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Abstract 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.

Learning in science, technology, engineering, and mathematics (STEM) is often challenging due to the abstract and counterintuitive nature of some concepts. Computer-based learning has emerged as an alternative method to help improve students' comprehension of these complex topics, even though technological tools must be supported with pedagogical strategies, technology affordances, sound design, and structured activities to teach scientific concepts properly. In that sense, we propose the design of an immersive virtual reality (IVR) experience, including visual and haptic cues to facilitate learning about electric fields (EFs) and charged particles (CPs) concepts. We scaffolded our design tool based on embodied design principles and cognition. The IVR experience allows learners to manipulate the components of point charges (e.g., particles, distance between particles, and charges) to learn electricity concepts. We conducted a qualitative study (N = 8) to assess the designed application. The sample included undergraduate students (five male and three female) from technology-related fields with some or no prior knowledge of high school or higher education physics. We assessed study participants' conceptual understanding through a pretest-posttest and conducted a brief interview to identify their expected interaction with the designed affordances. Screen recording and the System Usability Scale (SUS) are the other metrics of interest in defining study participants’ performance and experience. The collected data and thematic analysis suggested that participants recognized the included affordances based on the embodied design principles and used them to interact, link previous knowledge, and identify the different factors to explain the physics phenomenon. Additionally, we provided insights for designing IVR experiences to promote conceptual understanding of complex STEM topics based on embodied learning principles.

Introduction: This study aimed to develop, implement, and test a visuo-haptic simulator designed to explore the buoyancy phenomenon for freshman engineering students enrolled in physics courses. The primary goal was to enhance students’ understanding of physical concepts through an immersive learning tool.Methods: The visuo-haptic simulator was created using the VIS-HAPT methodology, which provides high-quality visualization and reduces development time. A total of 182 undergraduate students were randomly assigned to either an experimental group that used the simulator or a control group that received an equivalent learning experience in terms of duration and content. Data were collected through pre- and post-tests and an exit-perception questionnaire.Results: Data analysis revealed that the experimental group achieved higher learning gains than the control group (p = 0.079). Additionally, students in the experimental group expressed strong enthusiasm for the simulator, noting its positive impact on their understanding of physical concepts. The VIS-HAPT methodology also reduced the average development time compared to similar visuo-haptic simulators.Discussion: The results demonstrate the efficacy of the buoyancy visuo-haptic simulator in improving students’ learning experiences and validate the utility of the VIS-HAPT method for creating immersive educational tools in physics.

Examining the effectiveness of machine learning techniques in analyzing engineering students’ decision-making processes through topic modeling during simulation-based design tasks is crucial for advancing educational methods and tools. Thus, this study presents a comparative analysis of different supervised and unsupervised machine learning techniques for topic modeling, along with human validation. Hence, this manuscript contributes by evaluating the effectiveness of these techniques in identifying nuanced topics within the argumentation framework and improving computational methods for assessing students’ abilities and performance levels based on their informed decisions. This study examined the decision-making processes of engineering students as they participated in a simulation-based design challenge. During this task, students were prompted to use an argumentation framework to articulate their claims, evidence, and reasoning, by recording their informed design decisions in a design journal. This study combined qualitative and computational methods to analyze the students’ design journals and ensured the accuracy of the findings through the researchers’ review and interpretations of the results. Different machine learning models, including random forest, SVM, and K-nearest neighbors (KNNs), were tested for multilabel regression, using preprocessing techniques such as TF-IDF, GloVe, and BERT embeddings. Additionally, hyperparameter optimization and model interpretability were explored, along with models like RNNs with LSTM, XGBoost, and LightGBM. The results demonstrate that both supervised and unsupervised machine learning models effectively identified nuanced topics within the argumentation framework used during the design challenge of designing a zero-energy home for a Midwestern city using a CAD/CAE simulation platform. Notably, XGBoost exhibited superior predictive accuracy in estimating topic proportions, highlighting its potential for broader application in engineering education.

The application of extended reality (XR) technology in education has been growing for the last two decades. XR offers immersive and interactive visualization experiences that can enhance learning by making it engaging. Recent technological advances have led to the availability of high-quality and affordable XR headsets. These advancements have spurred a wave of research focused on designing, implementing, and validating XR educational interventions. Limited literature focuses on the recent trends of XR within science, technology, engineering, and mathematics (STEM) education. Thus, this paper presents an umbrella review that explores the exploding field of XR and its transformative potential in STEM education. Using six online databases, the review zoomed in on 17 out of 1972 papers on XR for STEM education, published between 2020 and 2023, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The results highlighted the types of XR technology applied (i.e., virtual reality and augmented reality), the specific STEM disciplines involved, the focus of each study reviewed, and the major findings from recent reviews. Overall, the educational benefits of using XR technology in STEM education are apparent: XR boosts student motivation, facilitates learning engagement, and improves skills, for example. However, using XR in education still has challenges that must be addressed, such as the physical discomfort of the learner wearing the XR headset and technical glitches. Besides revealing trends of using XR in STEM education, this umbrella review encourages reflection on current practices and suggests ways to apply XR to STEM education effectively.

