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.

1. [1] R. M. Felder and R. Brent, “Understanding Student Differences,” J. of Engineering Education, Vol.94, No.1, pp. 57-72, 2005.
2. [2] S. Graf, T. C. Liu, N. S. Chen, et al., “Learning Styles and Cognitive Traits-Their Relationship and Its Benefits in Web-based Educational Systems,” Computers in Human Behavior, Vol.25, No.6, pp. 1280-1289, 2009.
3. [3] E. Guyton, “Social Justice in Teacher Education,” The Educational Forum, Vol.64, No.2, pp. 108-114, 2000.
4. [4] R. G. Bringle and J. A. Hatcher, “Reflection in Service Learning: Making Meaning or Experience,” Educational Horizons, pp. 179-185, 1999.
5. [5] Y. C. Chang, W. Y. Kao, C. P. Chu, et al., “A Learning Style Classification Mechanism for E-learning,” Computers & Education, Vol.53, No.2, pp. 273-285, 2009.
6. [6] D. Anitha, C. Deisy, S. B. Lakshmi, et al., “Proposing a Classification Methodology to Reduce Learning Style Combinations for Better Teaching and Learning,” Technology for Education (T4E), 2014 IEEE Sixth Int. Conf. on. IEEE, pp. 208-211, 2014.
7. [7] D. Anitha and C. Deisy, “Proposing a Novel Approach for Classification and Sequencing of Learning Objects in E-learning Systems Based on Learning Style,” J. of Intelligent & Fuzzy Systems, Vol.29, No.2, pp. 539-552, 2015.
8. [8] L. J. Deborah, R. Baskaran, and A. Kannan, “Learning Styles Assessment and Theoretical Origin in an E-learning Scenario,” Artificial Intelligence Review, Vol.42, No.4, pp. 801-819, 2014.
9. [9] G. B. Ronsivalle and M. Conte, “A Model for the Evaluation of Learning Style Design Effectiveness,” Proc. the 4th Int. Conf. on Virtual Learning ICVL, pp. 70-77, 2009.
10. [10] H. M. Truong, “Integrating Learning Styles and Adaptive E-learning System: Current Developments, Problems and Opportunities,” Computers in Human Behavior, Vol.55, pp. 1185-1193, 2015.
11. [11] C. Manolis, D. J. Burns, R. Assudani, et al., “Assessing Experiential Learning Styles: A Methodological Reconstruction and Validation of the Kolb Learning Style Inventory,” Learning and Individual Differences, Vol.23, pp. 44-52, 2013.
12. [12] C. Ipbuker, “Learning Styles and Teaching Models in Engineering Education,” Proc. of the 6th WSEAS Int. Conf. on Engineering Education, pp. 104-107, 2009.
13. [13] S. Graf and T. C. Liu, “Supporting Teachers in Identifying Students’ Learning Styles in Learning Management Systems: An Automatic Student Modelling Approach,” Educational Technology & Society, Vol.12, No.4, pp. 3-14, 2009.
14. [14] E. ddotOzpolat and G. B. Akar, “Automatic Detection of Learning Styles for an E-learning System,” Computers & Education, Vol.53, No.2, pp. 355-367, 2009.
15. [15] L. Ling, Y. You, and M. Yan, “Research on Detecting Learning Style Based on TAN Bayesian Network,” Computer Engineering and Applications, Vol.51, No.6, pp. 48-54, 2015.