Abstract:
Contribution: This study indicates that supporting debugging processes is a strong method to improve debugging outcome quality among preservice, early childhood education...Show MoreMetadata
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)
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- IEEE Keywords
- Index Terms
- High Complexity ,
- Low Complexity ,
- Bayesian Model ,
- Strongest Predictor ,
- Early Education ,
- Uniform Prior ,
- Bayesian Regression ,
- Motivational Variables ,
- Inverse Gamma ,
- Bayesian Regression Model ,
- Early Childhood Education Teachers ,
- Final Model ,
- Intraclass Correlation Coefficient ,
- Cohen’s D ,
- Markov Chain Monte Carlo ,
- Goal Orientation ,
- Word Count ,
- Negative Valence ,
- Sentiment Analysis ,
- Identification Of Domains ,
- STEM Career ,
- Mastery Goals ,
- Interest In Mathematics ,
- Achievement Emotions ,
- Stereotype Threat ,
- Self-regulated Learning ,
- Performance-approach Goals ,
- Bug Fixes ,
- Interest In Technology ,
- Performance-avoidance Goals
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- High Complexity ,
- Low Complexity ,
- Bayesian Model ,
- Strongest Predictor ,
- Early Education ,
- Uniform Prior ,
- Bayesian Regression ,
- Motivational Variables ,
- Inverse Gamma ,
- Bayesian Regression Model ,
- Early Childhood Education Teachers ,
- Final Model ,
- Intraclass Correlation Coefficient ,
- Cohen’s D ,
- Markov Chain Monte Carlo ,
- Goal Orientation ,
- Word Count ,
- Negative Valence ,
- Sentiment Analysis ,
- Identification Of Domains ,
- STEM Career ,
- Mastery Goals ,
- Interest In Mathematics ,
- Achievement Emotions ,
- Stereotype Threat ,
- Self-regulated Learning ,
- Performance-approach Goals ,
- Bug Fixes ,
- Interest In Technology ,
- Performance-avoidance Goals
- Author Keywords