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This paper presents a systematic literature review of professional development programs in computational thinking (CT). CT has emerged as an essential set of skills that everyone should develop to participate in a global society. However, there were no pre-service or in-service teacher programs to integrate CT into the K-12 classrooms until very recently. Thus, it is important to identify how educators and researchers address the challenges to prepare the next generation of students and what gaps persist in the current literature. We review existing work in this field from two perspectives: First, we analyze the learning outcomes, assessment methods, pedagogical approaches, and pedagogical tools used in the professional development programs in CT. Second, we examine how these programs assess the teachers' knowledge and skills as outcomes. We used the technological pedagogical and content knowledge (TPACK) framework to characterize existing literature and identify possible gaps in the preparation of pre-service and in-service teachers in CT. Our results suggest that (1) existing evidence is limited to developed countries; (2) many studies are only focusing on teachers understanding the concepts but do not explore how the participants evaluate or create learning activities; (3) no studies look into classroom observations as part of the program, which limits our understanding to how these programs work; and (4) most programs use block-based programming languages as the tool to develop student CT. While block-based programming languages are used for introductory training programs, students are often expected to transfer their learning to more professional programming languages.

Quantum physics forms the basis for exciting new technologies, including quantum computers, quantum encryption, and quantum entanglement. The advancement of science and technology highlights the importance of mastering quantum physics and its applications, not only at the college level but also as early as high school. In this multiple case study, we investigated first- and second-year undergraduate college students' models of single and multi-electron atoms after completing a modern mechanics course, which addressed basic QM topics. The students' models were categorised into four primary categories: the discrete entity (particle) model, hybrid model, quantum-like model, and quantum model, and subcategories for atom structure and electron attribution and motion. Despite being introduced to quantum concepts in high school and first-year undergraduate calculus-based physics classes, many students constructed incomplete, inaccurate, or incoherent models. Seven of eight students' atomic structure models and six of eight students' electron attribution and motion models were classified as discrete entity and hybrid models. The findings of this study reveal students' conceptual challenges in explaining the structure of single and multiple-electron atoms and shed light on the factors contributing to the persistent difficulties in their understanding of atomic structure in the realm of quantum mechanics.

Background: This study posits that scaffolded team-based computational modeling and simulation projects can support model-based learning that can result in evidence of representational competence and regulatory skills. The study involved 116 students from a second-year thermodynamics undergraduate course organized into 24 teams, who worked on three two-week-long team-based computational modeling and simulation projects and reflected upon their experience. Results: Results characterized different levels of engagement with computational model-based learning in the form of problem formulation and model planning, implementation and use of the computational model, evaluation, and interpretation of the outputs of the model, as well as reflection on the process. Results report on students' levels of representational competence as related to the computational model, meaning-making of the underlying code of the computational model, graphical representations generated by the model, and explanations and interpretations of the output representations. Results also described regulatory skills as challenges and strategies related to programming skills, challenges and strategies related to meaning-making skills for understanding and connecting the science to the code and the results, and challenges and strategies related to process management mainly focused on project management skills. Conclusion: Characterizing dimensions of computational model-based reasoning provides insights that showcase students' learning, benefits, and challenges when engaging in team-based computational modeling and simulation projects. This study also contributes to evidence-based scaffolding strategies that can support undergraduate students' engagement in the context of computational modeling and simulation.

Contribution: The study characterizes aspects of cognitive and metacognitive dimensions of team cognition of software development teams in educational settings. Background: The software development industry requires software engineers and developers to work in teams; for this, there is substantial research on teamwork in the context of the organization. However, little is known about it in the context of educational settings, where there is scant research on teamwork in engineering and computing projects. Research Questions: How do students enact teamwork cognitive engagement and metacognitive regulation in the context of a systems analysis and design course? Methodology: The participants were 127 undergraduate students in a systems development course organized into 26 teams. Qualitative categories were derived from a content analysis based on recorded teamwork sessions, which were then quantized and visualized for pattern identification. Findings: Results provide each team's overall cognitive engagement coefficient and metacognitive regulation coefficient. The findings also describe three clusters, each with a description of a selected team to provide further insights into the identified patterns.

