Thesis Abstract

Abstract
Predicting students' academic performance is crucial for educators to provide timely support and interventions to help students succeed. Decision tree algorithms have shown promise in the field of education for predicting student outcomes based on various input variables. This research project focuses on utilizing decision tree models to predict students' academic performance based on demographic, social, and academic factors. The dataset used in this study comprises information on students' demographics (such as age, gender, and ethnicity), social background (parental education and occupation), and academic history (previous grades, attendance records, and study habits). By incorporating these factors into the decision tree algorithm, the model can analyze and identify patterns that influence students' academic performance. The decision tree algorithm works by recursively partitioning the dataset into subsets based on different attributes, creating a tree-like structure where each internal node represents a decision based on an attribute, and each leaf node corresponds to a predicted outcome. Through this process, the model can effectively classify students into different performance categories, such as high, medium, and low achievers. The research project employs popular decision tree algorithms such as CART (Classification and Regression Trees) and C4.5 to build predictive models for students' academic performance. These algorithms use different splitting criteria and pruning techniques to optimize the tree structure and improve prediction accuracy. To evaluate the performance of the decision tree models, various metrics such as accuracy, precision, recall, and F1 score are used. Additionally, techniques like cross-validation and grid search are employed to fine-tune the model hyperparameters and prevent overfitting. The results of the study demonstrate the effectiveness of decision tree models in predicting students' academic performance with a high degree of accuracy. The models successfully capture the complex relationships between input variables and academic outcomes, providing valuable insights for educators to identify at-risk students early and implement targeted interventions. In conclusion, this research project highlights the potential of decision tree algorithms in predicting students' academic performance based on a diverse set of factors. By leveraging these predictive models, educators can enhance their ability to support students effectively and improve overall academic outcomes.

Thesis Overview

1.1.Background of the Study

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).

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).

1.2. Statement of the Problem

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.

1.3. Aim and Objectives of the Study

The aim of this project is to predict a student’s performance using the decision tree method.

 The specific objectives are:

1.     To identify various factors that affect the performance of students in their academic endeavors.

2.      To use the identified factors as well as the student’s past performance to predict the future performance of the student.

3.     To develop a model which can predict student’s academic performance using decision tree method.

1.4. Scope and limitation of the Study

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.

1.5.Significance of the Study

1.     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

2.     To help the students identify and eliminate those factors either found in the student himself or the school or the society.

3.     To help the tutors and teachers find solutions to the problems affecting the weaker students so as to enhance overall academic performance