Academic Journal
The learning analytics of model-based learning facilitated by a problem-solving simulation game.
| Title: | The learning analytics of model-based learning facilitated by a problem-solving simulation game. |
|---|---|
| Authors: | Wen, Cai-Ting, Chang, Chia-Jung, Chang, Ming-Hua, Fan Chiang, Shih-Hsun, Liu, Chen-Chung, Hwang, Fu-Kwun, Tsai, Chin-Chung |
| Source: | Instructional Science; Dec2018, Vol. 46 Issue 6, p847-867, 21p |
| Subject Terms: | Problem solving, Reference sources, Alternative education, Teaching methods, Educational technology |
| Abstract: | This study investigated students’ modeling progress and strategies in a problem-solving simulation game through content analysis, and through supervised and unsupervised lag sequential analysis (LSA). Multiple data sources, including self-report models and activity logs, were collected from 25 senior high school students. The results of the content analysis found that the problem-solving simulation game helped most of the students to reflectively play with the science problem and build a workable model to solve it. By using the supervised LSA, it was found that the students who successful solved the game frequently linked the game contexts with the physics terminologies, while those who did not solve the problem simply relied on the intuitive knowledge provided in the reference materials. Furthermore, the unsupervised LSA identified four activity patterns that were not noticed in the supervised LSA: the fragmented, reference material centered, reference material aided modeling, and modeling centered patterns. Each pattern has certain associations with certain problem-solving outcomes. The results of this study also shed light on the use of different analytics techniques. While the supervised LSA is particularly helpful for depicting a contrast of activity patterns between two specific student groups, the unsupervised LSA is able to identify hidden significant patterns which were not clearly distinguished in the pre-defined student groups. Researchers may find these analytics techniques useful for analyzing students’ learning processes. [ABSTRACT FROM AUTHOR] |
| Copyright of Instructional Science is the property of Springer Nature 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: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s11251-018-9461-5 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Items | – Name: Title Label: Title Group: Ti Data: The learning analytics of model-based learning facilitated by a problem-solving simulation game. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wen%2C+Cai-Ting%22">Wen, Cai-Ting</searchLink><br /><searchLink fieldCode="AR" term="%22Chang%2C+Chia-Jung%22">Chang, Chia-Jung</searchLink><br /><searchLink fieldCode="AR" term="%22Chang%2C+Ming-Hua%22">Chang, Ming-Hua</searchLink><br /><searchLink fieldCode="AR" term="%22Fan+Chiang%2C+Shih-Hsun%22">Fan Chiang, Shih-Hsun</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Chen-Chung%22">Liu, Chen-Chung</searchLink><br /><searchLink fieldCode="AR" term="%22Hwang%2C+Fu-Kwun%22">Hwang, Fu-Kwun</searchLink><br /><searchLink fieldCode="AR" term="%22Tsai%2C+Chin-Chung%22">Tsai, Chin-Chung</searchLink> – Name: TitleSource Label: Source Group: Src Data: Instructional Science; Dec2018, Vol. 46 Issue 6, p847-867, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Problem+solving%22">Problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Reference+sources%22">Reference sources</searchLink><br /><searchLink fieldCode="DE" term="%22Alternative+education%22">Alternative education</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+methods%22">Teaching methods</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study investigated students’ modeling progress and strategies in a problem-solving simulation game through content analysis, and through supervised and unsupervised lag sequential analysis (LSA). Multiple data sources, including self-report models and activity logs, were collected from 25 senior high school students. The results of the content analysis found that the problem-solving simulation game helped most of the students to reflectively play with the science problem and build a workable model to solve it. By using the supervised LSA, it was found that the students who successful solved the game frequently linked the game contexts with the physics terminologies, while those who did not solve the problem simply relied on the intuitive knowledge provided in the reference materials. Furthermore, the unsupervised LSA identified four activity patterns that were not noticed in the supervised LSA: the fragmented, reference material centered, reference material aided modeling, and modeling centered patterns. Each pattern has certain associations with certain problem-solving outcomes. The results of this study also shed light on the use of different analytics techniques. While the supervised LSA is particularly helpful for depicting a contrast of activity patterns between two specific student groups, the unsupervised LSA is able to identify hidden significant patterns which were not clearly distinguished in the pre-defined student groups. Researchers may find these analytics techniques useful for analyzing students’ learning processes. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Instructional Science is the property of Springer Nature 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: 10.1007/s11251-018-9461-5 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 847 Subjects: – SubjectFull: Problem solving Type: general – SubjectFull: Reference sources Type: general – SubjectFull: Alternative education Type: general – SubjectFull: Teaching methods Type: general – SubjectFull: Educational technology Type: general Titles: – TitleFull: The learning analytics of model-based learning facilitated by a problem-solving simulation game. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wen, Cai-Ting – PersonEntity: Name: NameFull: Chang, Chia-Jung – PersonEntity: Name: NameFull: Chang, Ming-Hua – PersonEntity: Name: NameFull: Fan Chiang, Shih-Hsun – PersonEntity: Name: NameFull: Liu, Chen-Chung – PersonEntity: Name: NameFull: Hwang, Fu-Kwun – PersonEntity: Name: NameFull: Tsai, Chin-Chung IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 00204277 Numbering: – Type: volume Value: 46 – Type: issue Value: 6 Titles: – TitleFull: Instructional Science Type: main |
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