Dynamic Difficulty Adjustment through a Learning Analytics Model in a Casual Serious Game for Computer Programming Learning

Vahldick, Adilson and Jose Mendes, Antonio Jose and Jose Marcelino, Maria Jose (2017) Dynamic Difficulty Adjustment through a Learning Analytics Model in a Casual Serious Game for Computer Programming Learning. EAI Endorsed Transactions on Game-Based Learning, 4 (13). e1. ISSN 2034-8800

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Abstract

Teachers have used games as a support tool to engage students in learning tasks. As they often record student’s performance as learning progresses, it is interesting and useful to discuss how that information can be used to assess learning and to improve the learning experience. For instance, teachers can use that information to give personalized attention in classes and the game can use it to provide challenges of the “right” difficulty. In computer programming learning, games can provide an alternative way to introduce concepts and, mainly, to practice them. This paper proposes a model to identify the students’ progress considering their performance in programming tasks. The model is demonstrated by an implementation in a casual computer programming serious game. We illustrate how this game could use this model to personalize its challenges.

Item Type: Article
Uncontrolled Keywords: Novice programmers, learning analytics, dynamic difficulty adjustment, fuzzy systems.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Depositing User: EAI Editor IV
Date Deposited: 09 Jul 2021 08:34
Last Modified: 09 Jul 2021 08:34
URI: https://eprints.eudl.eu/id/eprint/4430

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