What does this research mean for the field?

Machine learning models utilizing a weighted attribute method that combines engagement, learning difficulty, and time allocation metrics can effectively identify technical education students at risk of academic failure. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This study aims to explore how AI and learning analytics can predict student dropout risk in technical education.

March 25, 2026Scientific ReportsOpen Access

Using AI to forecast student dropout risk in technical education using a learning analytics approach

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Authors

OOOlga OvtšarenkoVilnius Gediminas Technical University

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Implication

Predictive analytics improves dropout detection in technical education, suggesting timely interventions.

Key Points

This study aims to explore how AI and learning analytics can predict student dropout risk in technical education.
Analyzed data from six CAD course logs delivered via Moodle.
Developed a machine learning model using 99,104 activity records from 154 students.
Employed logistic regression and decision tree classifiers for prediction.
Standardized key metrics such as engagement, learning difficulty, and time allocation.
Predictive models successfully identified at-risk students.
Enabled early alerts for potential academic failure.
Facilitated adaptive support through continuous monitoring.

Cite This Study

Olga Ovtšarenko (2026) studied this question.

synapsesocial.com/papers/69c37be2b34aaaeb1a67ec90 https://doi.org/10.1038/s41598-026-44919-1

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Using AI to forecast student dropout risk in technical education using a learning analytics approach

Discussion

Member takes

Implication

Key Points

Cite This Study

Using AI to forecast student dropout risk in technical education using a learning analytics approach

Discussion

Member takes

Implication

Key Points

Cite This Study

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