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Teachers who are aware of potential student misconceptions teach better than teachers who do not. In this article, we focus on misconceptions in the context of teaching and learning graph algorithms: we seek to discover student misconceptions about Dijkstra’s shortest-path algorithm and related concepts. We observed and interviewed fourteen students who worked on a visual simulation task involving the algorithm; we qualitatively analyzed these data to explore the students’ mistakes and their underlying reasons. We find, among other things, that students conflate concepts such as spanning tree, fringe, and priority queue and that students may neglect the greedy and dynamic-programming aspects of the algorithm; we also identify usability issues in the visualization tool we employed. These findings suggest that teachers and tool designers need to take great care to help students tease apart the key concepts in graph algorithms.

Research into computing and learning how to program has been ongoing for decades. Commonly, this research has been focused on novice learners and the difficulties they encounter, especially during CS1. Cybersecurity is a critical aspect in computing -- as a topic in university education as well as a core skill in the industry. In this study, we investigate how students solve open-ended assignments on a cybersecurity course offered to university students after two years of CS studies. Specifically, we looked at how students perform SQL injection attacks on an web application system, and study to what extent we can characterize the process in which they come up with successful injections. Our results show that there are distinguishable strategies used by individual students who seek to hack the system, where these approaches revolve around exploration and exploitation tactics. We also find evidence of learning due to a more pronounced use of exploitation in a subsequent similar assignment.

The modality effect in multimedia learning suggests that pictures are best accompanied by audio explanations rather than text, but this finding has not been replicated in computing education. We investigate which instructional modality works best as an accompaniment for algorithm visualizations. In a randomized controlled trial, learners were split into three conditions who viewed an instructional video on Dijkstra’s algorithm, with diagrams accompanied by audio, text, or both. We find neither a modality effect in favor of the audio condition nor a verbal redundancy effect in favor of using only a single modality rather than both. Taken together with earlier research, our findings suggest that the modality effect is difficult to apply reliably and computing educators should not rush to integrate audio into visualizations in expectation of the effect. We discuss theoretical viewpoints that future research should attend to; these include alternative part-explanations of the modality effect and attention-based models of working memory, among others.

Visual algorithm simulation (VAS) is a method used in teaching data structures and algorithms. In a VAS exercise a learner simulates the steps of an algorithm by interacting with data structure visualisations and receives feedback on the correctness of steps taken. A data format for storing VAS simulation traces would allow for later inspection of the process by instructors and researchers. In this study we describe the development of a prototype language for this purpose. The initial version was tested in a research project where the language was used for recording traces, which were later analyzed. The results were positive but also instructed some revisions in the data format and the requirements. We describe an iterative development process for extending and improving the language and the tooling. This is a work in progress: we will proceed with the data format specification, as well as further develop the technologies needed to use the data format in conjunction with VAS exercises.