Automatic Assessment of Students' Engineering Design Performance Using a Bayesian Network Model. (April 2021)

• Record Type: Journal Article
• Authors: Xing, Wanli; Li, Chenglu; Chen, Guanhua; Huang, Xudong; Chao, Jie; Massicotte, Joyce; Xie, Charles
• Is Part Of: Journal of educational computing research. Volume 59:Number 2(2021)
• Page Start: 230
• Page End: 256
• Publication Date: 2021-04
• Subjects: Bayesian network -- engineering design -- assessment -- learning analytics -- educational data mining; Computer literacy -- Periodicals; Computer-assisted instruction -- Periodicals; Computer managed instruction -- Periodicals; Education -- Data processing -- Periodicals; 371.334
• DOI: 10.1177/0735633120960422
• Languages: English
• ISSNs: 0735-6331

Abstract

Integrating engineering design into K-12 curricula is increasingly important as engineering has been incorporated into many STEM education standards. However, the ill-structured and open-ended nature of engineering design makes it difficult for an instructor to keep track of the design processes of all students simultaneously and provide personalized feedback on a timely basis. This study proposes a Bayesian network model to dynamically and automatically assess students' engagement with engineering design tasks and to support formative feedback. Specifically, we applied a Bayesian network to 111 ninth-grade students' process data logged by a computer-aided design software program that students used to solve an engineering design challenge. Evidence was extracted from the log files and fed into the Bayesian network to perform inferential reasoning and provide a barometer of their performance in the form of posterior probabilities. Results showed that the Bayesian network model was competent at predicting a student's task performance. It performed well in both identifying students of a particular group (recall) and ensuring identified students were correctly labeled (precision). This study also suggests that Bayesian networks can be used to pinpoint a student's strengths and weaknesses for applying relevant science knowledge to engineering design tasks. Future work of implementing this tool within the computer-aided design software will provide instructors a powerful tool to facilitate engineering design through automatically generating personalized feedback to students in real time.