Students academic performance prediction using decision tree

Project Abstract

Predicting students' academic performance is crucial for educational institutions to provide targeted support and interventions. Decision tree algorithms have been widely used in educational data mining for predicting student outcomes. This research project aims to explore the application of decision tree models in predicting students' academic performance. The study will utilize a dataset containing various attributes such as students' demographic information, previous academic records, attendance, and study habits. These attributes will be used as input features for the decision tree algorithm to predict students' final grades or performance levels. The decision tree model will be trained on a portion of the dataset and tested on another portion to evaluate its performance. Various metrics such as accuracy, precision, recall, and F1 score will be used to assess the model's predictive capabilities. Additionally, feature importance analysis will be conducted to identify the most influential factors that contribute to students' academic performance. This analysis will provide insights into the key indicators that educators and administrators can focus on to improve student outcomes. Furthermore, the study will compare the performance of decision tree models with other machine learning algorithms commonly used in academic performance prediction, such as logistic regression and support vector machines. This comparison will help determine the effectiveness of decision tree models in this context. The research findings are expected to contribute to the development of predictive models that can assist educational institutions in identifying students at risk of academic failure and providing timely interventions to support them. By accurately predicting students' academic performance, educators can tailor their teaching strategies and support services to meet individual student needs effectively. In conclusion, this research project aims to leverage decision tree algorithms to predict students' academic performance based on various input features. The study will not only assess the predictive capabilities of decision tree models but also identify key factors influencing students' academic outcomes. The findings of this research will have implications for improving educational practices and fostering student success.

Project Overview

<p> </p><p><strong>1.1.Background of the Study</strong></p><p>Since one of the goals oftertiary institutions is to contribute to the improvement of the quality and standard of higher education, the success in the creation of human capital has been a subject of continuous analysis. Hence the prediction of students’ success is very important to these higher education institutions, because the purpose of any teaching process is to meet students’ educational needs and enhance overall student’s academic success. In this regard, important data and informationare gathered on a regular basis after which they are used in the prediction of students’ academic performance (EdinOsmanbegovic, 2012).</p><p>Measuring and predicting the academic performance of students has been a challenging task since students’ academic performance depends on diverse factors such as personal, socio-economic, psychological and other environmental variables. But the prediction of student’s performance is a very important endeavor as it helps the student and teachers to minimizepoor academic performances and produce better educated and enlightened students in order to make the society a better place. With the help of performance prediction, a failing student can be identified and helped by putting all the factors affecting the student into consideration and providing solutions to counter this factors so as to facilitate better performance (Brijesh Kumar Bhardwaj and Saurabh Pal, 2011).</p><p><strong>1.2. Statement of the Problem</strong></p><p>Without adequate measures to curb the existing problem of persistent students’ failure, it will continue to remain a major problem for higher institutions. But with the analysis of the factors which are socio-economic, psychological and environmental, a headway can be made towards curbing the problem of student failure.</p><p><strong>1.3. Aim and Objectives of the Study</strong></p><p>The aim of this project is to predict a student’s performance using the decision tree method.</p><p>&nbsp;The specific objectives are:</p><p>1. &nbsp; &nbsp; To identify various factors that affect the performance of students in their academic endeavors.</p><p>2. &nbsp; &nbsp; &nbsp;To use the identified factors as well as the student’s past performance to predict the future performance of the student.</p><p>3. &nbsp; &nbsp; To develop a model which can predict student’s academic performance using decision tree method.</p><p><strong>1.4. Scope and limitation of the Study</strong></p><p>This project work titled STUDENTS ACADEMIC PERFORMANCE PREDICTION USING DECISION TREE attempts to analyze those factors that affect the students academically. Furthermore, this work predicts the future academic performance of students but does not automatically address these problems as the tutors and teachers and even the students themselves still need to take steps towards curbing the performance problem by eliminating this factors themselves.</p><p><strong>1.5.Significance of the Study</strong></p><p>1. &nbsp; &nbsp; To help teachers and tutors identify weak and strong students so teachers can lay more emphasis on instructions and procedures when dealing with the weak students</p><p>2. &nbsp; &nbsp; To help the students identify and eliminate those factors either found in the student himself or the school or the society.</p><p>3. &nbsp; &nbsp; To help the tutors and teachers find solutions to the problems affecting the weaker students so as to enhance overall academic performance</p> <br><p></p>