The curious case of loops

ABSTRACT

Background and Context

Subgoal labeled worked examples have been extensively researched, but the research has been reported piecemeal. This paper aggregates data from three studies, including data previously unreported, to holistically examine the effect of subgoal labeled worked examples across three student populations and across different instructional designs.

Objective

By aggregating the data, we provide more statistical power for somewhat surprising yet replicable results. We discuss which results generalize across populations, focusing on a stable effect size for subgoal labels in programming instruction.

Method

We use descriptive and inferential statistics to examine the data collected from different student populations and different classroom instructional designs. We concentrate on the effect size across samples of the intervention for generalization.

Findings

Students using two variations of subgoal labeled instructional materials perform better than the others: the group that was given the subgoal labels with farther transfer between worked examples and practice problems and the group that constructed their own subgoal labels with nearer transfer between worked examples and practice problems.

KEYWORDS:

• Worked example
• subgoal label
• experiment
• CS1

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed here.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the National Science Foundation 1712231 and 1712025.

Notes on contributors

Briana B. Morrison

Briana B. Morrison is an Assistant Professor of Computer Science at the University of Nebraska Omaha. She has over 20 years’ experience in teaching computer science. She has served on the ACM SIGCSE Board and ACM Education Committee. Her research area is CS Education where she explores cognitive load theory within learning programming, broadening participation in computing and expanding and preparing computing high school teachers.

Lauren E. Margulieux

Lauren E. Margulieux is an Assistant Professor of Learning Sciences at Georgia State University. Her research interests are in educational technology and online learning, particularly for computing education. She focuses on designing instructions in a way that supports online students who do not necessarily have immediate access to a teacher or instructor to ask questions or overcome problem solving impasses.

Adrienne Decker

Adrienne Decker is an Assistant Professor in the Department of Engineering Education at University at Buffalo. Her research interests are in computing education, particularly at the introductory level. She is interested in techniques that support learning of introductory programming material at the university level and the impact that exposure to computing prior to university has on learners in the introductory courses.