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ERIC Number: EJ1314542
Record Type: Journal
Publication Date: 2021-Nov
Pages: 9
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0018-9359
EISSN: N/A
Using Process and Motivation Data to Predict the Quality with Which Preservice Teachers Debugged Higher and Lower Complexity Programs
Belland, Brian R.; Kim, ChanMin; Zhang, Anna Y.; Baabdullah, Afaf A.; Lee, Eunseo
IEEE Transactions on Education, v64 n4 p374-382 Nov 2021
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 sigma[superscript 2] 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.
Institute of Electrical and Electronics Engineers, Inc. 445 Hoes Lane, Piscataway, NJ 08854. Tel: 732-981-0060; Web site: http://bibliotheek.ehb.be:2578/xpl/RecentIssue.jsp?punumber=13
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education; Early Childhood Education
Audience: N/A
Language: English
Sponsor: National Science Foundation (NSF)
Authoring Institution: N/A
Grant or Contract Numbers: 1906059; 1927595