Robotics education fosters computational thinking, creativity, and problem-solving, but remains challenging due to technical complexity. Game-based learning (GBL) and gamification offer engagement benefits, yet their comparative impact remains unclear. We present the first PRISMA-aligned systematic review and comparative synthesis of GBL and gamification in robotics education, analyzing 95 studies from 12,485 records across four databases (2014–2025). We coded each study’s approach, learning context, skill level, modality, pedagogy, and outcomes (κ =.918). Three patterns emerged: (1) approach–context–pedagogy coupling (GBL more prevalent in informal settings, while gamification dominated formal classrooms [p <.001] and favored project-based learning [p =.009]); (2) emphasis on introductory programming and modular kits, with limited adoption of advanced software (~17%), advanced hardware (~5%), or immersive technologies (~22%); and (3) short study horizons, relying on self-report. We propose eight research directions and a design space outlining best practices and pitfalls, offering actionable guidance for robotics education.

Learnersourcing, an educational approach that positions students as active contributors rather than passive consumers, offers a scalable approach to co-creating instructional resources while engaging students in authentic problem-solving. However, it faces a fundamental tension: effective “learning” requires scaffolding that minimizes extraneous cognitive load and focuses attention on reasoning, while effective “sourcing” requires structure, completeness, and standardization to ensure student-generated content can be reused. These competing goals create a tradeoff: students either learn but produce content that is difficult to reuse, or generate usable resources but receive limited learning benefit. We propose a new AI-assisted learnersourcing paradigm to address this tension. By assigning collaborative roles to both learners and AI, the approach enables students to focus on cognitively meaningful sub-tasks that foster “learning”, while large language models (LLMs) handle mechanical and procedural sub-tasks for “sourcing”. Guided by user-centered design principles, we implement this workflow in ExPeerience, a system that scaffolds students in co-creating contextualized worked-out examples for database programming. Within ExPeerience, the AI serves as a collaborator for ideation, a co-creator of artifacts, and an evaluator of students’ inputs. Our evaluation with 24 participants showed that structuring AI into distinct collaborative roles improves learning engagement while producing high-quality student-generated content. Compared to a baseline using the Gemini chatbot, ExPeerience users created SQL problems in more diverse and personally meaningful contexts. They actively evaluated, edited, and refined AI-generated components, and most authored their own SQL solutions, whereas baseline participants largely accepted AI outputs without modification and did not attempt to solve the problem. Overall, ExPeerience produced more contextualized, varied, and thoughtfully constructed worked-out examples. These findings demonstrate the potential of AI-assisted learnersourcing as a paradigm to balance learning and sourcing goals. We also draw design implications for future AI-assisted learnersourcing systems that aim to produce reusable, high-quality learner-generated content while promoting educational value.

Transferring programming skills learned in the classroom to diverse real-world scenarios is both essential and challenging in computing education. This experience report describes an approach to facilitate learning transfer by fostering adaptive expertise. Students were engaged in co-creating contextualized worked-out examples, including step-by-step solutions. Through three homework assignments in a Spring 2023 database programming course, we observed substantial improvements, where students generated detailed and accurate solutions and enriched their problem-solving contexts from simple phrases to detailed stories, drawn from 17 real-life scenarios. Our results also suggest that the peer assessment process cultivated a supportive learning environment and fostered adaptive expertise. We discuss the lessons learned and draw pedagogical implications for integrating student-generated contextualized materials in other programming courses.

This paper describes XRXL, an extended-reality system for increasing student engagement in large lectures. Students wear XR headsets to see 3D visualizations controlled by the instructor. The instructor can virtually retract the roof and walls of the classroom to allow for large-scale visualizations that extend beyond the physical boundaries of the classroom, or to turn the classroom into a 360° theater. The instructor can also partition the classroom into small groups of students and to assist individual groups as needed. XRXL was tested in an IRB-approved user study with 82 students in the context of a mock-lecture on neural networks. To the best of our knowledge, the study is the largest deployment of a co-located collaborative XR application to date. The study shows that students had a favorable opinion of XRXL, that XRXL had a low task load, an acceptable usability level, and that it did not cause cybersickness.

Researchers have provided insights into using virtual reality (VR) for visualization and interaction with 3D models and simulations. The interaction allows users to manipulate the 3D elements and visualize changes based on their inputs from movement with controllers or spatial actions. However, some users may find this interaction overwhelming, especially when immersed in a virtual environment. Additionally, the choice of dimensionality for visualizations influences user interaction, with potential implications for immersive experiences. Thus, we conducted a 2 (Immersion: Desktop vs. HMDVR) $\times 2$ (Dimensionality: 2 D vs. 3 D) within-group study $(N=32)$ to explore the impact of the utilized immersive degree and the dimensionality representation of the content on participants’ experience in terms of engagement, task load, usability, skill, and emotions when interacting with a science simulation. We designed and developed an application to simulate charged particles and electric field lines. We asked participants to complete a task of changing particles by matching them to a given simulation output. Our results indicated higher workload rates for HMDVR conditions, particularly with 3D representation, compared to Desktop. However, HMDVR conditions also showed greater engagement, emotional response, and presence. Based on our findings, we argue that participants prefer HMDVR over Desktop environments regardless of dimensionality.

