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Related Experiment Video

Updated: Sep 1, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Predicting student performance using sequence classification with time-based windows.

Galina Deeva¹, Johannes De Smedt¹, Cecilia Saint-Pierre²

¹Research Centre for Information Systems Engineering, KU Leuven, Belgium.

Expert Systems with Applications

|August 15, 2022

Summary

This summary is machine-generated.

This study shows that analyzing student behavior patterns in online courses can accurately predict academic performance. Predictive models achieved 90% accuracy in identifying at-risk students early, improving the e-learning experience.

Keywords:

Behavioral patterns Feature engineering Machine learning Sequence mining Success prediction

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Published on: February 14, 2018

Area of Science:

Educational Technology
Computer Science
Data Science

Background:

The COVID-19 pandemic accelerated the adoption of online and blended learning in higher education.
E-learning offers enhanced student experiences and educational opportunities.
Learning analytics provide valuable insights into student learning processes.

Purpose of the Study:

To develop accurate predictive models for identifying underperforming students in e-learning environments.
To investigate the trade-off between model specificity (course-specific) and generalizability (cross-course) for predictive modeling.
To introduce a methodology for incorporating temporal data aspects to improve predictive performance.

Main Methods:

Utilizing sequential patterns derived from student behavioral data.
Developing and evaluating predictive models for student performance.
Comparing course-specific versus cross-course predictive models.
Implementing a novel methodology to capture temporal dynamics in behavioral data.

Main Results:

Accurate predictive models for student performance can be built using sequential behavioral patterns.
Course-specific models achieved high predictive accuracy, reaching up to 90%.
The methodology for capturing temporal aspects positively influenced model performance.

Conclusions:

Sequential pattern analysis of student behavior is effective for early identification of underperforming students in online courses.
The study provides insights into optimizing predictive model design for e-learning.
Accurate prediction of student performance can enhance e-learning strategies and student support.