Fostering productive engagement within teams has been found to improve student learning outcomes. Consequently, characterizing productive and unproductive time during teamwork sessions is a critical preliminary step to increase engagement in teamwork meetings. However, research from the cognitive sciences has mainly focused on characterizing levels of productive engagement. Thus, the theoretical contribution of this study focuses on characterizing active and passive forms of engagement, as well as negative and positive forms of engagement. In tandem, researchers have used computer-based methods to supplement quantitative and qualitative analyses to investigate teamwork engagement. Yet, these studies have been limited to information extracted primarily from one data stream. For instance, text data from discussion forums or video data from recordings. We developed an artificial intelligence (AI)-based automatic system that detects productive and unproductive engagement during live teamwork sessions. The technical contribution of this study focuses on the use of three data streams from an interactive session: audio, video, and text. We automatically analyze them and determine each team's level of engagement, such as productive engagement, unproductive engagement, disengagement, and idle. The AI-based system was validated based on hand-coded data. We used the system to characterize productive and unproductive engagement patterns in teams using deep learning methods. Results showed that there were > 91% prediction accuracy and < 7% mismatches between predictions for the three engagement detectors. Moreover, Pearson's r values between the predictions of the three detectors were > 0.844. On a scale of -1 (unproductive engagement) to 1 (productive engagement), the scores for all teams were 0.94 ± 0.04, suggesting high productive engagement. In addition, teams tended to mostly be in productive engagement before transitioning to disengagement (> 90.34% of the time) and to idle (> 93.69% of the time). Before transitioning to productive engagement, we noticed almost equal fractions of teams being in idle and disengagement modes. These results show that the system effectively detects engagement and can be a viable tool for characterizing productive and unproductive engagement patterns in teamwork sessions.

Background: For almost two decades, students with intellectual, developmental and multiple disabilities have attended undergraduate classes at the University of Manitoba, in Winnipeg, Canada as part-time auditing students. They are supported by the Campus Life program. Campus Life employs graduate and undergraduate university students as academic tutors and social role models because they too are immersed in university life. Methods: We chose collaborative autoethnography as a research method because we believe in researching and writing with individuals with disabilities, rather than about them. Three student tutor dyads elected to participate in this study. In the context of the pandemic and the switch from on-campus in-person learning to off-campus online learning, we were interested in learning about the changes, challenges or unanticipated benefits they experienced. Each dyad engaged in tutor-led conversations, guided by open-ended questions, and wrote their story. Findings: The stories from the three dyads were received and analysed. We found three emergent themes. The first was centred on worries about the health risks of COVID-19, the difficulties they experienced in adapting to online learning and their development of better skills in listening and communicating. The second theme was about the dramatic reduction in their opportunities for social engagement. With respect to the third emergent theme, they recounted some advantages of online learning, noting that it was more accessible, more convenient and safer during a pandemic. Conclusion: For the study participants the shift to online classes and tutoring sessions was surprisingly successful. They became more independent and improved their online communication, time-management and organisational skills. Social engagement was sorely missed, especially studying and socializing with classmates at university and participating in extracurricular activities. The students also noted some advantages to online learning such as accessibility, convenience, feeling comfortable at home, avoiding transportation to the university and learning to use new tech tools. The tutors began sharing and learning from each other. This was facilitated by online tutoring roundtables. Our first insight from this study is the realisation that we now have a new medium for tutoring Campus Life students. A second is the need to better understand student social engagement in the online world.

Information technology professionals are required to possess both technical and professional skills while functioning in teams. Higher education institutions are promoting teamwork by engaging students in cooperative and project-based learning environments. We characterized teams based on their collective orientations and evaluated their team performance in a cooperative project-based learning environment situated in a sophomore-level systems analysis and design course. We explored the orientation patterns in terms of goals, roles, processes, and interpersonal relations (GRPI). Specifically, we analyzed team retrospectives of 23 teams using a mixed-method approach. Findings characterized teams into balanced and unbalanced orientations. Teams with balanced orientations demonstrated a higher level of team performance in terms of academic achievement than the unbalanced category.

