analyzing score patterns, we found that students who consistently scored high on weekly tests performed better in the unit exam, whereas those who struggled with the unit's contents early tended to score lower. These findings emphasize the importance of using weekly test scores as formative assessments to help students monitor their progress and adjust their learning.

Recent studies have emphasized the importance of formative assessments in improving teaching and learning. This study investigated what insights can be gained from analyzing students' pen-stroke data in homework to support formative assessment (n=37). The results showed that pen-stroke data revealed students' thought processes, including partial understanding and trial-and-error attempts, which were not visible in the final answers. Regarding this result, one mathematics teacher interviewed expressed an interest in using pen-stroke data in the classroom, particularly in specific units. This study concluded that pen-stroke data can enhance formative assessment by offering more profound insights into student learning.

This study looks at learning habits of temporal regularity in learning activities. Building such habits involves learners' regulation of their behaviors and requires learning strategies for time management, which is a cornerstone of self-regulated learning (SRL). Given the importance of habit-building in education, Learning Analytics (LA) techniques have been applied to various long-term supports by monitoring learners' habitual behaviors from the trace data. However, building a learning habit does not always mean the productive use of time. Scant supports attend to recommending learners by building which habit can improve their learning productivity. Hence, this study proposes recommendations for productive learning habit-building from learning logs. We focus on the context of English reading in a Japanese junior high school and design an algorithm to compute a recommended learning time slot. Furthermore, we collect learners' perceptions of their productivity and learning status at different times of the day. The comparison between self-report and log data presents that learners are not aware of their learning as the detection from their learning logs. This implies the potential of the proposed recommendations for facilitating learners to build productive learning habits. Specifically, our study can suggest an optimal time in learning plans and provide learners with a sustainable cue to automate learning behaviors from long-term perspectives. By building productive learning habits, learners can become more engaged in their studies as well as lead more balanced lives.

In higher education, teachers sometimes urge students to apply what they have learned during class to real-life situations. However, interweaving real-world events with class activities, or the There and Back Again process, entails difficulty in relating dynamic experiences to the course contents. This study attempted to link both sides using text mining techniques on course data from a Japanese university. We extracted key phrases from four weekly assignments featuring students' real-world explorations. Then we measured the semantic similarity between each key phrase and each course content and linked the pairs with high similarity. For the linked pairs, we conducted data analyses. We also held a semi-structured interview with a course teacher regarding the interpretability of visualized data and its practical use. Consequently, we confirmed: 1) the links between the course contents and students' key phrases appeared differently in the weekly assignments, 2) notable links between specific course contents and key phrases were identified, and 3) the visualized data contained valuable insights for the teacher, but more fine-grained linking and integrated presentations were required. Despite several limitations, the results support the potential utilization of this approach for effective data-enhanced experiential learning.

Handwriting logs in the math answering process have recently been collected, and features related to the answering performance and the process, such as the stroke duration, have been investigated. However, the results reported in previous studies showed inconsistencies, and sufficient consideration had not been given to the differences in the problem types. In this study, we classified some problems into two types and verified whether there is a difference in the effect of the handwriting process on performance in each feature. The result of the analysis showed a significant difference in the effects of problems on the features used in this study, such as answering time and number of strokes. This study contributes to the need to take into consideration the problem type in learning support with handwriting process logs.

We extracted learners' characteristic answering behaviors from handwritten process log data and investigated whether learners' situations could be inferred based on these characteristics. The result showed we were able to extract several characteristic answering behaviors, such as stopped pen stroke and late start. Furthermore, we examined the learners' situation for each feature in the actual answering process. The results revealed that several characteristic answering behaviors indicated situations such as learners' stumbling or giving up. These results imply that handwritten process log data can allow teachers to capture learners' situations and support teachers' interventions.

