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Reflective practice is becoming a more necessary skill across all disciplines and industry sectors. This study describes the reflective processes capstone engineering students engaged with as part ...

This study investigates the relationship between two related computational thinking practices: data practices and computational problem-solving practices in acquiring related computational thinking practices during a first-year undergraduate engineering course. While computational thinking theory is still developing, empirical studies can help further understand how students demonstrate this knowledge and their progression in attaining the practices. Therefore, with this empirical study, the following questions are addressed. RQ1: What are the differences in students’ computational thinking practices at the beginning of an undergraduate introductory programming course? RQ 2: How do these differences correspond to the acquisition of more advanced computational thinking practices? The use of a descriptive non-experimental design that aims to understand the correlation between practices related to data and computational problem-solving is presented. A machine learning technique is employed, utilizing historical data from introductory programming for a problem-solving course with more than 1000 first-year engineering students. After identifying groups of students defining different profiles, data from posterior performance in more advanced programming topics were descriptively analyzed. This study supports the characterization of four different student profiles demonstrating differences in their performance at the beginning of the semester. From these four profiles, two of them show a subsequent differential progression besides their similarity at the beginning of the semester. In this particular case, troubleshooting and debugging appear as a relevant competency when distinguishing these two learners’ groups. These findings suggest that previous knowledge or exposure to different practices can result in different progressions of more complex computational practices, emphasizing the relevance of troubleshooting and debugging as a practice required for a successful and timely progression on the acquisition of other computational thinking practices.

The current study investigates the differences in students’ explanations of friction concepts after the use of the visuohaptic simulation with two different of sequenced approaches: enhanced visual first to enhanced visual and haptic feedback second (V → V + H), and haptic enhanced first to enhanced visual and haptic feedback second (H → V + H). Participants were students from a midwestern university who participated as part of their physics course assignments during the Fall of 2017 (n = 29). Participants first received a lecture about friction concepts, followed by the pretest. Then, they participated in a laboratory session where they interacted with the visuohaptic simulation and completed the posttest. Participants answered two conceptual questions at the different stages of the study, the first conceptual question was regarding the role of the objects’ weight in friction, and the second conceptual question was regarding the role of the objects’ size in friction. Results suggest three major findings: (a) the visuohaptic simulation had a positive influence in students’ conceptual knowledge; (b) students in the H → V + H sequenced approach outperformed the students in the V → V + H approach; and (c) the role of the object weight in friction resulted in an intuitive concept for the students, while the role of the object size in friction resulted in a counterintuitive concept for the students. Possible explanations of our findings are further discussed.

In science and engineering education, the use of heuristics has been introduced as a way of understanding the world, and as a way to approach problem-solving and design. However, important consequences for the use of heuristics are that they do not always guarantee a correct solution. Learning by Design has been identified as a pedagogical strategy that can guide individuals to properly connect science learning via design challenges. Specifically, we focus on the effect of simulation-enabled Learning by Design learning experiences on student-generated heuristics that can lead to solutions to problems. A total of 318 middle school students were exposed to a lesson that integrated design practices in the context of energy consumption and energy conservation considerations when designing buildings using an educational CAD tool. The students were pre- and posttested before and after the 2-week long intervention. The data analysis procedures combined qualitative with quantitative methods along with machine learning approaches. Our analysis revealed two distinct groups of students based on their learning achievement: the naive developing heuristic group and semi-knowledgeable fixated heuristic group. Differences between the groups are discussed in terms of performance, as well as implications for the use of computer simulations to improve student learning.

Experimentation is one of the important strategies used in engineering design to understand the relationship between relevant variables so that they can be manipulated to generate optimized solution for a particular problem or design. The understanding of students’ experimentation strategies allows educators to help students improve their design experiments by providing scaffolds or guidance. The purpose of this study is to investigate students’ experimentation strategies while they work on a design challenge. We performed a concurrent think-aloud to capture students’ verbal description of their actions while they designed using a CAD tool. Using mainly the think-aloud transcripts, we identified and characterized patterns of students’ experimentation strategies. In this study, we were able to identify four main activities whose combinations resulted in five different experimentation strategies performed by students, along with their explanation of their actions. Implications of this study relate to scientific argumentation, learning support, design of practical learning activities, and teacher encouragement.

Much can be learned from the vast work on the use of computer simulations for inquiry learning for the integration of modeling and simulation practices in engineering education. This special issue presents six manuscripts that take steps toward evidence-based teaching and learning practices. These six studies present learning designs that align learning objectives, with evidence of the learning, and pedagogy. Here we highlight the main contributions from each paper individually, but also themes identified across all of them. These themes include (a) approaches for modeling-and-simulation-centric course design; (b) teaching practices and pedagogies for modeling and simulation implementation; and (c) evidence of learning with and about modeling and simulation practices. We conclude our introduction by highlighting desirable characteristics of studies that report on the effectiveness of modeling and simulation in engineering education, and with that we provide some recommendations for improving the scholarship of teaching and learning in this field.