This research paper investigates the effectiveness of cooperative learning and co-regulation strategies in promoting teamwork and enhancing students' performance in higher education. The study was conducted in an in-person intermediate-level information system design course with 152 students divided into 31 teams. The students utilized the Scrum framework to manage a semester-long project with three milestones. Retrospective data were collected at the end of each milestone, and the first milestone data were analyzed in this study. Through a thematic analysis of retrospective data collected after the first milestone, the study examines students' planning, monitoring, and reflection strategies. The findings reveal that students demonstrated adaptive planning, equitable contribution, and task allocation based on individual strengths and preferences. In terms of monitoring, students adopted a proactive approach, displayed relational competence, and utilized both synchronous and asynchronous communication channels. Regarding reflection, students valued effective planning and execution but struggled with time management. They developed concrete improvement strategies for the next milestone, emphasizing realistic deadlines, improved communication, and a better understanding of team members' strengths. This research contributes to the understanding of cooperative learning and co-regulation in promoting effective teamwork in higher education. The findings have implications for pedagogical practices and suggest the importance of integrating cooperative learning and co-regulation strategies in team-based learning environments. Future research can further explore the application of these strategies in different educational contexts and investigate their long-term effects on students' performance and engagement.

The ICAP framework classifies learning into four levels based on students' cognitive engagement with the course materials. These levels are Interactive, Constructive, Active, and Passive. The built-in hypothesis within the framework suggests that Interactive activities may generate better learning and engagement than Constructive activities, which may generate better outcomes than Active activities, which in turn may generate better results than Passive activities. Prior literature has focused on the impact of ICAP learning modes on student engagement, motivation, and learning outcomes. Most of these studies were conducted in either lab settings or for testing a section of the hypothesis. Although ICAP provides a baseline to determine the nature of class activities, literature is scarce on studies that use the framework. Considering this gap, for this study, we specifically selected a class where the instructor conducted lectures using active learning-based instructional activities. Considering that the instructor's perspective may differ from students' perspective, we use the validated survey instrument Student Response to Instructional Practices (StRIP) to examine how students perceive the instructional activities in class. Also, this study focuses on the impact of the Interactive, Constructive, Active, and Passive components of ICAP on students' positivity, participation, and the value they derive from course materials. More specifically, the study answers two research questions: 1) How did students perceive the frequency of the different instructional activities (interactive, constructive, active, or passive) in class? and 2) How did the instructional activities relate to students' positivity, participation, and perceived value of the activities? We collected the data from a systems analysis and design course at a large Midwestern University. Eighty-eight students voluntarily participated in the end-of-semester survey. Data were collected for students' responses to instruction for each activity type, the value of class activities, degree of participation, and their positivity towards the class. Using the averages of students' responses to instructional activities, we calculated the Pearson bi-variate correlations between students' perceptions about activities and their participation, attitude, and value. Additionally, we used multiple regression analysis to examine the relationship between instructional activities and students' positivity, participation, and value of learning. In this paper, we explain the results of these analyses and describe the degree to which students' perspectives resonated with the instructor's perspective. The study's findings provide unique insight into students' perceived use of the various instructional activities in the class. Also, the study provides a preliminary way to use the ICAP framework for examining the nature of class as active or passive and the effect of such class designs on students' behaviors.

This study aims to explore the team coordination commitments reported by undergraduate students pursuing biomedical and agricultural engineering majors after completing three team-based computational modeling assignments throughout the course of a semester. In each post-assignment reflection, the teams were asked to reflect on what aspects could be done better for the next project in terms of team coordination to address the issues that didn't work. Team-based reflections were analyzed using thematic analysis. We identified students' recognition of the importance of scheduling and coordinating meetings, task management and delegation, collaboration and teamwork, and time management towards teams coordination. Furthermore, the results of this study provide the evolution of teams' commitments for three teams, indicating that teams had a shift in their coordination priorities for the different projects.

