Student academic performance prediction by using decision tree algorithm

Raza Hasan, Sellappan Palaniappan, Abdul Rafiez Abdul Raziff, Salman Mahmood, Kamal Uddin Sarker

Research output: Chapter in Book/Report/Published conference proceedingConference contributionpeer-review


This work explores student's academic performance using decision tree algorithm having parameters like Student's Academic Information and Students Activity. We collected records of 22 students from Spring 2017 semester, studying in undergraduate level from Oman's private Higher Education Institution. Proposed work utilizes Electronic Commerce Technologies module since it is a core module offered in every computing specialization. Furthermore, WEKA data mining tool is used to evaluate the decision tree algorithm for discovery of student's performance along with Moodle access time. Simulation results demonstrate that Random Forest Tree algorithm showed better accuracy than comparative decision tree algorithms. Hence, shows good agreement for the training set provided. Therefore, the proposed work aid in improving student's grades in the module. Helping stakeholders to analyze and evaluate the module delivery and results. Early detection and solution can be made both at the institutional level and module level.
Original languageEnglish
Title of host publication2018 4th International Conference on Computer and Information Sciences (ICCOINS)
Number of pages5
ISBN (Electronic)978-1-5386-4744-8
ISBN (Print)978-1-5386-4745-5
Publication statusPublished - 28 Oct 2018
Event2018 4th International Conference on Computer and Information Sciences (ICCOINS) - Kuala Lumpur, Malaysia
Duration: 13 Aug 201814 Aug 2018


Conference2018 4th International Conference on Computer and Information Sciences (ICCOINS)
Abbreviated titleICCOINS
CityKuala Lumpur

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