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Surveys

Surveys

Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

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Detecting Burnout Among Undergraduate Computing Students with Supervised Machine Learning.

Eldar Yeskuatov¹, Lee Kien Foo¹, Sook-Ling Chua¹

¹Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia.

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Summary

This summary is machine-generated.

Machine learning can detect academic burnout using university records, showing promise for identifying exhaustion and cynicism. This approach offers a survey-free method for early intervention in student well-being.

Keywords:

academic burnout burnout detection machine learning student mental health well-being

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Area of Science:

Educational Psychology
Computer Science
Data Science

Background:

Academic burnout negatively affects students' cognitive and psychological health, potentially leading to behavioral issues.
Early detection of student burnout is vital for institutions to provide support and interventions.
Current survey methods for burnout detection face challenges like response bias and administrative burden.

Purpose of the Study:

To explore the feasibility of using machine learning models trained on university administrative data for detecting academic burnout.
To develop and evaluate models for identifying three dimensions of burnout: exhaustion, cynicism, and low professional efficacy.
To assess the potential of a survey-free approach for unobtrusive student burnout detection.

Main Methods:

Developed machine learning models to detect exhaustion, cynicism, and low professional efficacy.
Utilized five algorithms: logistic regression, support vector machine, naive Bayes, decision tree, and extreme gradient boosting.
Engineered features exclusively from administrative university records, avoiding psychological surveys.

Main Results:

Model performance varied across burnout dimensions, with exhaustion detection yielding the highest results.
Logistic regression achieved an F1 score of 68.4% for exhaustion detection.
Cynicism detection showed moderate performance, while professional efficacy detection had the lowest performance.

Conclusions:

Automated detection of academic burnout signs, particularly exhaustion and cynicism, is feasible using passively collected university records.
The study highlights limitations in capturing psychological constructs solely through administrative data.
Findings provide a basis for future research into unobtrusive methods for student burnout detection.