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Predicting and Comparing Students' Online and Offline Academic Performance Using Machine Learning Algorithms.

Barnabás Holicza¹, Attila Kiss^1,2

¹Department of Information Systems, ELTE Eötvös Loránd University, 1117 Budapest, Hungary.

Behavioral Sciences (Basel, Switzerland)

|April 27, 2023

Summary

This summary is machine-generated.

Student performance prediction is crucial for educational success. Machine learning models identified that habits like sleep, study time, and screen time significantly impact academic achievement, guiding interventions for better outcomes.

Keywords:

decision tree e-learning k-nearest neighbors machine learning random forest students’ performance support vector machine

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Area of Science:

Educational Data Mining
Machine Learning in Education
Student Performance Analysis

Background:

The COVID-19 pandemic heightened the importance of researching educational data and improving e-learning systems.
Educational institutions require deeper insights into student performance to optimize talent utilization and address weaknesses.
Maintaining student engagement and improving Grade Point Average (GPA) are key goals in the evolving educational landscape.

Purpose of the Study:

To predict and identify reasons for declining student performance using machine learning algorithms.
To compare the effectiveness of machine learning models on online versus offline learning data.
To analyze the impact of student habits on academic success.

Main Methods:

Utilized machine learning algorithms: Support Vector Machine (SVM) with various kernels, Decision Tree, Random Forest, and K-Nearest Neighbors (KNN).
Compared two distinct datasets: one for online learning and another for offline learning properties.
Applied data normalization techniques to prepare datasets for prediction.

Main Results:

Machine learning models successfully predicted declining student performance.
Analysis revealed significant correlations between academic success and habits such as sleep duration, study time, and screen time.
Comparative analysis of online and offline learning data provided insights into performance metrics like F1 score and accuracy.

Conclusions:

Student academic success is demonstrably linked to lifestyle habits, including sleep, study duration, and screen time.
Machine learning offers a viable approach for predicting student performance and identifying contributing factors.
Understanding these factors can inform targeted interventions to support student achievement in both online and offline learning environments.