From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning
Abstract The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize le...
| Main Authors: | , , , , , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer US
2021
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| Online Access: | https://hdl.handle.net/1721.1/131877 |
| _version_ | 1811096476439281664 |
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| author | Lin, Jiayin Sun, Geng Cui, Tingru Shen, Jun Xu, Dongming Beydoun, Ghassan Yu, Ping Pritchard, David Li, Li Chen, Shiping |
| author2 | Massachusetts Institute of Technology. Research Laboratory of Electronics |
| author_facet | Massachusetts Institute of Technology. Research Laboratory of Electronics Lin, Jiayin Sun, Geng Cui, Tingru Shen, Jun Xu, Dongming Beydoun, Ghassan Yu, Ping Pritchard, David Li, Li Chen, Shiping |
| author_sort | Lin, Jiayin |
| collection | MIT |
| description | Abstract
The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning. |
| first_indexed | 2024-09-23T16:44:17Z |
| format | Article |
| id | mit-1721.1/131877 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T16:44:17Z |
| publishDate | 2021 |
| publisher | Springer US |
| record_format | dspace |
| spelling | mit-1721.1/1318772023-01-20T21:40:47Z From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning Lin, Jiayin Sun, Geng Cui, Tingru Shen, Jun Xu, Dongming Beydoun, Ghassan Yu, Ping Pritchard, David Li, Li Chen, Shiping Massachusetts Institute of Technology. Research Laboratory of Electronics Abstract The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning. 2021-09-20T17:30:45Z 2021-09-20T17:30:45Z 2019-10-23 2020-09-24T21:37:08Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131877 en https://doi.org/10.1007/s11280-019-00730-9 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ Springer Science+Business Media, LLC, part of Springer Nature application/pdf Springer US Springer US |
| spellingShingle | Lin, Jiayin Sun, Geng Cui, Tingru Shen, Jun Xu, Dongming Beydoun, Ghassan Yu, Ping Pritchard, David Li, Li Chen, Shiping From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning |
| title | From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning |
| title_full | From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning |
| title_fullStr | From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning |
| title_full_unstemmed | From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning |
| title_short | From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning |
| title_sort | from ideal to reality segmentation annotation and recommendation the vital trajectory of intelligent micro learning |
| url | https://hdl.handle.net/1721.1/131877 |
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