The discipline of data science is gaining attention due to a significant increase in the amount of data generated on a real-time basis. Despite the efforts from the institutions and national agencies, it has been witnessed that there have been substantial challenges in retaining and attracting students in the discipline of data science. Learning communities have been identified as effective mechanisms to improve student retention. However, fewer studies focus on student experiences within the context of a Data Science Learning Community. This study intends to investigate the student experiences within a Data Science Learning Community known as the The Data Mine (TDM) through the lens of Communities of Practice (CoP) and explore the contribution of the three tenets of CoP: "domain," "community," and "practice" in shaping student experiences. The study used a mixed-method case study design to understand the experiences of first-year students within TDM and identify the benefits and challenges associated with participating in the living-learning community. The findings of the study revealed that students considered participating in the learning community a valuable experience as it helped them collaborate with like-minded peers and mentors, also helped them develop data science skills.

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: Effectively facilitating teamwork experiences, particularly in the context of large-size courses, is difficult to implement. This study seeks to address the challenges of implementing effective teamwork experiences in large courses. This study integrated teamwork pedagogy to facilitate a semester-long project in the context of a large-size class comprising 118 students organized into 26 teams. The data for this study were collected from two online teamwork sessions when teams collaborated and self-recorded during the in-class time. The video recordings were qualitatively analyzed to identify patterns in team dynamics processes through visualizations. The study aims to provide insights into the different ways team members engaged in team dynamics processes during different phases of the semester. Results: Findings suggest that members of teams were mostly active and passive during meetings and less constructive and interactive in their engagement. Team members mainly engaged in communication, team orientation, and feedback behaviors. Over time, team members' interactions with one another remained about the same, with feedback behaviors tending to diminish and coordination behaviors staying about the same or slightly increasing over time. Conclusion: The implications of this study extend to both practice and theory. Practically, combining cooperative learning and scrum practices enabled a blend of collaborative and cooperative work, which suggests providing teams with tools and structures to coordinate teamwork processes and promote interaction among team members. From a theoretical perspective, this study contributes to the understanding of temporal aspects of teamwork dynamics by examining how team interactions evolve during working sessions at different points in time. Overall, this research provides valuable insights for educators, practitioners, and researchers aiming to enhance teamwork experiences in large courses, particularly in software development disciplines.

In this article, we explore how information, communications, and computational technology, or computer technology for short, influences the way that engineering is taught and learned. The goal of our analysis it to contribute towards a research, adoption, and policy agenda for propagating the effective use of computer technology in engineering education and for avoiding pitfalls associated with the connectivity that this technology enables. We seek to inform action and generate conversation amongst instructors, students, researchers, administrators, policy makers, and other key stakeholders in engineering education. We organize our discussion through six main issues as the focus for systemic change for the effective integration of technology in engineering education. These issues were identified through a Delphi study with a group of engineering education experts. Our analysis of the issues then draws on major policy reports of the use of technology in education and the extant research literature from engineering education and science education as well as technology studies and science studies. Discussion of each of these issues leads to a summary set of recommendations. Specifically, we address the role of technology in learning engineering, including both technologies developed specifically for learning engineering (learning innovations) and domain-specific computer technologies for engineering practice (computational tools). We next address technology-related issues around instructional design including learning outcomes, assessment, and instructional practice. We include discussion of professional development that better prepares faculty to effectively use technology in the classroom. Finally, we outline the broader ways technology interacts with the work of engineering students and faculty at the systems level - for better and for worse.

There is growing awareness of the need for mathematics and computing to quantitatively understand the complex dynamics and feedbacks in the life sciences. Although several institutions and research groups are conducting pioneering multidisciplinary research, communication and education across fields remain a bottleneck. The opportunity is ripe for using education research-supported mechanisms of cross-disciplinary training at the intersection of mathematics, computation, and biology. This case study uses the computational apprenticeship theoretical framework to describe the efforts of a computational biology lab to rapidly prototype, test, and refine a mentorship infrastructure for undergraduate research experiences. We describe the challenges, benefits, and lessons learned, as well as the utility of the computational apprenticeship framework in supporting computational/math students learning and contributing to biology, and biologists in learning computational methods. We also explore implications for undergraduate classroom instruction and cross-disciplinary scientific communication.

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.

