Grading for Self-Efficacy in Introductory Computer Science

Extended abstract | Doctoral Consortium

Megan Englert
Annual Conference on Innovation and Technology in Computer Science Education, 2025

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APA Click to copy
Englert, M. (2025). Grading for Self-Efficacy in Introductory Computer Science. In Annual Conference on Innovation and Technology in Computer Science Education.

Chicago/Turabian Click to copy
Englert, Megan. “Grading for Self-Efficacy in Introductory Computer Science.” In Annual Conference on Innovation and Technology in Computer Science Education, 2025.

MLA Click to copy
Englert, Megan. “Grading for Self-Efficacy in Introductory Computer Science.” Annual Conference on Innovation and Technology in Computer Science Education, 2025.

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@inproceedings{megan2025a,
  title = {Grading for Self-Efficacy in Introductory Computer Science},
  year = {2025},
  journal = {Annual Conference on Innovation and Technology in Computer Science Education},
  author = {Englert, Megan}
}

Abstract

Self-efficacy has long been linked to student motivation and retention in computing and other fields. While alternatives to traditional points-based grading systems (e.g., specifications grading) are gaining interest in the computing education research community, little research has investigated its impact on students' self-perceptions. Even variations in points-based grading, such as policies regarding attendance and late work, have not been sufficiently analyzed to understand their relationship to student self-efficacy. With this project, my aim is to identify and encourage best-practice grading policies for positive student self-efficacy in introductory computing classes.