Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game
Games have become one of the most popular activities across cultures and ages. There is ample evidence that supports the benefits of using games for learning and assessment. However, incorporating game activities as part of the curriculum in schools remains limited. Some of the barriers for broader...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2021
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| Online Access: | https://hdl.handle.net/1721.1/131340 |
| _version_ | 1826198402605514752 |
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| author | Gomez, Manuel J. Ruipérez-Valiente, José A. Martínez, Pedro A. Kim, Yoon Jeon |
| author2 | MIT Open Learning |
| author_facet | MIT Open Learning Gomez, Manuel J. Ruipérez-Valiente, José A. Martínez, Pedro A. Kim, Yoon Jeon |
| author_sort | Gomez, Manuel J. |
| collection | MIT |
| description | Games have become one of the most popular activities across cultures and ages. There is ample evidence that supports the benefits of using games for learning and assessment. However, incorporating game activities as part of the curriculum in schools remains limited. Some of the barriers for broader adoption in classrooms is the lack of actionable assessment data, the fact that teachers often do not have a clear sense of how students are interacting with the game, and it is unclear if the gameplay is leading to productive learning. To address this gap, we seek to provide sequence and process mining metrics to teachers that are easily interpretable and actionable. More specifically, we build our work on top of <i>Shadowspect</i>, a three-dimensional geometry game that has been developed to measure geometry skills as well other cognitive and noncognitive skills. We use data from its implementation across schools in the U.S. to implement two sequence and process mining metrics in an interactive dashboard for teachers. The final objective is to facilitate that teachers can understand the sequence of actions and common errors of students using <i>Shadowspect</i> so they can better understand the process, make proper assessment, and conduct personalized interventions when appropriate. |
| first_indexed | 2024-09-23T11:04:19Z |
| format | Article |
| id | mit-1721.1/131340 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:04:19Z |
| publishDate | 2021 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1313402023-02-23T21:08:57Z Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game Gomez, Manuel J. Ruipérez-Valiente, José A. Martínez, Pedro A. Kim, Yoon Jeon MIT Open Learning Games have become one of the most popular activities across cultures and ages. There is ample evidence that supports the benefits of using games for learning and assessment. However, incorporating game activities as part of the curriculum in schools remains limited. Some of the barriers for broader adoption in classrooms is the lack of actionable assessment data, the fact that teachers often do not have a clear sense of how students are interacting with the game, and it is unclear if the gameplay is leading to productive learning. To address this gap, we seek to provide sequence and process mining metrics to teachers that are easily interpretable and actionable. More specifically, we build our work on top of <i>Shadowspect</i>, a three-dimensional geometry game that has been developed to measure geometry skills as well other cognitive and noncognitive skills. We use data from its implementation across schools in the U.S. to implement two sequence and process mining metrics in an interactive dashboard for teachers. The final objective is to facilitate that teachers can understand the sequence of actions and common errors of students using <i>Shadowspect</i> so they can better understand the process, make proper assessment, and conduct personalized interventions when appropriate. 2021-09-20T14:16:17Z 2021-09-20T14:16:17Z 2021-02-03 2021-02-05T14:15:48Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/131340 Sensors 21 (4): 1025 (2021) PUBLISHER_CC http://dx.doi.org/10.3390/s21041025 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Gomez, Manuel J. Ruipérez-Valiente, José A. Martínez, Pedro A. Kim, Yoon Jeon Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game |
| title | Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game |
| title_full | Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game |
| title_fullStr | Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game |
| title_full_unstemmed | Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game |
| title_short | Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game |
| title_sort | applying learning analytics to detect sequences of actions and common errors in a geometry game |
| url | https://hdl.handle.net/1721.1/131340 |
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