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

Brian R. Belland, Chanmin Kim, Anna Y. Zhang, Afaf A. Baabdullah, Eunseo Lee

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)374-382
Number of pages9
JournalIEEE Transactions on Education
Volume64
Issue number4
DOIs
StatePublished - Nov 1 2021

All Science Journal Classification (ASJC) codes

  • Education
  • Electrical and Electronic Engineering

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