Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game †
1
Faculty of Computer Science, University of Murcia, 30008 Murcia, Spain
2
Playful Journey Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in TEEM’20.
Academic Editor: Carina González
Sensors 2021, 21(4), 1025; https://doi.org/10.3390/s21041025
Received: 23 December 2020
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Revised: 22 January 2021
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Accepted: 26 January 2021
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Published: 3 February 2021
(This article belongs to the Special Issue Pervasive Mobile-Based Games, AR/VR and Sensors)
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 Shadowspect, 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 Shadowspect so they can better understand the process, make proper assessment, and conduct personalized interventions when appropriate.
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Keywords:
educational games; learning analytics; game-based assessment; sequence mining; visualization dashboard
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MDPI and ACS Style
Gomez, M.J.; Ruipérez-Valiente, J.A.; Martínez, P.A.; Kim, Y.J. Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game. Sensors 2021, 21, 1025. https://doi.org/10.3390/s21041025
AMA Style
Gomez MJ, Ruipérez-Valiente JA, Martínez PA, Kim YJ. Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game. Sensors. 2021; 21(4):1025. https://doi.org/10.3390/s21041025
Chicago/Turabian StyleGomez, Manuel J., José A. Ruipérez-Valiente, Pedro A. Martínez, and Yoon J. Kim. 2021. "Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game" Sensors 21, no. 4: 1025. https://doi.org/10.3390/s21041025
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