Patricia Aluko Obe

As software becomes increasingly important, so does the education of people who develop software. To provide a good and motivating education to learners, it is necessary to be aware of their preferences. To give insight into the preference of learners, we conducted a study with university students and professionals aimed at learning more about their preferences. Results from our survey- and focus group-based research show large similarities between both groups of learners, such as the preference for e-learning materials, as this provides them flexibility for scheduling their learning sessions. However, differences exist when it comes to the preferred scheduling of online and offline sessions. Our findings can help educators not only in designing courses for university students or professionals, but also in designing courses that can be used by both groups of learners.

Students’ experience is used in empirical software engineering research as well as in software engineering education to group students in either homogeneous or heterogeneous groups. To do so, students are commonly asked to self-rate their experience, as self-rated experience has been shown to be a good predictor for performance in programming tasks. Another experience-related measurement is participants’ confidence (i.e., how confident is the person that their given answer is correct). Hence, self-rated experience and confidence are used as selector or control variables throughout empirical software engineering research and software engineering education. In this paper, we analyze data from several student experiments conducted in the past years to investigate whether self-rated experience and confidence are also good predictors for students’ performance in model comprehension tasks. Our results show that while students can somewhat assess the correctness of a particular answer to one concrete question regarding a conceptual model (i.e., their confidence), their overall self-rated experience does not correlate with their actual performance. Hence, the use of the commonly used measurement of self-rated experience as a selector or control variable must be considered unreliable for model comprehension tasks.

Cyber-physical systems interact closely at runtime with their operational context, i.e., other systems, users, and external entities. Therefore, eliciting and documenting interactions with the operational context allows documenting rationales for design decisions, which is essential to define a system architecture that suits the system's purpose. For cyber-physical systems acting in system networks, this task becomes challenging since these networks can change at runtime, because individual systems can leave or join a network spontaneously. Hence, it becomes impossible to predict at design time the operational contexts at runtime. To aid context analysis and specification of such systems, this paper contributes a solution approach where the context of a system is not defined based on a static system boundary but depends on the specific intended use. We propose a view-centric perception of the operational context to leverage the functionality of external systems. This allows differentiating between functionality offered by the cyber-physical system network as a whole and the functionality provided by each individual system. An evaluation with an industrial case example and industry collaboration shows that this approach allows making suitable assumptions of the operational context at design time to guide architecture decisions.