Abstract
ABSTRACT In this study, a predictive model is constructed to analyze learners’ performance in programming tasks using data of programming behavioral events and behavioral sequences. First, this study identifies behavioral events from log data and applies lag sequence analysis to extract behavioral sequences that reflect learners’ programming strategies. Then, learners’ behavioral events and behavioral sequences are selected using linear regression. Finally, a predictive model is built using majority vote. The study uses data from 3151 programming tasks to compare the accuracy of predictive models with and without behavioral sequences. The results show that the accuracy of the predictive model with behavioral sequences is 75.75%, which is 2.06% higher than that of the model without behavioral sequences. This study finds that editing, debugging, and self-regulating behavior sequences can reflect the strategies learners employ during programming. These findings underscore that the model based on behavioral events and behavioral sequences in this study can successfully predict learners’ programming performance and thereby has the potential to support programming instruction.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
