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Using Process and Motivation Data to Predict the Quality With Which Preservice Teachers Debugged Higher and Lower Complexity Programs | IEEE Journals & Magazine | IEEE Xplore

Using Process and Motivation Data to Predict the Quality With Which Preservice Teachers Debugged Higher and Lower Complexity Programs


Abstract:

Contribution: This study indicates that supporting debugging processes is a strong method to improve debugging outcome quality among preservice, early childhood education...Show More

Abstract:

Contribution: This study indicates that supporting debugging processes is a strong method to improve debugging outcome quality among preservice, early childhood education (ECE) teachers. Background: Central to preparing ECE teachers to teach computer science is helping them learn to debug. Little is known about how ECE teachers’ motivation and debugging process quality contributes to debugging outcome quality. Research Questions: How do debugging process and motivation variables predict the quality with which participants debug lower and higher complexity programs? Method: A Bayesian multiple linear regression model with debugging process and motivation variables as predictors was used to predict debugging outcome quality. An inverse gamma prior distribution for sigma2 and uniform prior distribution for Betas was used. Findings: The strongest positive predictor of debugging outcome quality for both the lower complexity and higher complexity debugging task was debugging process quality.
Published in: IEEE Transactions on Education ( Volume: 64, Issue: 4, November 2021)
Page(s): 374 - 382
Date of Publication: 10 March 2021

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