Library science

Random forest

Algorithm

Mathematics education

Academic institution

Engineering

Telecommunications

Set (abstract data type)

Student engagement

Data mining

Core (optical fiber)

Computer science

Mechanical engineering

Mathematics

Decision tree learning

Work (physics)

Mathematical analysis

Data science

Artificial intelligence

Decision tree

Tree (set theory)

Programming language

Machine learning

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.

Student Academic Performance Prediction by using Decision Tree Algorithm