This paper proposes a dynamic Knowledge, Skills, and Abilities (KSA) matrix-based taxonomy for the Industry 4.0 workforce. The study methodology consisted firstly of identifying the KSAs through a literature review and secondly of a KSA relevance analysis using information from World Economic Forum (WEF) global reports and the Organization for Economic Cooperation and Development (OECD). Finally, we identified the correlation coefficients of the KSA matrix elements concerning the data on jobs and occupations using information from the European Skills, Competencies, and Occupations (ESCO), Occupational Information Network (O*NET), and the strategic intelligence platform of the World Economic Forum. One of the goals was to make the taxonomy compatible with existing and future machine learning methods (i.e., AI-ready) that will enable efficient and effective use of AI in mining and explaining existing and potentially proposing novel trends and strategies. Preliminary results show that the KSA Industry 4.0 Taxonomy can serve as an international reference guide for designing 2030 educational approaches to active and experiential learning in Higher Education Institutions.

This work-in-progress paper will explore the effectiveness of topic modeling to support the analysis of Colombian teachers' conceptions of computational thinking (in Spanish) in an online professional development program. Computational thinking has become a form of literacy as it can help individuals to solve problems. Consequently, governments and bodies of accreditation worldwide have supported educational initiatives, primarily at the K-12 level. However, curricular changes are not enough. Teachers need to be prepared, so they develop the content knowledge associated with computational thinking concepts, practices, and applications in the classroom. To contribute to professional development opportunities geared toward the development of computational thinking pedagogical content knowledge, the Colombian National Academy of Exact, Physics, and Natural Sciences and the Global Center for Equitable Computer Science Education implemented an open online professional development program for Latin American early childhood and elementary educators. More than 100 teachers enrolled in a six-week online professional development program to integrate computational thinking activities from early childhood education. The program included two modules focused on conceptual understanding of computational thinking in early childhood and four more modules where the participants adapted, designed, implemented learning activities, and reflected on what happened during the implementation. As part of the participants' weekly interactions, the program included a Jamboard space, where the teachers answered a set of guiding questions, just like a discussion forum, but as a post-it wall, where they could access all their peers' contributions and questions in a single space.

This study implemented transformative pedagogy as a reflective approach to promote intercultural self-awareness and its potential consequences in the context of teamwork. The context was a second-year systems analysis and design course with 118 students in the fall 2021 semester and 155 students in the spring 2022 semester. The research question was: What are students' beliefs regarding their own cultural values and the potential implications of those values on their teamwork interactions? Findings from the study indicate that students realized that team dynamics and values are crucial to team experience. We found that students believed that commitment to the team and communication of values contributed to the experience of teamwork and teamwork success. Students also believed that coming together and making decisions together in a collectivistic manner would help the progress of the team.

Technology and automation have become increasingly critical for organizations today, and programming has become an essential skill for all STEM majors to meet this demand. Graduates are expected to possess programming skills to meet the needs of the modern workforce. Acquiring programming skills is a challenging task, and institutions often struggle to provide adequate resources to meet the industry's demand for proficient computer programmers. To take steps toward better understanding programming challenges among undergraduate students in science disciplines, this study aims to characterize patterns in students' solutions to programming assignments over the course of a semester. With this, the goal is to characterize students' most common challenges and take steps toward providing automated feedback. Specifically, this study applies machine learning (ML) classification algorithms to analyze student artifacts from a college-level introductory Python programming course for science majors, including source code from labs, homework assignments, projects, and two live-coding exams. Data was collected as part of a semester-long course and pre-processed and de-identified. The researchers labeled the data, and relevant features were selected to prepare the data for training the ML algorithms. Various classification algorithms were trained, and the resulting ML models were evaluated for their accuracy. Then, the study deployed a quantitative research method to evaluate both the effectiveness of various ML models and the quality of the feedback the model could provide, such as efficiency and accuracy. The research results are expected to inform the development of machine-learning algorithms to provide higher-quality feedback mechanisms for students in introductory programming courses. In that manner, this study contributes to improving the quality of programming education.

The entrepreneurial mindset is defined as the inclination to discover, evaluate, and exploit opportunities. One approach to developing the entrepreneurial mindset is through evidence-based decision-making, which has the potential to lower costs, improve quality of life, and even save lives by understanding learning patterns and trends related to data, in general, and big data, specifically. As a result, data science solutions are being increasingly deployed in the business world, and the growth of publicly accessible data provides a significant opportunity to transform educational efforts related to data science. This is of particular importance to the engineering and computer science fields given the increasing focus on big data and evidence-based decicision making. As such, the purpose of this study is to report on one approach to developing the entrepreneurial mindset through integrating evidence-based decision making into the engineering and technology classroom using Microsoft Power BI Desktop, a freely available tool released by Microsoft in September 2013. The study was assessed using a mixed methods approach, including a rubric to measure demonstration of the entrepreneurial mindset and metacognitive reflection to better understand student awareness of learning. The findings were categorized into two key themes related to each high performing and low performing groups. The high performers (1) expressed more concerns related to the "big picture" and "critical thinking" based challenges primarily focused on the research and solution, and (2) articulated coping habits and methods used to overcome stress. The low performers (1) highlighted challenges related to time management and procrastination and (2) confusion about project requirements and how to do the required tasks (which is believed to be attributed to missing classes). Lessons learned and recommendations are provided.

