Learning Analytics: A Comprehensive Analysis of Methods for Student Performance Prediction
Sakshi Sood1, Munish Saini2
1Sakshi Sood*, Dept. of Computer Engineering and Technology, Guru Nanak Dev University Amritsar, Punjab, India.
2Munish Saini, Assistant, Professor in Department of Computer Engineering and Technology Guru Nanak Dev University, Amritsar, India.
Manuscript received on November 13, 2019. | Revised Manuscript received on 22 November, 2019. | Manuscript published on December 10, 2019. | PP: 1509-1514 | Volume-9 Issue-2, December 2019. | Retrieval Number: B7217129219/2019©BEIESP | DOI: 10.35940/ijitee.B7217.129219
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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: Data Mining plays an important role in the Business world and it helps the educational institution to predict and make decisions related to the students’ academic status. From a large volume of data in educational databases it is difficult to predict student performance. In India currently the existing systems lack in monitoring and analyzing the students’ performance. The main reason is that the existing system has insufficient capabilities for identification of performance of the student and it also not considered all factors that affect the achievements of a student’s in the context of India. Therefore, a systematical literature review on predicting student performance by the proposed system is a web-based which makes use of the mining techniques for the extraction of useful information. This work is digging insight into the state-based and event based approaches for predicting student performance. A Comparative analysis is conducted to suggest regression-based algorithms of state-based framework lack accuracy and correlation-based algorithms under the event-driven approach outperform classical regression algorithms. It is also concluded from pedagogical a point of view, higher engagement with social media leads to higher final grades.
Keywords: Performance Prediction, Learning Analytics, Regression Algorithm, Correlation Algorithms, Social Media.
Scope of the Article: Algorithm Engineering