In terms of the learning logs accumulating from real-world educational activities, techniques of learning analytics help understand the characteristics of learners' behaviors. However, extracting learning habits from log data and data-driven support for building learning habits have not yet attracted much attention. Therefore, this research proposes the approach of “Learning Habits Mining,” which aims to extract the types and stages of learning habits from learners' daily learning logs and support learners to build learning habits with data-driven methods. We identify two contributions of this research. First, this research reveals the learning habits of K12 learners and provides an approach to trace the process of building learning habits automatically. Second, this research proposes interventions to the data-driven support for building learning habits so that learners can build learning habits based on evidence derived from learning logs.

As many countries seek to promote competency-based education, formative assessments are important to capture the learning processes of learners. However, as yet there are no assessments that can fully capture the learning process. Recently, the use of ICT tools for learning has become more general, and learning log data has been accumulated. Using these data, it has become possible to capture learning processes in detail; therefore, data-driven assessment has attracted increasing attention. However, as conventional data-driven competency assessments require experts to map data to competencies, they can only be applied in a defined context. In this study, we proposed an assessment framework that allows teachers to assess their students’ competency by freely combining data collected as students used the Learning & Evidence Analytics Framework (LEAF) platform. We created an assessment in a scenario in an assumed educational setting using the proposed framework and examined what kind of assessment would be possible. Then, we created a system for the framework. Finally, interviews were conducted with three teachers regarding the system. The results suggest that the system can achieve context-independent and flexible data-driven assessment, contributing to the continuous improvement of learning and teaching from multiple perspectives in activities that use the system.

Peer evaluation of individual or group work is often adopted in team-based learning design. However, some raters may not take the evaluation process seriously and exhibit behaviors such as using the same score, rushing through evaluations, or not evaluating during the presentation. This study investigates the issue of unserious peer evaluation in group presentations, focusing on their behavior patterns. Using evaluation behavior analysis indicators, we identified unserious raters who exhibited low reliability in the peer evaluation process. Further, we conducted a preliminary analysis to detect unserious raters based on learner model data available before the peer evaluation process. This information can assist teachers in providing personalized prompts and interventions prior to the peer evaluation process, thus enhancing the evaluation quality of these students with timely prompts to them.

This study proposes a method to visualize the movement of teachers and students in a classroom using OpenPose. With the Proposed method, the activities such as sitting, joining the class late, moving, and discussing in pairs were visualized. The method has those two advantages: human movement can be visualized semi-automatically from video data and as a single image, thus saving time spent on watching long videos.

In this study, we have focused on students’ peer assessment and analyzed evaluation behavior using data from the evaluation process. Peer assessments by students are problematic in terms of reliability and validity. Many previous studies have discussed the reliability or validity of peer assessment, based on the evaluation scores of the peer assessment. However, the “Evaluation Process”, that is, who, when, and which items were evaluated in what order, has not been studied. For this issue, in this research, we have proposed to acquire the “Evaluation Process” data in peer assessment and to analyze and visualize the students’ “Evaluation Behavior”. As a preliminary result, this study identified a range of characteristic evaluation behaviors, indicating the possibility that each student might have a unique style of evaluation and that there are students who do not take evaluation seriously. We expect that we will be able to estimate the students’ motivation on the peer assessment or evaluation ability, and also to improve the design of the peer assessment form or the conditions of the peer assessment based on the “Evaluation Behavior”.

This paper focused on academic procrastination by comparing students’ self-awareness of academic procrastination with their actual learning activity. We used questionnaires to measure students’ self-awareness of academic procrastination and LMS (Learning Management System) learning histories to measure their actual learning activities. The results from these comparisons indicate that the participants who do not recognize their procrastination habits tend to actually postpone beginning assigned tasks.

This study proposes a method to visualize utterance transition in a face-to-face group discussion using mobile devices. In a group discussion, it is difficult for an instructor to monitor many groups simultaneously, and there have been some preceding studies on finding methods to monitor learners. However, these methods require special equipment. To avoid this, we proposed to utilize the microphones in learners’ existing mobile devices to acquire sound data. Our method does not require special equipment to acquire data, as the program to acquire data runs via the learner’s web browser. We conducted a preliminary experiment to visualize utterance transition in a group discussion using our programs. The result indicated that we succeeded in visualizing utterance transition. The visualization diagram indicates participants’ frequency of communication with each other.