The Development of Student Academic Performance Prediction System for UPTM using RepTree Algorithm скачать в хорошем качестве

The Development of Student Academic Performance Prediction System for UPTM using RepTree Algorithm

Paper ID: ICASE 2023: 010 042 Title: The Development of Student Academic Performance Prediction System for UPTM using RepTree Algorithm Presenter Name: Noornajwa Md Amin Accurate prediction of student performance is critical in this digital age for educational institutions to identify at-risk pupils and provide timely interventions. Numerous models have been proposed under different educational context to address the student performance prediction but there is lack of sophisticated models caused difficulty towards the user on giving guide to the stakeholders to take appropriate measures to counter student’s problems. The RepTree algorithm, a decision tree-based machine learning approach used to model and forecast student academic achievement, was adopted in this study to develop a Student Academic Performance Prediction System (SAPPS) for the University Poly-Tech Malaysia (UPTM). This study is using a mixed method research design, based on historical student data to train the predictive model, including demographics, prior academic performance, and sponsorship attributes. Evaluation result shows how well the established system predicts student academic success and how reliable it is. The system's predictive capabilities give educational institutions the ability to spot students who are likely to have academic difficulties and take proactive measures to improve their results. The use of such a predictive system could improve the learning environment and boost students' achievement at UPTM. By having this information, the target users like administrators, lecturers and academic advisers can access and decipher the predictions produced by the RepTree algorithm using the suggested system's user-friendly interface.