Commitment is a multi-dimensional construct that has been extensively researched in the context of organizations. Organizational and professional commitment have been positively associated with technical performance, client service, attention to detail, and degree of involvement with one’s job. However, there is a relative dearth of research in terms of team commitment, especially in educational settings. Teamwork is considered a 21stcentury skill and higher education institutions are focusing on helping students to develop teamwork skills by applied projects in the coursework. But studies have demonstrated that creating a team is not enough to help students build teamwork skills. Literature supports the use of team contracts to bolster commitment, among team members. However, the relationship between team contracts and team commitment has not been formally operationalized.This research category study presents a mixed-methods approach towards characterizing and operationalizing team commitment exhibited by students enrolled in a sophomore-level systems analysis and design course by analyzing team contracts and team retrospective reflections. The course covers concepts pertaining to information systems development and includes a semester-long team project where the students work together in four or five member teams to develop the project deliverables. The students have prior software development experiences through an introductory systems development course as well as multiple programming courses. The data for this study was collected through the team contracts signed by students belonging to one of the 23 teams of this course. The study aims to answer the following research question: How can team commitment be characterized in a sophomore-level system analysis and design course among the student teams?A rubric was developed to quantify the team commitment levels of students based on their responses on the team contracts. Students were classified as high or low commitment based on the rubric scores. The emergent themes of high and low commitment teams were also presented. The results indicated that the high commitment teams were focused on setting goals, effective communication, and having mechanisms in place for timely feedback and improvement. On the other hand, low commitment teams did not articulate the goals of the project, they demonstrated a lack of dedication for attending team meetings regularly, working as a team, and had a lack of proper coordination while working together.

This Research to practice work in progress paper explores teachers' experiences about a professional development program focused on discipline-based computational thinking (CT). The goal of the program is to integrate computational practices into disciplinary learning environments at the K-12 level in Colombia. To promote this integration and to explore teachers' conceptions and possible scenarios to integrate CT in this context, the research team designed, implemented, and assessed a 20-hour professional development program with 21 teachers from public middle and high schools in Antioquia, Colombia. The program introduced the concepts and practices of CT and discussed why these are relevant for students to learn. A use-modify-create instructional sequence was used as the pedagogical approach to scaffold participants' learning process. At the end of the workshop, the teachers completed a survey regarding their experience in the program. Preliminary results indicate that the participating teachers increased their knowledge and interest to integrate computational thinking practices into their disciplinary learning environments.

We investigated whether an intelligent tutoring system, ElectronixTutor, improved students' types of knowledge, including conceptual learning, problem-solving, and model building in the domain of electronic circuits. Specific research questions were (1) Can students improve their deep learning through interacting with ElectronixTutor through conceptual learning, problem-solving, and model building modules? And (2) What are student learning characteristics as they proceed through each model and learning mode? We conducted a recorded interview procedure with 10 participants as they learned with ElectronixTutor, and gave them a pre-, mid-, and posttest in order to assess their overall learning gain as well as learning transfer. We found that, although there were no significant effects due to our small sample size, the results from the means suggest both learning gain and learning transfer. Qualitative analyses were conducted on each of the three different learning modes. Collectively we found that participants were very good at problem-solving using equations and writing formulas but struggled on the application or conceptual questions. Our study suggests that there is a need for building or improving more sophisticated learning or tutoring technologies on STEM domains that focus on the integration of knowledge and skills.

Intelligent tutoring systems (ITS) have shown to be useful learning aids for helping students learn STEM subjects. Previous studies on ITS tend to focus on developmental aspects of the system, such as system design, programming architecture, and dialogue moves. In this systemic literature review, we focus on pedagogical aspects of ITS within STEM domains. Specifically, we identified the implemented scaffolding approach and the grounding on learning theories of ITS implementations. Specific research questions were: (1) what types of knowledge (i.e., conceptual learning, problem-solving, and model building) are delivered via an ITS within STEM domains? (2) what pedagogies or scaffolding methods are used to guide the ITS learning experiences? (3) what are the characteristics of the research designs and specific learning outcomes when learning with the ITS? The steps followed for performing this systematic literature review were: (1) identifying the scope and research questions, (2) defining the inclusion and exclusion search criteria of literature, and (3) classifying and cataloging the literature sources that use ITS for STEM in classroom research. The final data set is comprised of a total of 22 papers that meet our criteria. We found a lack of fine-grained research on the effectiveness of using ITS to improve the three major learning modes: conceptual learning, problem-solving, and model building, particularly in STEM domains. In addition, we recommend that research conducted on ITS and other learning technology aids should emphasize the utilization of well-established learning theories and pedagogical scaffolding methods so that ITS will be more accessible to STEM educators for introducing ITS to their students to better learn STEM subjects.

