Michael Goedicke

Janine Langerbein, Till Massing, Jens Klenke et al.

Due to the precautionary measures during the COVID-19 pandemic many universities offered unproctored take-home exams. We propose methods to detect potential collusion between students and apply our approach on event log data from take-home exams during the pandemic. We find groups of students with suspiciously similar exams. In addition, we compare our findings to a proctored control group. By this, we establish a rule of thumb for evaluating which cases are "outstandingly similar", i.e., suspicious cases.

Nils Schwinning, Michael Striewe, Till Massing et al.

Web-based systems for assessment or homework are commonly used in many different domains. Several studies show that these systems can have positive effects on learning outcomes. Many research efforts also have made these systems quite flexible with respect to different item formats and exercise styles. However, there is still a lack of support for complex exercises in several domains at university level. Although there are systems that allow for quite sophisticated operations for generating exercise contents, there is less support for using similar operations for evaluating students' input and for feedback generation. This paper elaborates on filling this gap in the specific case of statistics. We present both the conceptional requirements for this specific domain as well as a fully implemented solution. Furthermore, we report on using this solution for formative and summative assessments in lectures with large numbers of participants at a big university.

Pontus Johnson, Paul Ralph, Mathias Ekstedt et al.

Background: Philosophers of science including Collins, Feyerabend, Kuhn and Latour have all emphasized the importance of consensus within scientific communities of practice. Consensus is important for maintaining legitimacy with outsiders, orchestrating future research, developing educational curricula and agreeing industry standards. Low consensus contrastingly undermines a field's reputation and hinders peer review. Aim: This paper aims to investigate the degree of consensus within the software engineering academic community concerning members' implicit theories of software engineering. Method: A convenience sample of 60 software engineering researchers produced diagrams describing their personal understanding of causal relationships between core software engineering constructs. The diagrams were then analyzed for patterns and clusters. Results: At least three schools of thought may be forming; however, their interpretation is unclear since they do not correspond to known divisions within the community (e.g. Agile vs. Plan-Driven methods). Furthermore, over one third of participants do not belong to any cluster. Conclusion: Although low consensus is common in social sciences, the rapid pace of innovation observed in software engineering suggests that high consensus is achievable given renewed commitment to empiricism and evidence-based practice.