A New Resource Recommendation Method for Experiential Leaching Based on the Completion Degree of Online Learning Tasks.
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| Title: | A New Resource Recommendation Method for Experiential Leaching Based on the Completion Degree of Online Learning Tasks. |
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| Authors: | Ligang Jia1 201999800028@stu.edu.cn |
| Source: | International Journal of Emerging Technologies in Learning. 2023, Vol. 18 Issue 1, p40-54. 15p. |
| Subject Terms: | *Online education, *Experiential learning, Prediction models |
| Abstract: | Experiential Learning (ExL) is an effective way to consolidate theoretical knowledge and deepen understandings, and the recommendation of ExL resources needs to also take the effect of students' theoretical learning into consideration. However, existing studies generally ignore the stage-by-stage assessment of students' completion of online learning tasks, and the recommendation performance of existing resource recommendation models for ExL is not satisfactory enough. Therefore, the recommendation method needs to be innovated, and the interpretability of recommendation results is facing challenges. To respond to these issues, this paper studied a new resource recommendation method for ExL based on the completion degree of online learning tasks. At first, the paper gave the principle of recommending ExL resources based on the completion degree of online learning tasks, and built an online learning task completion degree prediction model. Then, this paper adopted a bi-directional GRU network model based on attention mechanism to analyze the recent online learning behavior sequence of students and attain the completion degree of students' short term learning tasks. After that, a knowledge map representing ExL resources and the correlation of knowledge attributes was drawn; by combining the completion degree of both the short-term and long-term learning tasks, the ExL resources suitable for students were recommended to them. At last, experimental results verified the effectiveness of the constructed model. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 161237995 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A New Resource Recommendation Method for Experiential Leaching Based on the Completion Degree of Online Learning Tasks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ligang+Jia%22">Ligang Jia</searchLink><relatesTo>1</relatesTo><i> 201999800028@stu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Emerging+Technologies+in+Learning%22">International Journal of Emerging Technologies in Learning</searchLink>. 2023, Vol. 18 Issue 1, p40-54. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink><br />*<searchLink fieldCode="DE" term="%22Experiential+learning%22">Experiential learning</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Experiential Learning (ExL) is an effective way to consolidate theoretical knowledge and deepen understandings, and the recommendation of ExL resources needs to also take the effect of students' theoretical learning into consideration. However, existing studies generally ignore the stage-by-stage assessment of students' completion of online learning tasks, and the recommendation performance of existing resource recommendation models for ExL is not satisfactory enough. Therefore, the recommendation method needs to be innovated, and the interpretability of recommendation results is facing challenges. To respond to these issues, this paper studied a new resource recommendation method for ExL based on the completion degree of online learning tasks. At first, the paper gave the principle of recommending ExL resources based on the completion degree of online learning tasks, and built an online learning task completion degree prediction model. Then, this paper adopted a bi-directional GRU network model based on attention mechanism to analyze the recent online learning behavior sequence of students and attain the completion degree of students' short term learning tasks. After that, a knowledge map representing ExL resources and the correlation of knowledge attributes was drawn; by combining the completion degree of both the short-term and long-term learning tasks, the ExL resources suitable for students were recommended to them. At last, experimental results verified the effectiveness of the constructed model. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Emerging Technologies in Learning is the property of International Association of Online Engineering (IAOE) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijet.v18i01.37129 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 40 Subjects: – SubjectFull: Online education Type: general – SubjectFull: Experiential learning Type: general – SubjectFull: Prediction models Type: general Titles: – TitleFull: A New Resource Recommendation Method for Experiential Leaching Based on the Completion Degree of Online Learning Tasks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ligang Jia IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 18630383 Numbering: – Type: volume Value: 18 – Type: issue Value: 1 Titles: – TitleFull: International Journal of Emerging Technologies in Learning Type: main |
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