MySPPS: the development of students performance prediction system using machine learning

Nurul Hidayah Jemat

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Type :	final_year_project
Subject :	LB Theory and practice of education
Main Author :	Nurul Hidayah Jemat
Title :	MySPPS: the development of students performance prediction system using machine learning

Place of Production :	Tanjong Malim
Publisher :	Fakulti Seni, Komputeran dan Industri Kreatif
Year of Publication :	2022
Corporate Name :	Perpustakaan Tuanku Bainun
PDF Guest :	Click to view PDF file

Abstract : Perpustakaan Tuanku Bainun

The accurate estimation of students’ grades in future courses is important as it can inform the selection of next semester courses and create personalized degree pathways to facilitate successful and timely graduation. At present, students’ dropout rate in university is gradually increasing and in the majority of cases drives the students to be either motivated or demotivated. Therefore MySPPS was developed to predict students’ performance based on eight student’s skills. For this purpose, Random Forest Regression had been used for classifying students’ different levels of results and predicting students’ performances. The result shows that RFR can perform with more than 80% accuracy. Thus, MySPPS provides decision-making support for students to choose courses reasonably to improve grades and remind them to understand their performance.

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