This full research paper looks at data science, which is an increasingly important topic of interest across all academic disciplines and industry sectors. As such, the need for curricula and learning designs for teaching data science has never been greater. One increasingly useful institutional initiative for building critical skills is living learning communities. This study reports on a data science learning community, called The Data Mine. As part of this learning community, students worked on similar tasks, took classes together, and participated in industry related assignments. This exploratory study aims to understand how students navigated learning challenges, what self-regulation strategies were adopted, and what students ultimately learned through participating in the Data Mine Learning Community. Students were grouped into achievement categories using a developed rubric on course assignments and their responses to open ended questions were evaluated for patterns among those in their achievement group. The results indicate that the highest performing students avoided coding errors and were highly focused on learning gains.

Virtual reality (VR) has disrupted the gaming market and is rapidly becoming ubiquitous. Yet differences between VR and traditional mediums, such as controllers that are visible in the virtual world, enable entirely new approaches to instruction. In this paper, we present four studies, each using a different VR game. Within each study, we compared three different modalities of tutorials: Text (text-only), Text+Diagram (text with controller diagrams), and Text+Spatial (text with controller tooltips appearing on top of the player's virtual controllers). Data from our studies show that the importance of tutorial modality depends greatly on game type. In a third-person shooter, Text+Spatial led to significantly higher controls learnability than Text and Text+Diagram, and also led to significantly higher performance, player experience, and intrinsic motivation than Text. In a puzzle game, Text+Spatial led to significantly higher controls learnability and performance than Text. Additionally, Text+Diagram led to significantly higher controls learnability than Text. However, in a wave shooter and a rhythm game, differences between conditions were negligible on all measures. Our studies show that game type is an important factor to consider when designing tutorial modality.

Avatar identification is one of the most promising research areas in games user research. Greater identification with one's avatar has been associated with improved outcomes in the domains of health, entertainment, and education. However, existing studies have focused almost exclusively on the visual appearance of avatars. Yet audio is known to influence immersion/presence, performance, and physiological responses. We perform one of the first studies to date on avatar self-similar audio. We conducted a 2 x 3 (similar/dissimilar x modulation upwards/downwards/none) study in a Java programming game. We find that voice similarity leads to a significant increase in performance, time spent, similarity identification, competence, relatedness, and immersion. Similarity identification acts as a significant mediator variable between voice similarity and all measured outcomes. Our study demonstrates the importance of avatar audio and has implications for avatar design more generally across digital applications.

This Innovative Practice Full Paper details a design-based research approach for implementing computational learning activities in a first-year engineering physics course. This study contributes to the growing body of research on computation in engineering education by introducing computational concepts and activities during a physics laboratory class. Drawing from Experiential Learning Theory and using an adapted version of the Use-Modify-Create framework for teaching computational thinking, a series of lab activities was designed that combined physical lab experiments with computational modeling using custom-built VPython simulations. Data was collected from the labs in the form of (1) responses to lab activity worksheets, (2) code modified and/or generated by the students during lab activities, (3) in-code comments provided by the students during the activities. A qualitative thematic analysis was used to analyze students' learning benefits and any challenges faced during the activities. While results show a number of learning benefits, an observed homogeneity of student responses to the questions on the lab handouts point to a set of potential limitations within the activities themselves that warranted further examination. Insight gained from this analysis process is presented as a set of four design principles that will inform future implementations of the hybrid course design.

This Research to Practice Full Paper explores students' self-explanations in the context of programming. Specifically, this paper explores the use of in-code comments as an approach to support students' learning process and development of abstraction skills in an introductory programming course at the undergraduate level. Computer programming is a difficult skill to learn by novices due to the complexity of multiple elements interacting with each other to produce a specific outcome. Providing worked-examples paired with an engaging pedagogical practice has demonstrated to be an effective strategy for novices to start learning complex topics such as computer programming. One of the strategies that can support the introduction of worked examples is the use of self-explanation activities. In the context of programming, using in-code comments as a way for students to self-explain programming code can support the integration of worked-examples to scaffold their learning process. In this study, students wrote comments to explain how worked examples that were provided completed a specific task. Their comments were scored using an assessment rubric to provide detailed feedback about regarding the quality of their comments and their understanding of the code beyond the line by line execution. The goal of this study is to explore whether students with prior exposure to computer programming generate better self-explanations, and the effect that the quality of the written explanations and students' prior programming experiences have on student overall performance in the introductory programming course. The implications of this study will contribute to a better understanding of effective practices to incorporate worked examples and self-explanation activities in the form of in-code comments for introductory programming courses.

