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Background: The complexity and diversity of problems and concepts in different engineering subjects represent a great challenge for students. Traditional approaches to teaching statics are ineffective in helping some students overcome the learning barriers that underlie learning statics and developing problem-solving skills. Purpose: This article explores how self-explanation activities may support student learning in statics. Specifically, this study examines the characteristics of student self-explanations of worked examples and their relationship with students' conceptual change. Design/Method: The study population included 147 undergraduate engineering students enrolled in a statics course. The students wrote their self-explanations at each step of an incomplete or incorrect worked example in the context of static equilibrium. Students' self-explanations were qualitatively analyzed using content analysis to identify the approaches used. We used descriptive and inferential statistics to identify differences in students' conceptual understanding of statics, based on their approach to self-explanation. Results: We identified four self-explaining approaches: restricted explanations, elemental explanations, inferential explanations, and strategic explanations. After completing the activity, students who self-explained incomplete worked examples showed better results in the quality of their explanations and conceptual change than students in the incorrect worked example condition. Conclusions: The findings suggest a relationship between the type of worked example, students' approaches to self-explaining, and their conceptual change and problem-solving skills in statics. To increase the quality of the students' explanations and to improve their conceptual understanding, additional prompts or initial training in self-explaining may be required within the worked-examples context.

This paper describes the implementation and student learning outcomes of a nationwide professional development program for lower secondary and upper secondary school teachers to integrate computational thinking into the K-12 curriculum. Computational thinking comprises important concepts and skills that all students should develop to take an active role in a global society. However, teaching computational thinking is challenging. There are few teachers with the knowledge and skills to integrate computation into their courses. In this program, the participating teachers implemented a set of lesson plans that included both unplugged activities to scaffold student learning, and 'plugged' activities following a use-modify-create learning progression with the Micro:bit device to practice these skills. The study used a quasi-experimental design to compare students' level of computational thinking between the program participants and a control group. The results suggest a positive effect of the learning activities on student computational thinking knowledge and skills as compared to the control group. This result persists after controlling for school context and student gender. This study provides an explicit approach to implementing these activities in the context of a developing country and assesses their effectiveness in a large-scale study.

Worked examples can help novice learners develop early schemata from an expert's solution to a problem. Nonetheless, the worked examples themselves are no guarantee that students will explore these experts' solutions effectively. This study explores two different approaches to supporting engineering technology students' learning in an undergraduate introductory programming course: debugging and in-code commenting worked examples. In a Fall semester, students self-explained worked examples using in-code comments (n = 120), while in a Spring semester, students debugged worked examples (spring n = 101). Performance data included the midterm and final exams. Prior exposure to programming courses was taken from a survey at the beginning of each semester. Findings suggest that both the debugging and explaining forms of engagement with worked examples helped students with no prior programming experience to succeed in the course. For the worked examples to be effective, those need to be provided with some explicit form of engagement (i.e., debugging or self-explaining). Combining both strategies following explaining first and debugging second may result in a more effective approach.

Background and context: Transfer is a process where students apply their learning to different contexts. This process includes using their knowledge to solve problems with similar complexity, and in new contexts. In the context of programming, transfer also includes being able to understand and use different programming languages. Objective: This study explores: (a) student ability to transfer from a block-based programming language into another block-based programming language; (b) student ability to transfer from a block-based programming language to a text-based programming language; (c) student ability to transfer their learning within the same programming language; and (d) the difficulties students had to transfer in these contexts. Method: A group of students participating in a program called Coding For Kids explained three different programs in different programming languages during an interview protocol. The students used the programming language MakeCode, and worked on transfer activities in Scratch and Python. Findings: The results suggest that while most students are able to transfer between block-based programming languages, most of them struggle to explain a program in a text-based programming language, and to solve a new coding challenge. Implications: Instructional designers should consider different strategies to facilitate student transfer into professional programming languages, which is particularly difficult for non-English speakers.

BackgroundNiemann-Pick Disease Type C (NPC) is an inherited, often fatal neurovisceral lysosomal storage disease characterized by cholesterol accumulation in every cell with few known treatments. Defects in cholesterol transport cause sequestration of unesterified cholesterol within the endolysosomal system. The discovery that systemic administration of hydroxypropyl-beta cyclodextrin (HPβPD) to NPC mice could release trapped cholesterol from lysosomes, normalize cholesterol levels in the liver, and prolong life, led to expanded access use in NPC patients. HPβCD has been administered to NPC patients with approved INDs globally since 2009.ResultsHere we present safety, tolerability and efficacy data from 12 patients treated intravenously (IV) for over 7 years with HPβCD in the US and Brazil. Some patients subsequently received intrathecal (IT) treatment with HPβCD following on average 13 months of IV HPβCD. Several patients transitioned to an alternate HPβCD. Moderately affected NPC patients treated with HPβCD showed slowing of disease progression. Severely affected patients demonstrated periods of stability but eventually showed progression of disease. Neurologic and neurocognitive benefits were seen in most patients with IV alone, independent of the addition of IT administration. Physicians and caregivers reported improvements in quality of life for the patients on IV therapy. There were no safety issues, and the drug was well tolerated and easy to administer.ConclusionsThese expanded access data support the safety and potential benefit of systemic IV administration of HPβCD and provide a platform for two clinical trials to study the effect of intravenous administration of HPβCD in NPC patients.

