Productive Failure is a variant of problem-based learning in which the order of the instruction and problem-solving phase is reversed. The effectiveness of Productive Failure with respect to conceptual knowledge has been demonstrated through a number of studies. The majority of these studies, however, took place in secondary Mathematics classrooms, whereas other studies resulting in less or no support of such an effectiveness were contextualized in other disciplines, including Computer Science, or in tertiary education. This has raised the question of which conditions support or hamper the use of Productive Failure. To deepen our understanding of such conditions, we designed and executed a Productive Failure intervention for a Pattern Recognition course, thus shifting the intervention context into a tertiary setting while maintaining proximity to Mathematics. In an experimental study, we compared the problem-solving progression of students in a Productive Failure setting with the progression of students in a traditional Direct Instruction setting. For this, we analyzed patterns of discourse arising among the participants as well as the longer-term retention of the concepts addressed. The results of our qualitative analysis suggest that, even in a short intervention, Productive Failure can be used to elicit a distinct pattern of progressing though the problem-solving process. At the same time, our study confirmed previous findings that the mode of instruction does not affect exam performance with respect to the specific topics addressed in the intervention. We discuss limitations of the study setting and possible implications for designing future research studies and teaching interventions.

Motivation and Objectives. Productive Failure is a problem-based learning technique where students attempt to solve a problem before receiving instruction in the topic. By design, students may not find a satisfying solution. Prior studies of Productive Failure in STEM contexts have been conducted in secondary or introductory college settings. Focusing primarily on exploring appropriate analysis and modeling techniques, these studies showed that a Productive Failure approach can lead to greater conceptual knowledge acquisition and transfer capabilities compared to »traditional«Direct Instruction techniques. In this study, we build on these studies along two dimensions: First, we report on the design and evaluation of a Productive Failure intervention in a more advanced undergraduate class: third-year Operating Systems. Second, our intervention targeted a more advanced skill: applying synchronization primitives, rather than selecting appropriate modeling and analysis techniques.

Productive Failure is an instructional setting where learners are confronted with a problem-solving task prior to recieving an introduction to canonical solution methods. This approach has its roots in secondary school mathematics education and has been studied widely in this field. To the best of our knowledge, however, little if anything is known about its feasibility and efficacy in computer science at a tertiary level. In my PhD project, I work towards reducing this gap by comparing the outcome of Productive Failure settings with those of Direct Instruction approaches where students are introduced to required methods first. Is it possible to design educational settings in computer science that elicit the positive effects of Productive Failure on students’ learning? By designing interventions that allow for collecting and analyzing of both qualitative and quantitative data, I hope to find answers to this question and its corrolaries.

For many years, the C++-based Computer Programming Self-Efficacy Scale by Ramalingam and Wiedenbeck has been the de facto standard for assessing self-efficacy in introductory programming. Since the development of this instrument, however, both the landscape as well as the intended audience of introductory programming courses has changed beyond the use of a particular programming language. We revisit this instrument and its factorization in light of curricular developments and research results regarding concepts and competences taught in introductory courses. We report on the development and validation of a new instrument that covers most paradigms and languages used in CS1 and present exploratory and confirmatory factor analyses across different populations. Our validation and factor analyses suggest that the new instrument indeed measures self-efficacy with an acceptable fit of the model. In contrast, the factorization of the Computer Programming Self-Efficacy Scale was found to be less robust. Nonetheless, and in line with self-efficacy theory, our analyses suggest that researchers should take into account the educational context of the study population when reporting or comparing results at the level of factors.