Search for: All records

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. 

Primary barriers to the adoption of team-based learning in higher education pertain to classroom management difficulties regarding the large class size, no access to physical infrastructure, and the lack of implementation of student-centered pedagogical approaches. To overcome these challenges, this study proposes the use of collaborative technological environments in conjunction with teamwork pedagogy. The study investigates this approach by comparing two implementations of a large-size undergraduate course: (a) thein-personmode when an active learning classroom was assigned to the course, and (b) theblendedmode when a portion of traditional face-to-face instruction was replaced with web-based online learning to facilitate teamwork interactions. The study used the Team Learning Model to characterize students’ beliefs about their collaborative and social processes as they worked in teams as part of a semester-long project. The results indicated that students exhibited positive attitudes toward teamwork regardless of the delivery mode, with only affective connectedness showing significant differences between the two semesters for the initial survey rounds. However, this difference was no longer present in the later survey rounds, suggesting that the blended learning environment was successful in addressing social interaction and had a similar effect on students’ team-based learning when teaching in-person. Implications relate to the demonstration of the design of a collaborative technological learning environment and the integration of team-based pedagogies to facilitate socialization processes in large class size settings. 

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.