M.A. Steenbergen
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3 records found
1
Fairness in Student Group Formation
Perspectives, Priorities, Compromises, Mechanisms, and Tooling
Conference paper
(2026)
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Matthew Forshaw, Adriana Alexandru, Caitlin Bentley, Vladimiro González-Zelaya, Vangel Ajanovski, Mireilla Bikanga Ada, Julian Brooks, Joshua Burridge, Merel Steenbergen, More authors...
Allocating students to groups is a critical yet under-researched challenge in computing education, with significant implications for fairness and student outcomes. Little is known about existing allocation approaches and their treatment of fairness, whilst practical realities faced by educators and students remain largely undocumented. Without rigorous attention to fairness, group formation risks amplifying bias and disadvantaging vulnerable students. This study provides the first holistic exploration of fairness in group formation within higher-education computing contexts through a systematic review of 262 papers, analysis of 18 allocation tools, interviews with 20 educators, and six student focus groups. Findings reveal a lack of evidence linking fairness definitions to groupwork characteristics, processes, and outcomes. To address this gap, we propose a framework for pedagogically informed group formation that embeds fairness, supporting educator decision-making and improving student experiences. We also establish definitions of fairness, groupwork characteristics, and processes to guide future research in computing education.
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Allocating students to groups is a critical yet under-researched challenge in computing education, with significant implications for fairness and student outcomes. Little is known about existing allocation approaches and their treatment of fairness, whilst practical realities faced by educators and students remain largely undocumented. Without rigorous attention to fairness, group formation risks amplifying bias and disadvantaging vulnerable students. This study provides the first holistic exploration of fairness in group formation within higher-education computing contexts through a systematic review of 262 papers, analysis of 18 allocation tools, interviews with 20 educators, and six student focus groups. Findings reveal a lack of evidence linking fairness definitions to groupwork characteristics, processes, and outcomes. To address this gap, we propose a framework for pedagogically informed group formation that embeds fairness, supporting educator decision-making and improving student experiences. We also establish definitions of fairness, groupwork characteristics, and processes to guide future research in computing education.
Conference paper
(2025)
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Matthew Forshaw, Cristina Adriana Alexadru, Caitlin Bentley, Vladimiro González-Zelaya, Joseph Kwame Adjei, Vangel Ajanovski, Mireilla Bikanga Ada, Julian Brooks, Merel Steenbergen, More authors...
Allocating students to projects is a commonplace task in computing education. These decisions underpin student-supervisor allocation, the formation of tutee and capstone groups, and pair programming. These allocations play a critical role for individual learner outcomes and the success of collaborative interventions. For example, imbalance in either gender, ethnicity, or nationality can negatively impact learner outcomes. Despite the critical importance of these allocation choices, we see little consensus on how these are implemented. The allocation task can be challenging and time-consuming for instructors of even moderately-sized classes, and the fairness implications can be difficult to assess. Inadvertently, an instructor may allocate in a way that amplifies existing biases or disproportionately harms those from disadvantaged or protected groups. From students' perspectives, a lack of transparency on the allocation process may also lead to issues of trust. The Working Group will undertake a study of allocation practices by bringing together educational and ML literature to develop and evaluate the fairness of allocation methods, and develop educator guidelines to promote pedagogically grounded allocation practices.
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Allocating students to projects is a commonplace task in computing education. These decisions underpin student-supervisor allocation, the formation of tutee and capstone groups, and pair programming. These allocations play a critical role for individual learner outcomes and the success of collaborative interventions. For example, imbalance in either gender, ethnicity, or nationality can negatively impact learner outcomes. Despite the critical importance of these allocation choices, we see little consensus on how these are implemented. The allocation task can be challenging and time-consuming for instructors of even moderately-sized classes, and the fairness implications can be difficult to assess. Inadvertently, an instructor may allocate in a way that amplifies existing biases or disproportionately harms those from disadvantaged or protected groups. From students' perspectives, a lack of transparency on the allocation process may also lead to issues of trust. The Working Group will undertake a study of allocation practices by bringing together educational and ML literature to develop and evaluate the fairness of allocation methods, and develop educator guidelines to promote pedagogically grounded allocation practices.
Collaboration skills are essential for computer science (CS) students, yet many graduates enter the workforce lacking proficiency in collaboration skills. This research aims to identify missing collaboration skills in CS curricula, investigate the impact of prior programming experience on teamwork, and explore the use of software metrics to assess collaboration. Following the Educational Design Research approach, this thesis will lead to actionable recommendations for CS educators and supporting educational materials. The main expected contribution is a practical solution for integrating collaboration skill development into programming project courses which has been designed and prototyped over multiple iterations with testing and evaluation phases.
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Collaboration skills are essential for computer science (CS) students, yet many graduates enter the workforce lacking proficiency in collaboration skills. This research aims to identify missing collaboration skills in CS curricula, investigate the impact of prior programming experience on teamwork, and explore the use of software metrics to assess collaboration. Following the Educational Design Research approach, this thesis will lead to actionable recommendations for CS educators and supporting educational materials. The main expected contribution is a practical solution for integrating collaboration skill development into programming project courses which has been designed and prototyped over multiple iterations with testing and evaluation phases.