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- Publisher Website: 10.1111/jcal.12473
- Scopus: eid_2-s2.0-85088557445
- WOS: WOS:000552754700001
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Article: Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games
Title | Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games |
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Authors | |
Keywords | adaptivity game-based learning learning supports stealth assessment STEM education |
Issue Date | 2021 |
Citation | Journal of Computer Assisted Learning, 2021, v. 37, n. 1, p. 127-141 How to Cite? |
Abstract | In this study, we investigated the validity of a stealth assessment of physics understanding in an educational game, as well as the effectiveness of different game-level delivery methods and various in-game supports on learning. Using a game called Physics Playground, we randomly assigned 263 ninth- to eleventh-grade students into four groups: adaptive, linear, free choice and no-treatment control. Each condition had access to the same in-game learning supports during gameplay. Results showed that: (a) the stealth assessment estimates of physics understanding were valid—significantly correlating with the external physics test scores; (b) there was no significant effect of game-level delivery method on students' learning; and (c) physics animations were the most effective (among eight supports tested) in predicting both learning outcome and in-game performance (e.g. number of game levels solved). We included student enjoyment, gender and ethnicity in our analyses as moderators to further investigate the research questions. |
Persistent Identifier | http://hdl.handle.net/10722/318855 |
ISSN | 2023 Impact Factor: 5.1 2023 SCImago Journal Rankings: 1.842 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Shute, Valerie | - |
dc.contributor.author | Rahimi, Seyedahmad | - |
dc.contributor.author | Smith, Ginny | - |
dc.contributor.author | Ke, Fengfeng | - |
dc.contributor.author | Almond, Russell | - |
dc.contributor.author | Dai, Chih Pu | - |
dc.contributor.author | Kuba, Renata | - |
dc.contributor.author | Liu, Zhichun | - |
dc.contributor.author | Yang, Xiaotong | - |
dc.contributor.author | Sun, Chen | - |
dc.date.accessioned | 2022-10-11T12:24:43Z | - |
dc.date.available | 2022-10-11T12:24:43Z | - |
dc.date.issued | 2021 | - |
dc.identifier.citation | Journal of Computer Assisted Learning, 2021, v. 37, n. 1, p. 127-141 | - |
dc.identifier.issn | 0266-4909 | - |
dc.identifier.uri | http://hdl.handle.net/10722/318855 | - |
dc.description.abstract | In this study, we investigated the validity of a stealth assessment of physics understanding in an educational game, as well as the effectiveness of different game-level delivery methods and various in-game supports on learning. Using a game called Physics Playground, we randomly assigned 263 ninth- to eleventh-grade students into four groups: adaptive, linear, free choice and no-treatment control. Each condition had access to the same in-game learning supports during gameplay. Results showed that: (a) the stealth assessment estimates of physics understanding were valid—significantly correlating with the external physics test scores; (b) there was no significant effect of game-level delivery method on students' learning; and (c) physics animations were the most effective (among eight supports tested) in predicting both learning outcome and in-game performance (e.g. number of game levels solved). We included student enjoyment, gender and ethnicity in our analyses as moderators to further investigate the research questions. | - |
dc.language | eng | - |
dc.relation.ispartof | Journal of Computer Assisted Learning | - |
dc.subject | adaptivity | - |
dc.subject | game-based learning | - |
dc.subject | learning supports | - |
dc.subject | stealth assessment | - |
dc.subject | STEM education | - |
dc.title | Maximizing learning without sacrificing the fun: Stealth assessment, adaptivity and learning supports in educational games | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1111/jcal.12473 | - |
dc.identifier.scopus | eid_2-s2.0-85088557445 | - |
dc.identifier.volume | 37 | - |
dc.identifier.issue | 1 | - |
dc.identifier.spage | 127 | - |
dc.identifier.epage | 141 | - |
dc.identifier.eissn | 1365-2729 | - |
dc.identifier.isi | WOS:000552754700001 | - |