In this Work In Progress in the Research Category we present a qualitative study that explores how computing instruction experiences may mediate the progression of social and cognitive factors associated with career development in community college engineering students. The rapidly evolving technological landscape has created an increased need for an engineering workforce with computing skills. Traditionally, policymakers, educators and researchers have focused on K-12 and four-year secondary institutions as the primary arena for addressing our workforce development needs. However, community colleges, which enroll over one-third of U.S. undergraduates, represent an important, and not yet fully utilized resource for training future engineers. However, there is a dearth of engineering education research that seeks to critically understand the experiences of community college engineering students in learning computational skills. To address this gap, our study includes semi-structured interviews with first- and second-year students enrolled in an engineering program at a public two-year institution in the midwestern United States. Our work is informed by a social cognitive career theory (SCCT) perspective from which we examine how engineering course learning experiences involving integrated computing instruction influence students' self-efficacy beliefs and outcome expectations. In alignment with SCCT, our analysis will aim to identify personal attributes and external environmental factors that mediate students' differential responses to computational instruction. This pilot study is the first part of a larger research effort directed towards learning how to support students as they navigate the community college pathway to engineering.

This Work in Progress paper in the Research category studies undergraduate students use of higher order metacognitive skills while engaging in computational modeling engineering research. Policymakers, universities and other key stakeholders have promoted `authentic learning experiences' including undergraduate research experiences as a vehicle for the cognitive and metacognitive development of undergraduates. Indeed, undergraduate research experiences in STEM fields have been demonstrated to have positive effects including enhanced technical skills development and greater persistence in STEM degrees. However, specific research is needed to understand how metacognitive skills are engaged during undergraduate research experiences. Further, work on undergraduate research experiences has typically focused on science research, and relatively little work has been done to understand student development during research experiences in engineering and computational fields. To address this gap, we have initiated a qualitative study of undergraduate researchers within a computational modeling engineering research group at a large midwestern university. Here, we share preliminary results from semi-structured interviews of undergraduate student researchers and their supervisors. The research and implications of our work are grounded within a cognitive apprenticeship framework and will provide insight into how best to support the cognitive and metacognitive development of undergraduate engineering researchers.

This paper proposes a special session on the use of computational methods for analyzing educational data. Computation has permeated all disciplines because it provides unique opportunities to represent knowledge and understand complex phenomena. In education, disciplines such as learning analytics and educational data mining have emerged to better understand educational phenomena. This special session will discuss three different approaches to use computational methods to analyze qualitative educational data. After the discussion, the participants will be able to implement these methods using R programming, while reflecting on how they can use these methods in their own context.

This Full Paper in the Research Category presents a principled approach to integrate machine learning within qualitative education research. More specifically, we show how to build on an existing theory or conceptual framework using machine learning applied to qualitative data in order to make valid conclusions. Our model is guided by the assessment triangle. One case study is presented. The study focuses on habits of mind and their relationship to course outcomes. Patterns among students are identified using the n-TARP clustering method and validated statistically. Students are represented by a profile representing the patterns they follow and their individual course outcomes. We subsequently test for the existence of a relationship between the patterns of habits of mind and the course outcomes using a statistical approach in order to meaningfully interpret the profiles.

Statics is a backbone course for several engineering disciplines and also a pre-requisite for dynamics and mechanics of materials. Researchers have identified a lack of understanding of statics as a significant source of difficulties in terms of both conceptual understanding, representation of free body diagrams (FBD) and problem-solving ability. Our approach to improve the learning of the concept of friction focuses on students' understanding of acting forces and specific components of such forces of a system. The presented quasi-experimental study investigates how the use of visuo-haptic simulations can improve students understanding and use of FBD. Specifically, we compared two visuo-haptic simulations; one that explicitly visually depicts FBD of multiple objects interacting with different surfaces, while the other only provides haptic feedback while students engage in the same forms of interaction. Our results suggest that using the visuo-haptic simulator with FBD leads to better learning results. These findings support the hypothesis that appropriately sequenced visuo-haptic simulators with well-designed visual cues can help students to better understand and use FBD.

Structural analysis is a foundational statics concept for students majoring in mechanical engineering, civil engineering, and engineering technology, among others. However, the mathematical emphasis of a typical statics courses lies in algebraic calculations, matrices, vectors, and sometimes deemphasizes student understanding of the behavior of the overall structure as a system, focusing instead on its individual elements. This study investigates students' conceptual understanding of forces acting and reacting in a truss structure as well as their corresponding representations in the form of Free Body Diagrams (FBDs). Our findings suggest that students primarily demonstrated partially coherent answers suggesting that they may hold some misconceptions about truss behavior. The most prevalent error was that students failed to account for the mutual (equal and opposite) forces between connected bodies that were separated for analysis. Based on our findings we propose the design of a learning experience that combines principles of embodied learning with the affordances of visuohaptic simulations to address students' misconceptions.

