Predicting Student Performance using Classification and Regression Trees Algorithm
M Krishna1, Bandlamudi S B P Rani2, G Kalyan Chakravarthi3, B Madhavrao4, S M B Chowdary5
1Dr. M Krishna*, Professor, CSE Department, Sir Cattamanchi Ramalinga Reddy College of Engg, Vatluru, Andhra Pradesh.
2Bandlamudi S B P Rani, Asst. Professor in the Department of CSE, Sir Cattamanchi Ramalinga Reddy College of Engg, Vatluru, Andhra Pradesh.
3G Kalyan Chakravarthi, Asst. Professor in CSE Department, GVPCDPGC, Visakhapatnam, Andhra Pradesh.
4B Madhavrao, Asst. Professor in the CSE Department, Sir Cattamanchi Ramalinga Reddy College of Engg, Vatluru, Andhra Pradesh.
5S M B Chowdary, Asst. Professor in the Department of CSE, Sir Cattamanchi Ramalinga Reddy College of Engg, Vatluru, Andhra Pradesh
Manuscript received on December 14, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 3349-3356 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8964019320/2020©BEIESP | DOI: 10.35940/ijitee.C8964.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Now a days Internet and Web technologies providing students opportunities for flexible interactivity with study materials, peers and instructors. And also generating large amounts of usage data that can be processed and reveal behavioral patterns of study and learning. In this paper, to predict course performance we extracted data from a Moodlebased blended learning course and build a student model. Classification and Regression Trees (CART) decision tree algorithm was used to classify students and predict those at risk, based on the impact of four online activities: message exchanging, group wiki content creation, course files opening and online quiz taking. The correct classifications in results prove that the model is sensitive to categorize very specific groups at risk.
Keywords: Education Data Mining,
Scope of the Article: Classification