Algorithmically supported team composition for software engineering project courses

Dzvonyar, Dora; Henze, Dominic; Alperowitz, Lukas; Bruegge, Bernd

doi:10.1109/educon.2018.8363446

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…For instance, [17] considers teams for specific projects, and discusses the balance between skills across team-members, practical constraints such as schedules and also factors such as motivation. [18] presents an algorithmic approach and tool to ease instructor burden in group formation, which is similar to our goals. However, this tool is not open-source, and does not incorporate any machine learning components that we aim to bring in.…”

Section: Group Compositionmentioning

confidence: 99%

“…Since learning is a social experience [60], peer-learning is essential [3,9,50,53], and peer-groups and social networks can have a huge impact on the success of individuals [11,12,21,35,42,58], providing social support is critical for students. The positive impacts and importance of study groups has been extensively studied [7,15,17,18,24,31,33,34,38,41,44,52].…”

Section: Introductionmentioning

confidence: 99%

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Inclusive Study Group Formation At Scale

Kohli¹,

Ramachandran²,

Tudor³

et al. 2022

Preprint

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The student peer-group is one of the most important influences on student development. Group work is essential for creating positive learning experiences, especially in remote-learning where student interactions are more challenging. While the benefits of study groups are established, students from underrepresented communities often face challenges in finding social support for their education when compared with those from majority groups. We present a system for flexible and inclusive study group formation that can scale to thousands of students.Our focus is on long-term study groups that persist throughout the semester and beyond. Students are periodically provided opportunities to obtain a new study group if they feel their current group is not a good fit. In contrast to prior work that generates single-use groups, our process enables continuous refinement of groups for each student, which in turn informs our algorithm for future iterations.We trialed our approach in a 1000+ student introductory Electrical Engineering and Computer Science course that was conducted entirely online during the COVID-19 pandemic. We found that of all students matched to study groups through our algorithm, a large majority felt comfortable asking questions (78%) and sharing ideas (74%) with their group. Students from underrepresented backgrounds were more likely to request software-matching for study groups when compared with students from majority groups. However, underrepresented students that we did match into study groups had group experiences that were just as successful as students from' majority groups. Students in engaged, regularly participating study groups had more positive results across all other indicators of the study group experience, and certain positive group experiences were associated with higher exam scores overall. Furthermore, students performing at a B-level on the first class midterm, who participated in high-quality software-matched study groups, demonstrated higher final exam scores than students in lowerquality groups.

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Section: Group Compositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Inclusive Study Group Formation At Scale

Kohli¹,

Ramachandran²,

Tudor³

et al. 2022

Preprint

View full text Add to dashboard Cite

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Forming and Assessing Student Teams in Software Engineering Courses

Løvold

Lindsjørn

Stray

2020

Lecture Notes in Business Information Processing

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In software development projects, working in teams is essential. Therefore, software engineering courses often require the students to be working in teams to learn about team work behaviors and practices. The instructors of software engineering courses are presented with several challenges when teaching courses that require teamwork. For example, how to form high-performing student teams, and how to assess their work. The aim of this study is to evaluate whether there are differences in performance whether the students form the teams themselves, or if the teams are formed by the instructor. We evaluated a course involving agile software development by 200 students working in 39 teams. A total of 76% of the students chose to form their own teams, the remaining 24% were placed in teams by the instructors. Our findings indicate that teams formed by the students perform slightly better than the teams formed by the instructors.

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Adaptable Concept for Projects in Computer Science Degree Programs – An Experience Report

Liebehenschel

2019

2019 IEEE Global Engineering Education Conference (EDUCON)

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Algorithmically supported team composition for software engineering project courses

Cited by 6 publications

References 32 publications

Inclusive Study Group Formation At Scale

Inclusive Study Group Formation At Scale

Forming and Assessing Student Teams in Software Engineering Courses

Adaptable Concept for Projects in Computer Science Degree Programs – An Experience Report

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