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| Title | AI and Big Data in Education: Learning Patterns Identification and Intervention Leads to Performance Enhancement |
| Creator | 呂欣澤 Lu, Owen H.T;Yang, Stephen J.H.;Lin, Chien-Chang;Huang, Anna Y.Q.;Hou, Chia-Chen;Ogata, Hiroaki |
| Contributor | 創國學士班 |
| Key Words | learning pattern; learning analytics; self-regulated learning; intervention |
| Date | 2023-07 |
| Date Issued | 4-Oct-2024 14:13:10 (UTC+8) |
| Summary | Improving learning outcomes is always one of the key objectives of learning analytics (LA) and educational data mining (EDM). In recent years, many Massive Open Online Courses (MOOC) have been deployed and making it easier to collect learners’ data for further analysis. Naturally, leveraging AI to process such kind of big data becomes one of the main research streams to support education. In this paper, we collected data and defined student learning patterns by leveraging online courses on Python programming and we then verified if their learning performance was influenced by different learning patterns and interventions. We designed the intervention process, explored the impact of final learning outcomes, and analyze Self-Regulated Learning (SRL) abilities. From the experimental results, we share the learning outcomes and the difference in SRL with detailed explanation based on different groups. |
| Relation | Information and Technology in Education and Learning, Vol.3, No.1, pp.1-11 |
| Type | article |
| DOI | https://doi.org/10.12937/itel.3.1.Inv.p002 |
| dc.contributor | 創國學士班 | |
| dc.creator (作者) | 呂欣澤 | |
| dc.creator (作者) | Lu, Owen H.T;Yang, Stephen J.H.;Lin, Chien-Chang;Huang, Anna Y.Q.;Hou, Chia-Chen;Ogata, Hiroaki | |
| dc.date (日期) | 2023-07 | |
| dc.date.accessioned | 4-Oct-2024 14:13:10 (UTC+8) | - |
| dc.date.available | 4-Oct-2024 14:13:10 (UTC+8) | - |
| dc.date.issued (上傳時間) | 4-Oct-2024 14:13:10 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/153939 | - |
| dc.description.abstract (摘要) | Improving learning outcomes is always one of the key objectives of learning analytics (LA) and educational data mining (EDM). In recent years, many Massive Open Online Courses (MOOC) have been deployed and making it easier to collect learners’ data for further analysis. Naturally, leveraging AI to process such kind of big data becomes one of the main research streams to support education. In this paper, we collected data and defined student learning patterns by leveraging online courses on Python programming and we then verified if their learning performance was influenced by different learning patterns and interventions. We designed the intervention process, explored the impact of final learning outcomes, and analyze Self-Regulated Learning (SRL) abilities. From the experimental results, we share the learning outcomes and the difference in SRL with detailed explanation based on different groups. | |
| dc.format.extent | 106 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Information and Technology in Education and Learning, Vol.3, No.1, pp.1-11 | |
| dc.subject (關鍵詞) | learning pattern; learning analytics; self-regulated learning; intervention | |
| dc.title (題名) | AI and Big Data in Education: Learning Patterns Identification and Intervention Leads to Performance Enhancement | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.12937/itel.3.1.Inv.p002 | |
| dc.doi.uri (DOI) | https://doi.org/10.12937/itel.3.1.Inv.p002 |