This paper presents a literature review on the integration of laboratories into undergraduate engineering education. The literature review starts with a brief discussion about historical and current trends of how laboratories were (are) integrated into the engineering curriculum. This initial review indicates that although highly important to the education of engineers, laboratories often are not used to their full potential. To identify the possible reasons for that situation, the authors synthesize the scarce literature on faculty attitudes toward the integration of laboratories into undergraduate engineering education, and identify factors that could be associated with instructional decisions and approaches to teaching laboratory classes. The findings indicate that faculty decisions regarding laboratory education are shaped by a myriad of factors, including beliefs, knowledge, institutional factors such as departmental values and reward systems, and external factors such as funding agencies and professional societies. Finally, the authors discuss implications of these findings for the design of professional development programs that would foster the effective use of laboratory classes in engineering education.

This paper proposed a workshop to introduce the use of computational tools and methods to analyze educational data. The workshop will demonstrate three different contexts in which these tools can be used to visualize and characterize patterns within educational data, and validate them using statistical techniques. Participants in this workshop will have the opportunity to learn how to implement these methods using R programming language.

We explored the affordances of using visuo-haptic simulations to improve conceptual understanding and representational competence of the concept of friction. Visuohaptic simulations are computer-based simulations that encode mathematical and physical models of certain phenomena and provide visual and tactile feedback. Users can see the simulation and feel the friction with their hand by using a special device connected to a computer. We hypothesized that visual and haptic feedback together can help students to improve learning of friction. We recruited 24 engineering technology students with a previous experience in at least one physics course, and we examined their reasoning and understanding about statics concepts before and after engaging with visuo-haptic simulations. Our instructional approach included four steps: 1) lecture about friction, 2) pretest, 3) laboratory session, and 4) posttest. The laboratory session consisted of a pre-training session, guided learning materials based on a constructivist framework, and use of the friction visuo-haptic simulation. We report students' prior conceptions of statics concepts, ways in which they interacted and reasoned with each of the different pedagogical tools, and compared reasoning processes, explanations and learning gains. Our results suggest that the visuo-haptic simulation helped students refine their explanations and increased the coherence between their verbal explanation and mathematical representation.

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.

Engineering design is an iterative process that supports the solution of problems by applying scientific knowledge to make informed decisions. Assessing different levels of expertise in experimentation is a difficult task since these are not usually visible as part of a student's final design solution. The purpose of this research is to investigate and characterize students' experimentation strategies while working on a design challenge. We conducted a concurrent think-aloud to capture students' thinking while they were working on a design challenge using an educational computer-aided design (CAD) software. We showed how the design replays generated from the log files collected from the CAD software can be used to represent students' experimentation strategies and how these representations can be validated by the data collected from the think-aloud. Our preliminary results show that technology-based assessment by the educational CAD tool allows us to identify the differences between different experimentation strategies and that the result of this assessment is supported by the result obtained from the concurrent think-aloud. Implications of this work would be relevant to engineering educators and researchers who are interested in understanding and assessing students' experimentation strategies in engineering design.

Haptic devices are electro-mechanical tools controlled by computers that allow to recreate the sense of touch. They enhance the sense of interaction with virtual objects from purely visual to haptic and visual. One of its application areas is in training environments, where the users can interact with virtual objects to learn procedures or tasks. In this paper we describe the use of haptic devices to improve the learning process of basic physics concepts from electromagnetism and the haptic tools via simulation of magnetic forces in 3D. We have created three scenarios with different distribution of charges: point charge, line charge, and plane charge. Each scenario was properly calibrated and has different force feedback (quadratic, linear, and constant) depending on the scenario. We wanted to investigate how forces are perceived by students. A user study was carried out to assess students' perception and knowledge acquired when they were working with the system. Results suggest that students from the treatment group achieved better understanding than those from the control group. Results also indicate that 95% of the students considered that the use of haptic devices combined with appropriate virtual environments facilitated them to understand the nature and origin of electrical forces.

Many cyber infrastructure and cloud computing systems have been developed and deployed over the past decade. Although use metrics are collected by many of these systems, there is not a clear link from these metrics to the ultimate effectiveness and impact of these systems on science communities. This paper describes a framework we developed that seeks to provide context for use and impact metrics to facilitate understanding of how these systems are used and ultimately adopted by science and engineering communities. We use this framework to present metrics of use, impact, and effectiveness collected from the NEES cyber infrastructure.

The focus of this study is to characterize the representational practices of engineering graduate students who were involved in using computational and analytical mathematical models to solve a complex battery system design problem. The research question of this study is: How do students develop and use representational artifacts, while using mathematical or computer simulation models, for articulating their solutions to a complex battery system design problem? Results of this study provide descriptions of students representational artifacts produced in each of the stages of the problem solving process. The outcome of the study provides a framework for future studies using a larger sample size for investigating the role of mathematical and computational models in supporting students' problem solving processes in engineering education.

