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This article presents two case studies aimed at exploring the use of self-explanations in the context of computational science and engineering (CSE) education. The self-explanations were elicited as students’ in-code comments of a set of worked-examples, and the cases involved two different approaches to CSE education: glass box and black box. The glass-box approach corresponds to a programming course for materials science and engineering students that focuses on introducing programming concepts while solving disciplinary problems. The black-box approach involves the introduction of Python-based computational tools within a thermodynamics course to represent disciplinary phenomena. Two semesters of data collection for each case study allowed us to identify the effect of using in-code comments as a self-explanation strategy on students’ engagement with the worked-examples and students’ perceptions of these activities within each context. The results suggest that the use of in-code comments as a self-explanation strategy increased students’ awareness of the worked-examples while engaging with them. The students’ perceived uses of the in-code commenting activities include: understanding the example, making a connection between the programming code and the disciplinary problem, and becoming familiar with the programming language syntax, among others.

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Engineering design is a complex process. The design process cannot be assessed based solely on a product or as a simple test because there is no single perfect design for a problem. An important design strategy is the conduction of experiments. Informed designers carry out experiments and use their outcomes to inform their next steps. On the other hand, beginning designers do little or no experiments, and the few experiments they do involve confounding variables. These behaviours that differentiate beginning and informed designers are not easy to assess in educational settings because they occur throughout the design process. This paper proposes and evaluates a model to analyze student interactions with a CAD tool in order to identify and characterize the different strategies students use to conduct experiments. A two-fold study is carried out to validate the model. The first phase uses the clickstream data of 51 middle school students working on a design project to create a net-zero energy house. The analysis of clickstream data is compared to a qualitative analysis of an open-ended posttest. The second phase correlates the number of experiments students did to the student prototype quality. The results suggest that the proposed model can be used to identify, characterize, and assess student strategies to conduct experiments.

Background Modeling and simulation practices have become commonplace in modern engineering, but in‐depth research on the development of modeling and simulation expertise is needed to identify the learning processes involved in successfully acquiring these skills. Purpose This study investigated student dimensions of expertise when engaged in modeling and simulation practices. The guiding research question was “How do students use cognitive and metacognitive knowledge when engaged with computational modeling and simulation practices?” Design/Method This study included 11 undergraduate engineering students enrolled in a course on computational materials science. Data were collected from one of five computational challenges administered during the course. Students' cognitive and metacognitive knowledge use was first analyzed using thematic analysis and then through a phenomenographic approach. Findings were interpreted using the lens of adaptive expertise. Results Results describe how students used their cognitive and metacognitive knowledge to approach the computational challenge. Two categories in the cognitive dimension were identified: implementation‐oriented and knowledge‐oriented. Two categories identified in the metacognitive dimension were plan‐oriented and action‐oriented approaches. These categories are further presented as an outcome space by aligning them with the dimensions of adaptive expertise. Conclusions Results indicated that students in the action/implementation‐oriented category exhibited many of the qualities expected of inexperienced novices, while students in the plan/knowledge‐oriented category demonstrated more of the qualities expected of adaptive experts. However, results were less conclusive for students in the two intermediate thematic categories, who often exhibited some but not all of the qualities of both novices and experts.

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