Background and Context: A traditional computer science (CS) undergraduate student will enroll in a sequence of computer programming courses (e.g., CS1, CS2) leading to a typical data structures course following ACM curriculum guidelines [1]. Unfortunately, these courses often do not boast the best retention rates [2], and students perceive the course sequence as a barrier to entry into the CS profession. This study attempts to better understand this programming course sequence by developing and validating a measure for students who completed CS2 and are enrolling in data structures next. Our goal is to use this measurement system as a predictive tool to gauge an undergraduate student’s success in data structures and to provide information to the students as a self-assessment of their learning. The notion of self-efficacy inspires our measurement tool because it has a documented relationship with academic achievement [3]. The tool was carefully aligned to the CS2 course topics at two public universities in the southeastern United States (U.S.).

Objectives: The overarching objective of this research is to develop and validate a self-efficacy measure for CS2 courses for undergraduate CS students and to provide validity and reliability evidence for the measure. We attempted to answer the following research questions: What are the self-efficacy factors for students who completed CS2, and what evidence of validity and reliability can support using this measure?

Method: After carefully reviewing the syllabi and curriculum resources for the CS2 course and interviewing the instructors at two public universities in the southeastern U.S., we created topic indicators as a blueprint for creating our self-efficacy measurement system. Both institutions emphasized the underlying principles of Object-Oriented Programming (OOP) in their CS2 courses, and thus, our final draft had 27 items aligned to OOP concepts. We administered the scale to n = 292 students enrolled in a data structures course at both institutions. We connected the data to their academic performance in the data structures course at the conclusion of the semester. Exploratory factor analysis (EFA) and internal consistency reliability analysis were executed to identify the underlying factors of the scale and provide reliability evidence. We used ordinal regression models to connect the factors to the undergraduate student’s grades in the data structures course for predictive validity evidence. We also analyzed the role of prior programming experience in impacting self-efficacy using non-parametric statistical tests.

Findings: The EFA identified four primary self-efficacy factors: 1) Class Design and Data Manipulation, 2) Flow of Control, 3) Class Hierarchy and Inheritance, and 4) Class Behavior and Methods. It was found that the students with higher self-efficacy scores are significantly more likely to receive higher grades in the data structures course. The overall scores of the self-efficacy scale were found to be higher in the students with prior programming experience.

Implications: To the best of the authors’ knowledge, this is the first instrument directed toward assessing self-efficacy in CS2 courses emphasizing the domain of OOP. In the field of CS education, understanding both teaching and student learning can be improved by developing and using instruments that can measure how students perceive their self-efficacy for personal growth, research, and predictive applications.

References:

[1] Amruth N. Kumar, Rajendra K. Raj, Sherif G. Aly, Monica D. Anderson, Brett A. Becker, Richard L. Blumenthal, Eric Eaton, Susan L. Epstein, Michael Goldweber, Pankaj Jalote, Douglas Lea, Michael Oudshoorn, Marcelo Pias, Susan Reiser, Christian Servin, Rahul Simha, Titus Winters, and Qiao Xiang. 2024. Computer Science Curricula 2023. Association for Computing Machinery, New York, NY, USA.

[2] Bennedsen, J., & Caspersen, M. E. (2019). Failure rates in introductory programming: 12 years later. ACM Inroads, 10(2), 30-36.

[3] Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63-84.