Welcome To UTPedia

We would like to introduce you, the new knowledge repository product called UTPedia. The UTP Electronic and Digital Intellectual Asset. It stores digitized version of thesis, final year project reports and past year examination questions.

Browse content of UTPedia using Year, Subject, Department and Author and Search for required document using Searching facilities included in UTPedia. UTPedia with full text are accessible for all registered users, whereas only the physical information and metadata can be retrieved by public users. UTPedia collaborating and connecting peoples with university’s intellectual works from anywhere.

Disclaimer - Universiti Teknologi PETRONAS shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.Best viewed using Mozilla Firefox 3 or IE 7 with resolution 1024 x 768.

STUDENT’S PERFORMANCE PREDICTION USING EDUCATIONAL DATA MINING

QUAH, MIN QI (2020) STUDENT’S PERFORMANCE PREDICTION USING EDUCATIONAL DATA MINING. IRC, Universiti Teknologi PETRONAS. (Submitted)

[img] PDF
Restricted to Registered users only

Download (2794Kb)

Abstract

Every educational institution generates a large amount of data related to the registered students. If that data is not analyzed and used for useful purposes, then all efforts will be wasted as there is no future use of the data occurring. Academic institutions such as universities, colleges and schools usually do not have tools or process flow that uses the big data from the systems (e.g. enrollment and performance systems) to perform prediction on student’s performance. By using the big data, academic institutions shall be able to predict student’s performance for strategic decision making (e.g. improve current teaching model, identify low performing students etc.). The most used technique for prediction is educational data mining. This study is conducted with the aim to better understand educational data mining. The main objective of this study is to appropriate educational data mining techniques and select suitable technique(s) to implement analyses and prediction on the big data obtained. The method that used to conduct this study is design science. The outcome of this study is acquiring which classification method has the highest accuracy in predicting student’s performance and visualizing the prediction results in a Power BI Dashboard. The findings of this study may contribute towards the improvement of educational institution’s teaching models and student performance.

Item Type: Final Year Project
Academic Subject : Academic Department - Information Communication Technology
Subject: Q Science > Q Science (General)
Divisions: Sciences and Information Technology > Computer and Information Sciences
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 23 Sep 2021 23:43
Last Modified: 23 Sep 2021 23:43
URI: http://utpedia.utp.edu.my/id/eprint/21718

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...