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dc.contributor.author | Yang, Yuanyuan | en |
dc.contributor.author | Majumdar, Rwitajit | en |
dc.contributor.author | Li, Huiyong | en |
dc.contributor.author | Akçapinar, Gökhan | en |
dc.contributor.author | Flanagan, Brendan | en |
dc.contributor.author | Ogata, Hiroaki | en |
dc.contributor.alternative | 楊, 媛媛 | ja |
dc.contributor.alternative | 李, 慧勇 | ja |
dc.contributor.alternative | 緒方, 広明 | ja |
dc.date.accessioned | 2022-09-30T08:08:14Z | - |
dc.date.available | 2022-09-30T08:08:14Z | - |
dc.date.issued | 2021 | - |
dc.identifier.uri | http://hdl.handle.net/2433/276417 | - |
dc.description.abstract | Self-direction skill is considered a vital skill for twenty-first-century learners in both the learning context and physical activity context. Analysis skill for self-directed activities requires the students to analyze their own activity data for understanding their status in that activity. It is an important phase that determines whether an appropriate plan can be set or not. This research presents a framework designed to foster students’ analysis skill in self-directed activities, which aims (1) to build a technology-enabled learning system allowing students to practice analyzing data from their own daily contexts, (2) to propose an approach to model student’s analysis skill acquisition level and process, and (3) to provide automated support and feedback for analysis skill development tasks. The analysis module based on the proposed framework was implemented in the GOAL system which synchronized data from learners’ physical and reading activities. A study was conducted with 51 undergraduate students to find reliable indicators for the model to then measure students’ analysis skills. By further analyzing students’ actual usage of the GOAL system, we found the actual activity levels and their preferences regarding analysis varied for the chosen contexts (learning and physical activity). The different context preference groups were almost equal, highlighting the utility of a system that integrates data from multiple contexts. Such a system can potentially respond to students’ individual preferences to execute and acquire self-direction skill. | en |
dc.language.iso | eng | - |
dc.publisher | Springer Nature | en |
dc.rights | © The Author(s). 2021 | en |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | Data analysis | en |
dc.subject | Self-directed learning | en |
dc.subject | Self-direction skill | en |
dc.subject | Skill measurement | en |
dc.subject | Automated measurement | en |
dc.subject | Student modeling | en |
dc.subject | User interface design | en |
dc.subject | Adaptive support | en |
dc.title | A framework to foster analysis skill for self-directed activities in data-rich environment | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | Research and Practice in Technology Enhanced Learning | en |
dc.identifier.volume | 16 | - |
dc.relation.doi | 10.1186/s41039-021-00170-y | - |
dc.textversion | publisher | - |
dc.identifier.artnum | 22 | - |
dcterms.accessRights | open access | - |
datacite.awardNumber | 16H06304 | - |
datacite.awardNumber | 20K20131 | - |
datacite.awardNumber | 20H01722 | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-16H06304/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-20K20131/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-20H01722/ | - |
dc.identifier.eissn | 1793-7078 | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.awardTitle | 教育ビッグデータを用いた教育・学習支援のためのクラウド情報基盤の研究 | ja |
jpcoar.awardTitle | GOAL Project: SMART AI Support with Student's Learning and Wellbeing Data | en |
jpcoar.awardTitle | Knowledge-Aware Learning Analytics Infrastructure to Support Smart Education and Learning | en |
出現コレクション: | 学術雑誌掲載論文等 |
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