This study implements a design-based research approach to design and evaluate different scaffolding strategies for supporting student learning as well as promoting student agency within a computational science course. The course introduces computational methods and tools in the context of disciplinary problems for materials science and engineering students. Initial course offerings suggested that students were overwhelmed by the interdisciplinary nature of the course. Therefore, the research team evaluated different scaffolding strategies for supporting students' learning, and how those may have provided students with agency to self-scaffold when needed. Three rounds of data collection included 17 students who participated in individual semi-structured interviews to explore how they used (or not) different scaffolds. Five of the participants were recruited for the first iteration; six of them were recruited in the second iteration, and six more in the third one. The iterative process allowed us to adapt the scaffolding procedures for the third iteration from the data collected in iterations 1 and 2. The purpose of this study is to understand how students used different scaffolds, and what implementation strategies were effective according to student uses of these scaffolds in the context of computational science. The results suggest that students developed agency to self-scaffold when needed, as they benefited from multiple scaffolds at different steps of the problem-solving process. Moreover, providing worked examples without engaging students in their active exploration can be ineffective, but this engagement can be achieved using written explanations. Additional support may be needed at an early stage of skill development, so students have an idea of how to validate their model.

Engineering design is a complex process both for students to participate in and for instructors to assess. Informed designers use the key strategy of conducting experiments as they test ideas to inform next steps. Conversely, beginning designers experiment less, often with confounding variables. These behaviours are not easy to assess in educational settings because they occur throughout the design process. This paper reports on a two-fold study carried out to test the model for identifying student behaviours during design experimentation. The first phase uses the process data from 48 middle-school students designing an energy-plus house. The study utilized learner interaction data sets collected through automatic, unobtrusive logging of student actions in a CAD platform. The analysis of learner process data is compared to student performance on an open-ended post-test. The second phase correlates the number of experiments students conducted to the quality of student prototypes. The results suggest that the proposed model can be used to identify, characterize, and assess student strategies associated with conducting experiments. Implications of this work are relevant to engineering and design educators as well as researchers interested in the role of learning analytics in studying complex processes.

Modeling is an important element of discovery and design processes because it can help individuals to comprehend and facilitate solutions to problems, mediate among mental and external representations, and off-load cognitive demands. However, engaging in model generation, comprehension, and transformation requires the orchestration of domain knowledge, meta-representational knowledge, and various reasoning processes. This study aims to understand the interplay between domain knowledge, meta-representational knowledge, and students' reasoning processes while engaging in modeling activity. Specifically, our goal is to investigate: (1) the types and quality of knowledge students used when performing modeling activities; and (2) the differences in types of knowledge used by students regarding their performance in a modeling and simulation challenge. Our qualitative analysis focused on verbal descriptions via a retrospective semi-structured interview and artifacts created by seventeen students after engaging in synthetic modeling activity. The resulting analysis reports overall quality for the types of domain and meta-representational knowledge that students used along with reasoning processes during synthetic modeling activity. The analysis also compared and contrasted the patterns of conceptual understanding and problem-solving skills between three groups, which differed in terms of modeling performance.

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.

Creating explanations is an important process for students, not only to make connections between novel information and background knowledge, but also to be able to communicate their understanding of any given topic. This article explores students' explanations in the context of computational science and engineering, an important interdisciplinary field that enables scientists and engineers to solve complex problems. Specifically, this study explores: (a) students' approaches to create written explanations of programing code and (b) the relationship between students' explanations and their ability to do computer programing. Students wrote in-code comments for 3 MATLAB® worked-examples, which were qualitatively analyzed using a coding scheme. Different approaches to self-explain were identified using hierarchical cluster analysis, and differences in students' ability to do computer programing were identified using analysis of variance. The resulting approaches to self-explain were: original solution, mechanistic, principle-based, limited, and goal-based. The findings suggest that experienced students wrote simple in-code comments to self-explain, but students with lower ability to do computer programing wrote more comprehensive explanations, as they may take this as a learning opportunity.

Computational tools and methods have permeated multiple science and engineering disciplines, because they enable scientists and engineers to process large amounts of data, represent abstract phenomena, and to model and simulate complex concepts. In order to prepare future engineers with the ability to use computational tools in the context of their disciplines, some universities have started to integrate these tools within core courses. This paper evaluates the effect of introducing three computational modules within a thermodynamics course on student disciplinary learning and self-beliefs about computation. The results suggest that using worked examples paired to computer simulations to implement these modules have a positive effect on (1) student disciplinary learning, (2) student perceived ability to do scientific computing, and (3) student perceived ability to do computer programming. These effects were identified regardless of the students' prior experiences with computer programming.

This paper studies electrical engineering learners' preferences for learning methods with various degrees of activity. Less active learning methods such as homework and peer reviews are investigated, as well as a newly introduced very active (constructive) learning method called "slectures," and some others. The results suggest that graduate students' perception of the usefulness of the activity increases with its level of activity. For undergraduate students, an increased perception of the usefulness of the activity was observed for lightly active but structured learning methods. Group-based analysis focusing on two types of learners, defined as "instructor-dependent" and "instructor-independent" according to their perception of the usefulness of the classroom lectures, was also performed. The results suggest that instructor-independent learners may benefit more from active learning methods than instructor-dependent learners. For example, instructor-independent undergraduate learners were found to perceive the homework assignment as being more useful than the lectures. Such a preference was not seen in the average group data. In fact, no learning method was found to be perceived as more useful than the lectures, on average. Thus this paper illustrates the pertinence of group-based data analysis.

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.