Engineering design thinking is hard to teach and still harder to learn by novices primarily due to the undetermined nature of engineering problems that often results in multiple solutions. In this paper, we investigate the effect of teaching engineering design thinking to freshmen students by using a computer-aided Design (CAD) simulation software. We present a framework for characterizing different levels of engineering design thinking displayed by students who interacted with the CAD simulation software in the context of a collaborative assignment. This framework describes the presence of four levels of engineering design thinking—beginning designer, adept beginning designer, informed designer, adept informed designer. We present the characteristics associated with each of these four levels as they pertain to four engineering design strategies that students pursued in this study—understanding the design challenge, building knowledge, weighing options and making tradeoffs, and reflecting on the process. Students demonstrated significant improvements in two strategies—understanding the design challenge and building knowledge. We discuss the affordances of the CAD simulation tool along with the learning environment that potentially helped students move towards Adept informed designers while pursuing these design strategies.

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 mixed-methods sequential explanatory design investigates disciplinary learning gains when engaging in modeling and simulation processes following a programming or a configuring approach. It also investigates the affordances and challenges that students encountered when engaged in these two approaches to modeling and simulation. © 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:352–375, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21804

Electromagnetism is an umbrella encapsulating several different concepts like electric current, electric fields and forces, and magnetic fields and forces, among other topics. However, a number of studies in the past have highlighted the poor conceptual understanding of electromagnetism concepts by students even after instruction. This study aims to identify novel forms of “hands-on” instruction that can result in representational competence and conceptual gain. Specifically, this study aimed to identify if the use of visuohaptic simulations can have an effect on student representations of electromagnetic-related concepts. The guiding questions is How do visuohaptic simulations influence undergraduate students’ representations of electric forces? Participants included nine undergraduate students from science, technology, or engineering backgrounds who participated in a think-aloud procedure while interacting with a visuohaptic simulation. The think-aloud procedure was divided in three stages, a prediction stage, a minimally visual haptic stage, and a visually enhanced haptic stage. The results of this study suggest that students’ accurately characterized and represented the forces felt around a particle, line, and ring charges either in the prediction stage, a minimally visual haptic stage or the visually enhanced haptic stage. Also, some students accurately depicted the three-dimensional nature of the field for each configuration in the two stages that included a tactile mode, where the point charge was the most challenging one.

In this paper, a map of the state of the art of recent medical simulators that provide evaluation and guidance for surgical procedures is performed. The systems are reviewed and compared from the viewpoint of the used technology, force feedback, learning evaluation, didactic and visual aid, guidance, data collection and storage, and type of solution (commercial or non-commercial). The works’ assessment was made to identify if—(1) current applications can provide assistance and track performance in training, and (2) virtual environments are more suitable for practicing than physical applications. Automatic analysis of the papers was performed to minimize subjective bias. It was found that some works limit themselves to recording the session data to evaluate them internally, while others assess it and provide immediate user feedback. However, it was found that few works are currently implementing guidance, aid during sessions, and assessment. Current trends suggest that the evaluation process’s automation could reduce the workload of experts and let them focus on improving the curriculum covered in medical education. Lastly, this paper also draws several conclusions, observations per area, and suggestions for future work.

Background The computational simulations used by the instructors in this study were originally developed for use as research tools by subject-matter experts and then incorporated in advanced undergraduate and graduate courses in engineering and science. Although some research has been done on students' learning with these computational simulations, less progress has been made toward understanding instructors' goals, or affordances, for incorporating these simulations in their teaching. Purpose (Hypothesis) To identify how computational simulations can be effectively used in teaching and learning environments, this study examined instructors' rationale for using these simulations as learning tools. The study was based on the following research question: What were the intended learning outcomes that guided the instructors' use of computational simulations as learning tools? Design/Method This study used qualitative methods based on the theoretical framework of phenomenography. Openended interviews were conducted with 14 instructors teaching undergraduate and graduate courses in science and engineering who were not familiar with the research literature on beneficial ways of using simulations for learning and instruction. Results Analysis revealed an outcome space consisting of eight qualitatively different categories that detailed ways in which the engineering and science instructors in this study conceptualized the use of simulation tools with learning activities into existing courses they taught. Conclusions The outcome space of instructors' goals for using computational simulations is consistent with the recommendations found in the literature based on studies of the use of simulations in more restricted research settings.

To meet the urgent need for a well-prepared CS&E workforce, we must better understand how students perceive the field.