Abstract 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.

Fields in the social sciences, such as education research, have started to expand the use of computer-based research methods to supplement traditional research approaches. Natural language processing techniques, such as topic modeling, may support qualitative data analysis by providing early categories that researchers may interpret and refine. This study contributes to this body of research and answers the following research questions: (RQ1) What is the relative coverage of the latent Dirichlet allocation (LDA) topic model and human coding in terms of the breadth of the topics/themes extracted from the text collection? (RQ2) What is the relative depth or level of detail among identified topics using LDA topic models and human coding approaches? A dataset of student reflections was qualitatively analyzed using LDA topic modeling and human coding approaches, and the results were compared. The findings suggest that topic models can provide reliable coverage and depth of themes present in a textual collection comparable to human coding but require manual interpretation of topics. The breadth and depth of human coding output is heavily dependent on the expertise of coders and the size of the collection; these factors are better handled in the topic modeling approach.

This study implements a conflict management training approach guided by principles of transformative learning and conflict management practice simulated via an LLM. Transformative learning is more effective when learners are engaged mentally and behaviorally in learning experiences. Correspondingly, the conflict management training approach involved a three-step procedure consisting of a learning phase, a practice phase enabled by an LLM, and a reflection phase. Fifty-six students enrolled in a systems development course were exposed to the transformative learning approach to conflict management so they would be better prepared to address any potential conflicts within their teams as they approached a semester-long software development project. The study investigated the following: (1) How did the training and practice affect students’ level of confidence in addressing conflict? (2) Which conflict management styles did students use in the simulated practice? (3) Which strategies did students employ when engaging with the simulated conflict? The findings indicate that: (1) 65% of the students significantly increased in confidence in managing conflict by demonstrating collaborative, compromising, and accommodative approaches; (2) 26% of the students slightly increased in confidence by implementing collaborative and accommodative approaches; and (3) 9% of the students did not increase in confidence, as they were already confident in applying collaborative approaches. The three most frequently used strategies for managing conflict were identifying the root cause of the problem, actively listening, and being specific and objective in explaining their concerns.

Companies and their executives are concluding that to accomplish their complex tasks and all-encompassing missions and goals, they must step outside the traditional realms of teamwork and partnership and engage in cross-cultural and multinational alliances. One of the principal areas affecting teamwork today, especially in diverse workplaces, is the variety of cultural value orientations that the members relate to. To better prepare students for joining global enterprises, it is necessary to understand their cultural orientations and how those may influence their teamwork interactions. This study used a mixed methods design to characterize computer and information technology undergraduate students’ cultural orientations and their cultural awareness in the context of teamwork experiences. The data for the study was collected in the form of a reflection assignment that was implemented during the first week of classes as a way to promote students’ cultural awareness and how that may play out in their teamwork interactions. The reflection assignment had two parts: (i) a survey and (ii) reflection questions. The study used descriptive statistics and t-tests to analyze the survey (quantitative data), and thematic analysis was conducted to analyze the reflection questions (qualitative data). Our analysis of the quantitative data revealed that students identified the following core values they are Individualism, Equality, Monochronic, Meritocracy, Informality, Personal Efficacy, and Directness, and these values resonated with most of the students in class. Corresponding qualitative themes that emerged regarding students’ beliefs of how those values may play out in their teamwork experience were (a) Treating everyone equally and understanding others, (b) Increasing temporal Awareness, (c) Enacting effective communication, (d) Monitoring the progress of team members and (e) Establishing rules and boundaries.

Computational thinking (CT) is a critical skill needed for STEM professionals and educational interventions that emphasize CT are needed. In engineering, one potential pedagogical tool to build CT is modeling, an essential skill for engineering students where they apply their scientific knowledge to real-world problems involving planning, building, evaluating, and reflecting on created systems to simulate the real world. However, in-depth studies of how modeling is done in the class in relation to CT are limited. We used a case study methodology to evaluate a model-planning activity in a final-year undergraduate engineering classroom to elicit CT practices in students as they planned their modeling approach. Thematic analysis was used on student artifacts to triangulate and identify diverse ways that students used CT practices. We find that model-planning activities are useful for students to practice many aspects of CT, such as abstraction, algorithmic thinking, and generalization. We report implications for instructors wanting to implement model-planning activities into their classrooms.

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

Student attrition is a challenge experienced by higher education institutions. One of the key reasons for student attrition is the inability of students to develop an identity and a sense of belonging. This study aims to understand the role of a data science learning community in helping students to develop identity and a sense of belonging. The study used a mixed-methods approach to collect and analyze the data. The study used a pre–post survey to understand students’ perceptions regarding identity formation and sense of belonging. The results of the survey study indicated that the perception of identity formation and sense of belonging increased as students progressed through the semester. To further investigate the reasons for the increase in the perception of identity formation and sense of belonging, in-depth semi-structured interviews were conducted. The results of the interview revealed that living in the data science learning community, participating in various events and interacting with faculty, staff and working with peers of similar interest helped students to develop a data science identity and leader identity, as well as experience sense of belonging.

