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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.

This working group concerns the adoption of computing education (CE) in undergraduate computer science (CS) programmes. Such adoption requires both arguments sufficient to persuade our departmental colleagues and our education committees, and also curricular outlines to assist our colleagues in delivery. The goal of the group is to develop examples of both arguments and curricular outlines, drawing on any prior experience available.

Students are able to produce correctly functioning program code even though they have a fragile understanding of how it actually works. Questions derived automatically from individual exercise submissions (QLC) can probe if and how well the students understand the structure and logic of the code they just created. Prior research studied this approach in the context of the first programming course. We replicate the study on a follow-up programming course for engineering students which contains a recap of general concepts in CS1. The task was the classic rainfall problem which was solved by 90% of the students. The QLCs generated from each passing submission were kept intentionally simple, yet 27% of the students failed in at least one of them. Students who struggled with questions about their own program logic had a lower median for overall course points than students who answered correctly.

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.

We propose a framework for identifying, organizing, and communicating learning objectives that involve program semantics. In this framework, detailed learning objectives are written down as rules of program behavior (RPBs). RPBs are teacher-facing statements that describe what needs to be learned about the behavior of a specific sort of programs. Different programming languages, student cohorts, and contexts call for different RPBs. Instructional designers may define progressions of RPB rulesets for different stages of a programming course or curriculum; we identify evaluation criteria for RPBs and discuss tradeoffs in RPB design. As a proof-of-concept example, we present a progression of rulesets designed for teaching beginners how expressions, variables, and functions work in Python. We submit that the RPB framework is valuable to practitioners and researchers as a tool for design and communication. Within computing education research, the framework can inform, among other things, the ongoing exploration of “notional machines” and the design of assessments and visualizations. The theoretical work that we report here lays a foundation for future empirical research that compares the effectiveness of RPB rulesets as well as different methods for teaching a particular ruleset.

Full research paper-Team configuration, work practices, and communication have a considerable impact on the outcomes of student software projects. This study observes 150 college students who first individually solve exercises and then carry out a class project in teams of three. All projects had the same requirements. We analyzed how students' behavior on individual pre-project exercises predict team project outcomes, investigated how students' time management practices affected other team members, and analyzed how students divided their work among peers. Our results indicate that teams consisting of only low-performing students were the most dysfunctional in terms of workload balance, whereas teams with both low-and high-performing students performed almost as well as teams consisting of only high-performing students. This suggests that teams should combine students of varying skill levels rather than allowing teams with only low performers or letting students to form teams without constraints. We also observed that individual students' poor time management practices impair their teammates' time management. This underlines the importance of encouraging good time management practices. Most teams reported that they divided tasks in a way that is beneficial for the acquisition of technical skills rather than collaboration and communication skills. Only a few teams assigned tasks so that students would have worked only on tasks they already knew and thus felt most comfortable to work with.

Visual algorithm simulation (VAS) is an activity in which students practice their understanding of algorithms: They simulate the steps of an algorithm by manipulating a bespoke visualization within a supporting software system. Multiple instances of a VAS exercise may be automatically generated for different learners or for repetitive practice by a single learner. In this work-in-progress report, we discuss how misconceptions might be automatically detected in students' solutions to VAS exercises and how misconception-aware feedback might be provided in VAS. We identify two strategies for producing multiple instances of VAS exercises and evaluate them against several criteria. The tradeoffs so identified are also pertinent for similar process-simulation practice beyond VAS and computing education.

We describe how our Finnish talking head was improved by using a new auditory speech synthesis method based on neural networks and optimal synchronization of the facial speech animation and the audio signal. In our first version of the talking head, the user typed in text and synthesized auditory speech and synchronized facial animation were created automatically. We combine a 3D facial model with a commercial auditory text-to-speech synthetizer (TTS). The auditory speech is produced by concatenating pre-recorded samples of natural speech according to a set of rules. The quality of the current speech synthesis is not yet adequate. A new strategy has been developed to improve the TTS and to integrate auditory synthesizer synchronization, especially when hardware capabilities are limited. We are developing a new method to achieve an optimal synchronization, independent of the platform used. This method is based on predictive visual synthesis. The new synchronization method gives us better control over audio-visual speech synthesis in the time domain. Using the diphone duration, we can use a more realistic interpolation function between the visemes. Thus, we can also take into account coarticulation effects.