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JACIII Vol.20 No.7 pp. 1141-1146
doi: 10.20965/jaciii.2016.p1141
(2016)

Paper:

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Using Data Mining on Students’ Learning Features: A Clustering Approach for Student Classification

Xiaolan Zhou*, Jianqi An**,†, Xin Zhao**, and Yuanxing Dong*

*School of Foreign Languages, China University of Geosciences
Wuhan 430074, China

**School of Automation, China University of Geosciences
Wuhan 430074, China

†Corresponding author

Received:
July 5, 2016
Accepted:
October 1, 2016
Published:
December 20, 2016
Creative Commons License
Keywords:
data mining, learning features, student classification, PCA, Ward clustering
Abstract
Students have different levels of motivation, approaches to learning, and intellectual levels. The better that instructors understand these differences, the better the chances they have of improving their quality of teaching. To explore differences thoroughly, we focuses on three crucial factors in student learning features – i.e., personality, learning style and multiple intelligences – and propose an approach effective in classifying students for the purpose of instructing instructors while optimizing their teaching process. We collected data on learning features from a class of 58 college students and analyzed these data by using principal component analysis (PCA) and then classified them using Ward clustering. Results of experiments indicate that our proposal effectively classifies students based on their learning features and that classification results facilitate instructors in creating personalized teaching strategies.
Cite this article as:
X. Zhou, J. An, X. Zhao, and Y. Dong, “Using Data Mining on Students’ Learning Features: A Clustering Approach for Student Classification,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.7, pp. 1141-1146, 2016.
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