Software modeling is an integral practice for software engineers, especially as the complexity of software solutions increases. Unified Modeling Language (UML) is the industry standard for software modeling. however, it is often used incorrectly and misunderstood by novice software designers. This study is centered around understanding patterns of student proficiency of abstraction and systems thinking within a software modeling context. The participants of this study (n = 97) belonged to a systems analysis and design course that is primarily taken by second-year university students. The exam solutions to a case study from the course were evaluated for modeling proficiency. As evidence of proficiency in abstract thinking and systems thinking, we evaluated UML activity diagrams, class diagrams, and sequence diagrams and the alignment between these representations in terms of functions, structures, and behaviors. The results suggest students being proficient in modeling the functional aspects of an information system while facing some difficulty in capturing the structural and behavioral aspects of an information system. Clustering analysis revealed two groups within the sample, with one group displaying a significantly higher abstraction and systems thinking ability. Statistically significant correlations were also found between student proficiency of abstraction and their modeling proficiency in terms of functions, structures, and behaviors.

This multiple case study focused on the implementation of a computer-aided design (CAD) simulation to help students engage in engineering design to learn science concepts. Our findings describe three case studies that adopted the same learning design and adapted it to three different populations, settings, and classroom contexts: at the middle-school, high-school, and pre-service teaching levels. Although the classroom orchestration of the particular learning design was customised for specific audiences and contexts, findings from this study suggest that the core components of the learning design, such as content, assessment, and pedagogy, and their alignment among them, resulted in students' learning. Specifically, results from a pre-post science assessment suggest that the three student groups arrived at similar understanding post-intervention levels, along with a significant aggregate growth in their scientific understanding. Regarding design performance, students in different groups demonstrated different levels of success in meeting design constraints. The findings also suggest that students' success rate in meeting the design constraints directly influenced their final design performance, where middle-school students had better performance than students in the other groups. That is, across the board, students increased their conceptual understanding of heat transfer, Earth, and solar science and were able to produce feasible designs. Implications of the study include how learning experiences with engineering and science simulations should be designed so that teachers can adopt and adapt materials for their specific audiences, contexts, and settings.

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 of modelling-based learning assignments over the entire course of a semester. Students answered multiple reflection questions asking them about planning, monitoring, and evaluating their performance during the modelling-based learning exercises. A rubric was used to quantify the student performance on the reflection assignments and clustering analysis to identify and characterise different student groups. Thematic analysis was then performed on students' reflections within the different clusters to understand the qualitative differences in student reflective practices. Our results uncovered two groups of students with similar patterns in their reflective practices, which we called active and inactive reflectors. The implications of the study include instructional approaches that can give students repeated practice and encourage them to engage in active reflection behaviours.

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 [right arrow] V + H), and haptic enhanced first to enhanced visual and haptic feedback second (H [right arrow] 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 [right arrow] V + H sequenced approach outperformed the students in the V [right arrow] 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.

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.

Latino first-generation college students face a unique set of challenges that are not part of the college experience of their non-first-generation counterparts. Nonetheless, many Latinos remain optimistic about overcoming barriers that might impede their educational pursuits. The present study was aimed at exploring factors that contribute to Latino students' positive expectations for degree attainment. Utilizing data from a large online survey administered at a four-year Hispanic Serving Institution (HSI), we found that competency beliefs and persistent determination to overcome challenges significantly influences students' optimism. In addition, strong connections with institutional agents (largely, faculty and student affairs staff) emerged as significant contributing factors. Students who reported having close relationships with their parents also reported a greater degree of optimism about postsecondary success. Implications for programs and services to improve the college experiences of Latino first-generation student, enhance optimism, and increase retention are discussed.

In this article we argue that the practice of requisite criminal/ized history disclosure in college admissions enhances carceral state power. Situating institutions of higher education as key sites through which surveillance is practiced, we traced the available evidence regarding targeted punishment in the US and the ramifications of a criminal record. We contend that this admissions practice constitutes a renewed form of racial exclusion and positions institutions of higher education as directly complicit in the US carceral state. It is only through carcerality that requiring criminal/ized history in college admissions can be seen as a rational practice.

