Adapting gamified learning systems using educational data mining techniques

Citation:
Daghestani, L. F., L. F. Ibrahim, R. S. Al-Towirgi, and H. A. Salman, "Adapting gamified learning systems using educational data mining techniques", Computer Applications in Engineering EducationComputer Applications in Engineering Education, vol. 28, issue 3: John Wiley & Sons, Ltd, pp. 568 - 589, 2020.

Abstract:

Abstract Artificial intelligence (AI) provides opportunities to improve the effectiveness of e-learning by increasing students' engagement. Adaptive e-learning uses AI to support individual learners by responding to their different learning needs which can be determined by analyzing their navigation history of e-learning systems using data mining methods. Educational data mining (EDM) discovers new patterns of learning and teaching to facilitate the process of decision-making to serve education improvement. Gamification is another way of increasing students' engagement by using game elements in a nongame context. In this paper, the gamification technique and EDM methods were used in combination with adaptive learning to increase the students' engagement and learning performance. An adaptive gamified learning system (AGLS) was developed which combines gamification, classification, and adaptation techniques to increase the effectiveness of e-learning. This paper studies the impact of gamification and adaptive gamification on the effectiveness of e-learning through increasing students' engagement and learning performance. AGLS was applied to the data structure course. Results showed that adaptive gamification has a positive effect on students' engagement and learning performance compared to just gamification.

Notes:

doi: 10.1002/cae.22227

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