Advances in computing contribute to science and engineering discovery, innovation, and education by facilitating representations, processing, storage, analysis, simulation, and visualization of unprecedented amounts of experimental and observational data to address problems that affect health, energy, environment, security, and quality of life. In spite of the emerging importance of the role of computing in engineering, a well-recognized shortage of scientists and engineers who are adequately prepared to take advantage of, or contribute to, such highly interdisciplinary, highly computational scientific challenges is evident. This exploratory study identifies how computation is integrated in the engineering disciplines at the undergraduate level. The research question is: How engineering professors integrate computation as part of their disciplinary undergraduate courses? This study reports anonymous survey responses of thirty-nine engineering and engineering technology faculty members who identified themselves as integrating computation as part of their undergraduate courses. Results indicate that most of the faculty members used computation for the solution of complex calculations, for conducting simulations, and for design purposes. Further research is required in order to identify and validate appropriated pedagogical practices to integrate computation as part of disciplinary courses.

This paper describes the outcomes of a three-day teacher professional development workshop aimed at introducing concepts, principles and practices of computational thinking. The guiding research question for this study was: How teachers implement the backwards design process embodying elements of CS Principles (i.e., computational thinking big ideas and computational thinking practices) in the context of their classrooms? The participants of this study included 15 elementary, high school and college level teachers who are also graduate students from a master program in engineering. As part of the workshop participants developed a learning activity that included a set of learning objectives, the design of computational thinking related activities considering appropriate pedagogical strategies, and the integration of mechanisms to evaluate students' performance. Here we describe (a) how participants embodied the CS Principles in the design of learning activities to be integrated into their classrooms, (b) how they used the backwards design process as a tool to implement elements of the CS Principles and (c) what is teachers' performance in integrating CS Principles to the design of learning activities as evidenced by their peer evaluations. Finally, we propose the use of backwards design process together with the CS Principles as a framework for the design of computing learning activities and the development of teacher professional development programs in computing 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.

Declining interest in computing programs nationwide presents a threat to America's security and limits potential for innovation across all domains. One way to address this problem is to remove misconceptions held by the nation's youth about computing, including information about how it positively impacts many subjects and showing them that applying computing can be fun and rewarding. One program at a Midwestern university accomplished this goal through a week-long, residential, summer camp for high school students to educate them about career opportunities and possibilities for people with Information Technology skills. Participants completed a variety of hands-on activities daily, along with listening to work experiences of computing professionals. Feedback collected from the student participants showed that in addition t o raising awareness about computing opportunities, the program increased youth interest in IT, prompted many to enroll in computing/engineering courses, and improved their performance in school. This paper shares details about the program and participant feedback to make a case for offering similar programs to correct the knowledge people have about computing.

This work in progress explores the landscape of computing learning resources and environments found on the web together with teaching and learning materials that can facilitate the integration of “computational thinking” into the K-12 classroom. In specific, this paper focuses in finding and describing existing learning environments that integrate computational thinking into a STEM discipline together with lesson plans, activities and other curricula.

Previous studies indicated that misconceptions related to heat transfer, fluid mechanics, and thermodynamics, persist among engineering juniors and seniors even after they completed college-level courses in these subjects. Researchers have proposed an innovative instructional approach, the ontological schema training method, which helps students develop appropriate schemas or conceptual frameworks for learning difficult science concepts. Three online training modules were designed to help engineering students develop appropriate schemas in heat transfer, diffusion and microfluidics. The effectiveness of these modules was examined with two different student populations from two different universities (US and Hispanic). At each institution, participants were assigned randomly to a control or experimental group. The treatment for each group at both institutions was exactly the same. Preliminary results indicated a mixed effectiveness of the training modules among these populations.

Computation is an increasingly essential tool for doing scientific research. It is expected that future engineers will need to engage and understand computing in order to work effectively with computational systems, technologies, and methodologies. Toward this goal, we leverage our previous work with SugarAid v0.2 to allow learners to test their knowledge of computing by applying engineering concepts. Applied concepts include designing multidisciplinary systems including electrical, mechanical, fluidic, and thermal components. Through this method, we expect learners to enhance their computing knowledge by applying their engineering knowledge.

Technological advances in cyberinfrastructure have paved the way for research grade computational simulation tools, such as those available on nanoHUB.org. Even though benefits have been acknowledged for incorporating these tools into teaching and learning environments, difficulties have also been identified. To address some of these difficulties researchers have emphasized that inquiry learning with simulations, in order to be successful, needs adequate but not intrusive scaffolding. As a response to this need, nanoHUB.org affiliated faculty have proposed tool-based curricula to be used for training 21stcentury engineers in the nanoelectronics field. Motivated and informed by our previous work and related literature on inquiry learning with simulation, a transparency and scaffolding framework is proposed to be integrated into existing nanoHUB tool-based curricula.