Data mining for providing a personalized learning path in creativity: An application of decision trees
Chun Fu Lin, Department of Engineering Science and Ocean Engineering ; Yu-chu Yeh, Institute of Teacher Education ; Yu Hsin Hung, Information Techonology Office ; Ray I Chang, Department of Engineering Science and Ocean Engineering
Computers & Education Volume 68, Number 1, ISSN 0360-1315 Publisher: Elsevier Ltd
Customizing a learning environment to optimize personal learning has recently become a popular trend in e-learning. Because creativity has become an essential skill in the current e-learning epoch, this study aims to develop a personalized creativity learning system (PCLS) that is based on the data mining technique of decision trees to provide personalized learning paths for optimizing the performance of creativity. The PCLS includes a series of creativity tasks as well as a questionnaire regarding several key variables. Ninety-two college students were included in this study to examine the effectiveness of the PCLS. The experimental results show that, when the learning path suggested by a hybrid decision tree is employed, the learners have a 90% probability of obtaining an above-average creativity score, which suggests that the employed data mining technique can be a good vehicle for providing adaptive learning that is related to creativity. Moreover, the findings in this study shed light on what components should be accounted for when designing a personalized creativity learning system as well as how to integrate personalized learning and game-based learning into a creative learning program to maximize learner motivation and learning effects.
Lin, C.F., Yeh, Y.c., Hung, Y.H. & Chang, R.I. (2013). Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers & Education, 68(1), 199-210. Elsevier Ltd.
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Aditya Khamparia & Babita Pandey, Lovely Professional University, Jalandhar, India
International Journal of Web-Based Learning and Teaching Technologies Vol. 13, No. 2 (April 2018) pp. 32–45
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