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Early detection of student degree-level academic performance using educational data mining.

Areej Fatemah Meghji¹, Naeem Ahmed Mahoto¹, Yousef Asiri²

¹Department of Software Engineering, Mehran University of Engineering and Technology Jamshoro, Hyderabad, Jamshoro, Pakistan.

Peerj. Computer Science

|June 22, 2023

Summary

This summary is machine-generated.

Educational data mining predicts student performance using classification. This research proposes a framework to segment students by academic level, aiding pedagogical policy development for improved educational outcomes.

Keywords:

Classification Data mining Decision tree Educational data mining Pedagogical policy Student performance prediction

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Area of Science:

Educational Data Mining
Higher Education Analytics

Background:

Universities generate vast student data crucial for understanding learning behaviors.
Educational Data Mining (EDM) offers methods to extract valuable insights from this data.

Purpose of the Study:

To analyze student data using EDM classification to predict academic performance.
To propose a student segmentation framework for identifying performance levels.
To support pedagogical policy development for enhancing educational quality.

Main Methods:

Applied classification techniques from educational data mining.
Analyzed data from 291 university students.
Developed a student segmentation framework for academic performance levels.

Main Results:

The classification model effectively predicted student performance.
The segmentation framework successfully identified students across different academic levels.
Early-course data from the first two years proved sufficient for classification.

Conclusions:

The proposed EDM framework is effective for predicting and segmenting students by performance.
This approach can inform pedagogical strategies to reduce academic failure and boost student success.
Early academic indicators can be leveraged for timely interventions.