Contribution: Knowledge of students' Habits of Mind in a signal processing course, and a method for education research. The method identifies factors that may influence students' performance, but are not evident when analyzing agglomerated data; it is an alternative to the traditional case study method as it derives the cases after applying a clustering approach. Background: Habits of Mind refer to mathematical, logical, and attitudinal modes of thinking required for students of science, mathematics, technology, and engineering to become effective problem solvers, capable of transferring such modes to new contexts. These are particularly relevant in a signal processing course in which students must learn to address engineering problems using tools and techniques previously acquired in an abstract context (mathematics). Research Questions: 1) What are the different Habits of Mind patterns exhibited by the students? 2) Are some of these patterns associated with differences in course grades? Methodology: A qualitative method is combined with random signal modeling and machine learning. Students' work is first annotated manually based on a custom-built rubric of Habits of Mind. Data is modeled and clustered to obtain statistically significant patterns of Habits of Mind corresponding to divisions of the students into groups. Findings: The data obtained suggests that the student group is inhomogeneous in terms of their Habits of Mind, and this inhomogeneity is associated with grade differences. In particular, the course grade is found to be dependent on inhomogeneity based on at least two Habits of Mind: 1) computation and estimation and 2) values and attitudes.

Virtual learning environments can now be enriched not only with visual and auditory information, but also with tactile and kinesthetic feedback. However, the way to successfully integrate haptic feedback on a multimodal learning environment is still unclear. This study aims to provide guidelines on how visuohaptic simulations can be implemented effectively, thus the research question is: Under what conditions do visual and tactile information support students' development of conceptual learning of force-related concepts? Participants comprised 170 undergraduate students of a Midwestern University enrolled in a physics for elementary education class. Four experiments were conducted using four different configurations of multimodal learning environments: Visual feedback only, haptic force feedback only, visual and haptic force feedback at the same time, and sequenced modality of haptic feedback first and visual feedback second. Our results suggest that haptic force feedback has the potential to enrich learning when compared with visual only environments. Also, haptic and visual modalities interact better when sequenced one after another rather than presented simultaneously. Finally, exposure to virtual learning environments enhanced by haptic forced feedback was a positive experience, but the ease of use and ease of interpretation was not so evident.

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.

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.

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.

Bioinformatics education can be broadly defined as the teaching and learning of the use of computer and information technology, along with mathematical and statistical analysis for gathering, storing, analyzing, interpreting, and integrating data to solve biological problems. The recent surge of genomics, proteomics, and structural biology in the potential advancement of research and development in complex biomedical systems has created a need for an educated workforce in bioinformatics. However, effectively integrating bioinformatics education through formal and informal educational settings has been a challenge due in part to its cross-disciplinary nature. In this article, we seek to provide an overview of the state of bioinformatics education. This article identifies: 1) current approaches of bioinformatics education at the undergraduate and graduate levels; 2) the most common concepts and skills being taught in bioinformatics education; 3) pedagogical approaches and methods of delivery for conveying bioinformatics concepts and skills; and 4) assessment results on the impact of these programs, approaches, and methods in students' attitudes or learning. Based on these findings, it is our goal to describe the landscape of scholarly work in this area and, as a result, identify opportunities and challenges in bioinformatics education.

This article investigates the effectiveness of a course employing a discipline-based computing approach. The research questions driving this study were: (1) Can experiences with discipline-based computing promote students' acquisition and application of foundational computing concepts and procedures? (2) How do students perceive and experience the integration of discipline-based computing as relevant to their future career goals? (3) How do students perceive the structure of the class as useful and engaging for their learning? We used qualitative and quantitative research methods to approach the research questions. The population studied was 20 engineering undergraduates from Johns Hopkins University. Results of this study suggest that students performed proficiently in applying computing methods, procedures, and concepts to the solution of well-structured engineering problems. Results also suggest that student self-perceptions of their overall computing abilities and their abilities to specifically solve engineering problems shifted from low to high confidence. Students consistently found the course to be important and useful for their studies and their future careers. They also found the course to be of very high quality and identified the instructors and the teaching and feedback methods employed as very useful for their learning. Finally, students also described the course as very challenging compared with other courses in their own department and at the university in general.