Does Learning Style Predict Academic Performance of Engineering and Technology Students in India?
Thaddeus Alfonso1, Sharon Sophia2
1Thaddeus Alfonso, VIT Business School, VIT University, Chennai, India.
2Dr. Sharon Sophia, VIT Business School, VIT University, Chennai, India. 
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 167-175 | Volume-8 Issue-4, November 2019. | Retrieval Number: C6596098319/2019©BEIESP | DOI: 10.35940/ijrte.C6596.118419

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Abstract: Learning styles have been associated with academic performance of engineering and technology students. The aim of the present study was to identify the learning styles of students and investigating the relationship between learning style subscale scores and academic performance, and thereby determining whether the learning styles predict the academic performance of engineering and technology students in India. We used the Index of Learning Styles (ILS) to determine the learning styles of students and used Cumulative Grade Point Average (CGPA) for academic performance. The Pearson correlation, ANOVA and stepwise regression tests were used to find the correlation between academic performance and learning styles, the difference between the academic performance of groups and to identify the predictors of academic performance respectively. The most strongly preferred learning style was sequential (75.1%). The academic performance had a significant positive correlation with age and four learning styles namely sequential, sensing, visual and active (p=0.000). The sequential learning style was the powerful predictor of academic performance of students in comparison to other learning styles (p=0.000). Studying methods of students and teaching approaches of faculty consistent with the sequential and sensing learning styles may increase the academic performance among students enrolled for engineering and technology degree course in the higher education institutions in India.
Keywords: Learning Style, Academic Performance, Students, Engineering and Technology.
Scope of the Article: Deep Learning.