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Predicting pre-service teachers' computational thinking skills using machine learning classifiers.

Hao-Yue Jin¹, Maria Cutumisu¹

¹Centre for Research in Applied Measurement and Evaluation, Department of Educational Psychology, Faculty of Education, University of Alberta, 6-102 Education Centre North, Edmonton, T6G 2G5 Canada.

Education and Information Technologies

|February 27, 2023

Summary

This summary is machine-generated.

This study found that Decision Tree models accurately predict pre-service teachers' computational thinking (CT) skills. Key predictors include training time, prior CT skills, and perceived difficulty.

Keywords:

Classifier Computational Thinking Decision Tree Educational Data Mining K-Nearest Neighbors Logistic Regression Machine Learning Naive Bayes Pre-service Teachers

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Area of Science:

Education
Computer Science
Educational Technology

Background:

Computational thinking (CT) skills are crucial for educators, yet CT training effectiveness remains inconsistent.
Identifying key predictors of CT skills is essential for optimizing teacher training programs.

Purpose of the Study:

To develop an online CT training environment for pre-service teachers.
To compare the predictive performance of four machine learning algorithms for CT skill classification.
To identify significant predictors of CT skills in pre-service teachers.

Main Methods:

Developed an online computational thinking training environment.
Utilized supervised machine learning algorithms (Decision Tree, K-Nearest Neighbors, Logistic Regression, Naive Bayes).
Employed log data and survey data for model training and validation.

Main Results:

Decision Tree algorithm demonstrated superior performance in classifying pre-service teachers' CT skills compared to other models.
Time spent on CT training emerged as a primary predictor.
Prior CT skills and perceived difficulty of learning content were also significant predictors.

Conclusions:

Machine learning models, particularly Decision Tree, can effectively predict CT skills in pre-service teachers.
Training duration, existing CT proficiency, and learner perception of difficulty are critical factors influencing CT skill development.