Overview

Educational data mining focuses on developing different methods for solving educational problems which are hidden in an education field. The major problem which is faced in an education field is ‘Student Dropouts or Failure’. There are many factors which are influencing the student dropouts. Many data mining methods are used for identifying and predicting student’s failure. A student failure is a major social problem where educational professionals need to understand the causes, why many students fail in completing their education. It is a difficult task as there are many factors that cause student failure.

Goal

This project understands how the student’s performance is affected by other variables such as Gender, Ethnicity, Parental level of education, and Lunch and Test preparation course. This project focuses on evaluating students’ capabilities in various subjects using a classification task. By performing this task, knowledge is extracted that describes students’ performance in the end-semester examination. This helps in identifying dropouts and students who require special attention, enabling teachers to provide appropriate advising and counseling.

Objectives

Techniques/Models

1. Techniques:
   • Data exploration
   • Feature Engineering
   • Categorical Encoding
   • Hyperparameter tuning
   • Performance metric evaluation
2. Models
   • Logistic Regression Classifier
   • Decision Tree Classifier
   • Random Forest Classifier
   • XGBoost Classifier

Findings

1. Assigning research-based topics to students helps them explore the online resources for their survey.
2. Regular class test results provides how attentive and interactive the students are in the class while lecture delivery.
3. An eye should be kept on how frequently the students check the announcements made by the professor and should be strictly followed.
4. Group discussions should be organized in order to share the knowledge of any trending topic related to studies.