Predicting Students’ Final Grade in Mathematics Module using Multiple Linear Regression
Hazlina Darman1, Sarah Musa2, Rajasegeran Ramasamy3, Raja Rajeswari4
1Hazlina Darman, School of Actuarial Science, Mathematics, and Qualitative Study, Asia Pacific University of Technology and Innovation, Malaysia.
2Sarah Musa, School of Actuarial Science, Mathematics, and Qualitative Study, Asia Pacific University of Technology and Innovation, Malaysia.
3Rajasegeran Ramasamy, School of Actuarial Science, Mathematics, and Qualitative Study, Asia Pacific University of Technology and Innovation, Malaysia.
4Raja Rajeswari, School of Actuarial Science, Mathematics, and Qualitative Study, Asia Pacific University of Technology and Innovation, Malaysia.
Manuscript received on 06 February 2019 | Revised Manuscript received on 12 February 2019 | Manuscript Published on 19 February 2019 | PP: 331-335 | Volume-7 Issue-5S January 2019 | Retrieval Number: ES2162017519/19©BEIESP
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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: Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs. In this paper, multiple linear regression model is developed to predict the students’ score in Final Exam using their assessments’ score. The response variable in this model is the students’ score in Final Exam and the predictor variables are the assessment components (Test 1 and Test 2). The data were collected from a group of students in School of Actuarial Science, Mathematics, and Qualitative Study (SOMAQS), Asia Pacific University of Technology and Innovation (APU), Malaysia. In this research, a regression model has been developed with the aid of Statistical Package for Social Sciences (SPSS) analysis tool. The graphical representations and tables are presented to illustrate the models.
Keywords: Multiple Linear Regression, Students’ Performance, Learning Analytics, SPSS, Response, Variable, Predictor Variable, Correlation.
Scope of the Article: Regression and Prediction