Publisher：Sage Open  

The development of information and communication technologies has created limitless prospects for using augmented reality (AR) in various fields. Unfortunately, the multitasking nature of AR systems prevents learners from successfully reflecting and retaining knowledge. This study developed and designed a learning analytics dashboard (LAD) with three components: personal learning data, social comparison, and visualization of the learning process, to promote knowledge acquisition and metacognitive awareness among AR learners. A total of 31 intermediate-level Japanese learners participated in an experiment involving pre-tests, post-tests, and a delayed test to assess the LAD. Learners engaged with the AR learning system for Japanese compound verb learning and utilized the LAD to monitor and reflect on their AR learning activities. Behavioral data were analyzed using Lag Sequential Analysis (LSA), while learning performance was evaluated through one-way repeated-measures ANOVA tests. The findings indicate that the use of the LAD significantly improves learning outcomes and metacognitive processes, such as knowledge of cognition and regulation of cognition. Additionally, there were different usage patterns of the dashboard among learners, which corresponded to significant differences in their learning outcomes and changes in metacognitive awareness. Learners who primarily focused on the learning process and social comparison components of the dashboard demonstrated improved knowledge retention. Conversely, those who mainly concentrated on personal learning data experienced the most significant gains in metacognitive awareness. This study also provides crucial design insights on the integration of dashboard components with cognitive efforts to maximize learning outcomes.

DOI： 10.1177/21582440251341675

Web of Science

Scopus

Language：Japanese   Publisher：Japanese Society for Information and Systems in Education  

<p>As learning analytics research has become active both in Japan and abroad, there is a growing demand for data analysis that contributes to the improvement of learning environments that utilizes evidence based on the collection of the results of such analysis. However, in order to accumulate evidence for effective improvement of learning environment, data analysis and practical studies are needed to be utilized for improvement of learning environment. This explanatory paper reviews research on evidence-based improvement of learning environments through learning analytics and introduces examples of learning analytics research conducted by the authors to support learning and improve teaching.</p>

DOI： 10.14926/jsise.41.86

CiNii Research

Publisher：Research and Practice in Technology Enhanced Learning  

Traditional textbooks are progressively being replaced by e-book systems, which are also being utilized more commonly in K–12 education. The study investigated learning behavioral patterns in a seven-week high school mathematics course using an e-book system. In this study, learning data from the BookRoll system was analyzed with lag sequential analysis to examine learning behavioral patterns, learning strategies, and the differences between students with different performances. The results of the learning behavior patterns of all students confirmed the usage of rehearsal and elaboration strategies. However, it demonstrated the lack of using metacognitive strategies in the e-book learning process. Additionally, the results also revealed different learning patterns among students with different learning performances. Students with decreased performance tended to use shallow cognitive processing strategies, while students with increased performance used deeper learning strategies, such as integrating information from the previous and next pages to highlight learning contents. Regarding the strategy usage of students with unchanged performance, students in the unchanged low and middle performance groups tended to utilize the re-reading strategy, while students in the unchanged high performance group utilized the elaboration strategy. Notably, students with increased performance employed fewer learning behavioral patterns than decreased performance students. The behavioral patterns of students with increased performance were more efficient and effective.

DOI： 10.58459/rptel.2024.19011

Web of Science

Scopus

Publisher：Proceedings 2024 IEEE International Conference on Advanced Learning Technologies Icalt 2024  

As information technology advanced, accuracy of technology-driven assessment is being improved. In order to assess learning performance and awareness, assessment of metacognition level with technology can be useful to understand learner's learning comprehension and awareness. Metacognition is one of the most important elements for successful learning. However, current way to evaluate metacognition level focuses on psychological method such as questionnaire and interview. The recent growth of learning analytics research has demonstrated the relationships between metacognition, learning awareness, and learning behaviors. This study aims to investigate metacognitive learning behaviors using small grain data on eBook and learning analytics dashboard (LAD) over eight weeks in a university course. To do so, we determined high and low metacognitive learner groups using the Metacognitive Awareness Inventory and investigated the differences between the two groups in eBook and LAD. The findings suggest that four learning behaviors eBook and LAD were detected as metacognitive learning behaviors, and contribute to the improvement of technology-driven assessment.

DOI： 10.1109/ICALT61570.2024.00088

Web of Science

Scopus

Publisher：Sage Open  

Developed to maximize learning performance, learning analytics dashboards (LAD) are becoming increasingly commonplace in education. An LAD’s effectiveness depends on how it is used and varies according to users’ academic levels. In this study, two LADs and a learning support system were used in a higher education course to support students’ cognitive and self-regulated learning (SRL) strategies. A total of 54 students’ learning logs on three systems and their learning performance scores were collected; descriptive statistics of learning behaviors, Mann-Whitney U test, and lag sequential analysis were used to explore how students with different learning performances used LADs to support their learning. Compared to low-performers, high-performers used the LADs more frequently during preview and review phases and conducted more monitoring and reflection strategies to support their learning. Finally, some practical implications for improving the design and use of LADs were provided.

DOI： 10.1177/21582440231192151

Web of Science

Scopus