Using self-efficacy and assessment to support computer science student success

Rao, Jun (2021) Using self-efficacy and assessment to support computer science student success. Doctoral thesis, Northern Arizona University.

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Abstract

In recent years, not only has there been a dramatic drop in the number of students enrolling in computer science courses, and attrition from computer science courses continues to be significant. Traditionally, computer programming courses have high failure rates, and as they tend to be core to computer science courses can be a roadblock for many students to their studies. But, is a computer science course really that difficult — or are there other barriers that negatively affect student progression? This work uses qualitative research following three aspects of computer science pedagogy to help students to improve their academic performance. These three aspects include:(1) How to manage students learning behavior --- Pair Programming and Self-Efficacy in CS1; (2) How to assess students learning and performance --- Logarithmic Rubric Scoring; (3) How to manage student behavior in the examination to make sure fairness --- The Geometry of Assessment. Results showed that this work could incrementally predict course performance, and it mediated the relationship between study behavior (self-efficacy or self-assessment) and both academic criteria. The computer science pedagogy behaviors in the research are a promising predictor of educational outcomes, and they may have usefulness in student course performance.

Item Type:	Thesis (Doctoral)
Publisher’s Statement:	© Copyright is held by the author. Digital access to this material is made possible by the Cline Library, Northern Arizona University. Further transmission, reproduction or presentation of protected items is prohibited except with permission of the author.
Keywords:	Assessment; Computer Science Pedagogy; Grading practice; Pair Programming; Self-efficacy
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
NAU Depositing Author Academic Status:	Student
Department/Unit:	Graduate College > Theses and Dissertations College of Engineering, Informatics, and Applied Sciences > School of Informatics, Computing, and Cyber Systems
Date Deposited:	14 Feb 2022 18:02
Last Modified:	14 Feb 2022 18:02
URI:	https://openknowledge.nau.edu/id/eprint/5694

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