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Abstract BackgroundEffectively 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. ResultsFindings 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. ConclusionThe 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. 

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. 

This study proposes and demonstrates how computer‐aided methods can be used to extend qualitative data analysis by quantifying qualitative data, and then through exploration, categorization, grouping, and validation. Computer‐aided approaches to inquiry have gained important ground in educational research, mostly through data analytics and large data set processing. We argue that qualitative data analysis methods can also be supported and extended by computer‐aided methods. In particular, we posit that computing capacities rationally applied can expand the innate human ability to recognize patterns and group qualitative information based on similarities. We propose a principled approach to using machine learning in qualitative education research based on the three interrelated elements of the assessment triangle: cognition, observation, and interpretation. Through the lens of the assessment triangle, the study presents three examples of qualitative studies in engineering education that have used computer‐aided methods for visualization and grouping. The first study focuses on characterizing students' written explanations of programming code, using tile plots and hierarchical clustering with binary distances to identify the different approaches that students used to self‐explain. The second study looks into students' modeling and simulation process and elicits the types of knowledge that they used in each step through a think‐aloud protocol. For this purpose, we used a bubble plot and a k‐means clustering algorithm. The third and final study explores engineering faculty's conceptions of teaching, using data from semi‐structured interviews. We grouped these conceptions based on coding similarities, using Jaccard's similarity coefficient, and visualized them using a treemap. We conclude this manuscript by discussing some implications for engineering education qualitative research. 

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. 

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. 

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.