Team-based Project Courses Research Articles

Seeing into your Teams: An Instructor Interface to Support Team Learning

This paper outlines the design of a new instructor interface that has been added to the on-line Team-effectiveness Learning System (TELS) at the University of Toronto. TELS is a tool that supports team-based project courses by facilitating the development of individual team-effectiveness competencies in students within their teams. Instructors saw the system as beneficial for student growth, but they also saw opportunities for them to get a better understanding of the state of their student teams. As a result, TELS has now developed an instructor interface to let instructors “see” into their teams. The instructor interface has been adopted by four courses since its development

Using Essence Reflection Meetings in Team-based Project Courses

Background Many software engineering curriculum contain a team-based project course. This is the case of Carnegie Mellon University Silicon Valley's masters of science in software engineering. In this context, we have been using Essence Reflection Meetings for five semesters with 17 teams and approximately 70 students. During these meetings, the teams reflect on various project's dimensions based on a systems thinking framework. The positive results have been published in research papers. Activity and Discussions Participants will learn about Essence Reflection Meetings for team-based project courses by practicing in a classroom environment. They will discuss challenges and solutions for team-based project courses, and how the proposed approach could potentially be leveraged in their own teaching environment. Organization We will start the tutorial with a discussion revealing the participants positive and negative experiences with team-based projects. After briefly introducing the Essence's systems thinking framework and our research results, we will use hands-on training exercises to demonstrate how to use the approach. This will be followed with guided debriefing. Finally, we will go deeper into the Essence framework, and discuss our research results and their applicability in various teaching environments. Learning Objectives By the end of the tutorial, participants will be familiar with a systems thinking framework that they can leverage to coach their students teams and monitor their progress. They will be able to articulate the pros and cons of applying the approach in their own teaching environment.

Towards Generating Essence Kernels Using Genetic Algorithms

Abstract The Software Engineering Method and Theory (SEMAT) community created the Essence kernel as a unifying framework for describing and analyzing software engineering endeavors. The Essence kernel is based upon human experience and judgment, not empirical data. Background At Carnegie Mellon University in Silicon Valley, we have collected data from masters of science in software engineering students as they complete a team-based project course as their capstone or practicum project using the Essence kernel. Each week, the team recorded their progress in an Essence Reflection meeting. This data serves as training data for evaluating the Essence kernel and alternative candidate kernels. Objective Generate candidate replacement kernels by using a fitness function based on empirical data. Method Using genetic programming, the kernel genotype is represented as a collection of linear state machines each with a collection of unique checklist items. Operations to evolve the genotypes include randomly moving checklist items, splitting states, and deleting states by moving their checklist items to other states. Results Genetic programming created random candidate essence kernels that scored higher fitness scores than the original essence kernel. The purpose of this exploratory work is to demonstrate one way to generate a candidate Essence kernel directly from empirical data, not to recommend a replacement for the original Essence kernel. Reducing the Essence kernel from seven alphas to one alpha results in higher fitness scores. Limitations Given the limited amount of data, the generated kernels may be over-optimized. Additional empirical data is required before recommending replacing the original kernel with a candidate kernel that fits the data. Conclusion The original Essence kernel is highly structured around human notions of order. Genetic algorithms can generate candidate kernels that humans might not normally consider. Based on the analysis of the fitness function, a kernel with a fundamentally different structure might more effectively recommend next steps for a team during Essence Reflection Meetings.

Incoming Leadership-Oriented Differences between Students in a Leadership Studies Course and a Team-based Project Course

This study examined the incoming leadership-oriented differences between students (N=166) enrolled in either an elective leadership studies course (n=50) or an elective team-based engineering projects course (n=116) to determine significant predictors of transformational leadership behavior. Participants completed measures of leadership-oriented behaviors, self-efficacy, and motivation. Students enrolled in the leadership studies course scored higher on measures of both transformational and transactional leadership behaviors, as well as motivation to lead based on affective identity and social-normative motivation. For students in the leadership course, the only significant predictor of transformational leadership was leadership-self-efficacy score. For students interested in team-based projects, the significant predictors included affective-identity and social-normative motivation to lead, as well as leadership self-efficacy. While women displayed higher motivation to lead across all motivation categories, neither race nor gender emerged as a significant predictor of leadership behaviors. These findings suggest the importance of self-efficacy in predicting behavior and the need to attend to students’ internal and external motivations in creating pathways to leadership practices.

Incoming Leadership-Oriented Differences between Students in a Leadership Studies Course and a Team-based Project Course

This study examined the incoming leadership-oriented differences between students (N=166) enrolled in either an elective leadership studies course (n=50) or an elective team-based engineering projects course (n=116) to determine significant predictors of transformational leadership behavior. Participants completed measures of leadership-oriented behaviors, selfefficacy, and motivation. Students enrolled in the leadership studies course scored higher on measures of both transformational and transactional leadership behaviors, as well as motivation to lead based on affective identity and social-normative motivation. For students in the leadership course, the only significant predictor of transformational leadership was leadershipself-efficacy score. For students interested in team-based projects, the significant predictors included affective-identity and social-normative motivation to lead, as well as leadership selfefficacy. While women displayed higher motivation to lead across all motivation categories, neither race nor gender emerged as a significant predictor of leadership behaviors. These findings suggest the importance of self-efficacy in predicting behavior and the need to attend to students’ internal and external motivations in creating pathways to leadership practices.

Supporting collaborative learning in engineering design

In team-based project courses, collaborative learning is the dominant learning mode. Collaborative learning has been shown to increase individual learning through co-construction and personal reflection. Rapid adoption of web-based communication and mobile computing by students provide opportunities to take advantage of computer-supported collaboration for engineering education. We present preliminary findings on a computer environment, the Kiva Web, that supports the activities of group collaboration for interdisciplinary engineering design teams. We have employed methods from human–computer interaction (HCI) to iterate on the design in the context of use. In this paper, we discuss the evolution of the Kiva Web and the implications for both student design teams and professional design teams.

Real-world project management in the academic environment

Instructors of many team-based project courses commonly focus on technical aspects such as experimenting with the best software development methods, developing processes that improve important soft...