This document presents a systematic review of Multimodal Human–Computer Interaction. It shows how different types of interaction technologies (virtual reality (VR) and augmented reality, force and vibration feedback devices (haptics), and tracking) are used in different domains (concepts, medicine, physics, human factors/user experience design, transportation, cultural heritage, and industry). A systematic literature search was conducted identifying 406 articles initially. From these articles, we selected 112 research works that we consider most relevant for the content of this article. The articles were analyzed in-depth from the viewpoint of temporal patterns, frequency of usage in types of technology in different domains, and cluster analysis. The analysis allowed us to answer relevant questions in searching for the next steps in work related to multimodal HCI. We looked at the typical technology type, how the technology type and frequency have changed in time over each domain, and how papers are grouped across metrics given their similarities. This analysis determined that VR and haptics are the most widely used in all domains. While VR is the most used, haptic interaction is presented in an increasing number of applications, suggesting future work on applications that configure VR and haptic together.

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.

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.

Abstract Although visualization remains a primary mode of interaction in simulations, touch is the most common way people use to interact with the physical objects. A greater sense of immersion in a learning environment can be reached when the user is able to feel and manipulate objects as compared to only seeing or listening. Despite the affordances of haptic technologies, which could serve as scaffolds for deep conceptual learning, their true potential in education has not been fully harnessed and little research has been done to investigate its effectiveness for learning difficult concepts. This study explores the potential of haptic technologies in supporting conceptual understanding of difficult concepts in science, specifically concepts related to electricity and magnetism. A pretest-posttest study identified if students improved their conceptual understanding of electricity and magnetism concepts. Specifically, this study identified (a) how students, with different physics background, conceptually interpreted the tactile learning experience in the context of the visualization, and (b) students’ perceptions on the use of haptic technologies for their learning, as well as their perceived usefulness and ease of use. Our results suggest that overall students significantly improved their conceptual understanding about electric fields for distributed charges after being exposed to a visuohaptic simulation guided activity. Regarding students’ prior coursework, students with high school-only physics background outperformed students who have been previously exposed to college-level physics courses 8% higher in the posttest average score. Similarly, students overall agreed that they enjoyed using the haptic device for learning and found the technology as easy to interact with. Implications for teaching and learning are provided as well as venues for future work.

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.

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.

Scrum methodology is widely used in the information technology (IT) industry for the purposes of team-based iterative software development. However, limited studies have been conducted to explore the nature of interactions between a Scrum Master and other team members and the effect of these interactions on team effectiveness. The aim of this study is to understand the interactions between the Scrum Master and other team members in an educational setting and propose and demonstrate an application of cooperative game theory for the same. Cooperative game theory can model scenarios where other team members can benefit from cooperating. Through the lens of the cooperative game-theoretic model, we investigated the strategies employed by the Scrum Master and other team members when involved in a semi-capstone IT project. Specifically, the study explored the team interaction between a Scrum Master and other team members at three different levels of team effectiveness: least effective, partially effective, and most effective. Our results indicate that a Scrum Master should be active to maximize their payoff as well as the teams’ overall payoff. Contrary to this, other team members should be active in the most and partially effective teams, while being passive in the least effective teams at higher costs of interpersonal relations and the processes. The results of the study represent a novel application of game-theoretic modeling for understanding the Scrum Master and other team member interactions. These results are applicable not just in an educational setting but also to the wider area of software development by identifying the right set of strategies by the Scrum Master, and other team members in order to help IT professionals to maximize their payoff.

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

The research aim of this article is to investigate the adoption patterns of HUB platforms that create and support virtual learning communities (VLC). The adoption patterns of one particular HUB called the Collaboratory for Engineering Education Research or CLEERhub, is presented as an example of how HUBs may be used as VLCs. After explaining the affordances of the HUB architecture, the article uses two approaches to discuss the adoption of CLEERhub by users. First, the authors link the five stages of Rogers’ Diffusion of Innovation model with various CLEERhub user metrics. The resultant mapping suggests that CLEERhub users are primarily in early stages of adoption. This is not an unexpected finding given that CLEERhub has been recently created. The second approach to studying adoption investigates the experience of a group of college students who used CLEERhub to aid them in completing a group assignment. A CLEERhub Usage Survey was developed and implemented during the last part of the semester to collect information about students’ experience with CLEERhub. Student reactions to CLEERhub were generally positive. After the two approaches are presented, the paper connects the approaches by speculating on how student experience (adoption approach 2) might be mapped to the five stages of Rogers’ model (adoption approach 1). The paper ends with considerations and suggestions for best practices.