Assigning Students to Groups: A Multi‐Criteria Decision Support System Approach*

Weitz, Rob R.; Jelassi, M. Tawfik

doi:10.1111/j.1540-5915.1992.tb00415.x

Cited by 50 publications

(27 citation statements)

References 18 publications

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“…It arises in a wide range of real-world settings. Firstly stressed by Weitz and Jelassi (1992) in their work on forming student workgroups, MDGP has a variety of applications in different contexts, such as education, industry, scientific funding agencies or organizations. In business schools, it becomes increasingly co mmon to create diverse student workgroups or training teams in order to provide students a diverse environment (Weitz and Lakshminarayanan, 1998).…”

Section: Introductionmentioning

confidence: 99%

Erratum: A hybrid genetic algorithmic approach to the maximally diverse grouping problem

Fan

Chen

et al. 2011

Journal of the Operational Research Society

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It has come to our notice that several aspects of the above paper were incorrect. The correct version of the paper is reproduced here.The maximally diverse grouping problem (MDGP) is a NP-complete problem. For such NP-complete problems, heuristics play a major role in searching for solutions. Most of the heuristics for MDGP focus on the equal group-size situation. In this paper, we develop a genetic algorithm (GA)-based hybrid heuristic to solve this problem considering not only the equal group-size situation but also the different group-size situation. The performance of the algorithm is compared with the established Lotfi-Cerveny-Weitz algorithm and the nonhybrid GA. Computational experience indicates that the proposed GA-based hybrid algorithm is a good tool for solving MDGP. Moreover, it can be easily modified to solve other equivalent problems.

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Section: Introductionmentioning

confidence: 99%

Erratum: A hybrid genetic algorithmic approach to the maximally diverse grouping problem

Fan

Chen

et al. 2011

Journal of the Operational Research Society

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“…Teachers can get help from a number of applications implementing a diversity of optimal algorithms and heuristic approaches following a given programming model to assign members to groups [17,18,19,20]. Nevertheless, on the one hand, the data used are often imprecise or incomplete, and sometimes fail to capture reality.…”

Section: Teacher-assigned Teamsmentioning

confidence: 99%

“…Thus, group composition criteria are tackled in many works [2,3,4,5] being grouped in three categories: i.…”

Section: Previous Work On Student Groupwork Compositionmentioning

confidence: 99%

“…Thus, alternatives reported in the literature fall [2,3,4,5] within three different approaches depending on whether students are allowed to decide about their partners, the group members are assigned by instructor, or a random assignment method is used. Specifically, in our academic circle, we have observed a clear trend among students of grouping themselves by friendship affinity; moreover, this behaviour seems to become persistent throughout their studies (fact confirmed by the results presented in this paper).…”

Section: Introductionmentioning

confidence: 99%

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A Comparative Study of Two Automated Workgroup Composition Strategies

Calafate¹,

Mart́ınez²,

Arlandis³

2016

INTED2016 Proceedings

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Nowadays, the professional environment of leading companies requires multidisciplinary teams to be created, including both internal and external experts, to adequately face the challenges of a fast-evolving and complex market. For newcomers, this situation can be difficult to handle if no previous experiences allowed preparing them for those situations. This is where college education finds its place, and in fact the curricula of different university careers are being updated to include more transversal competences like leadership and cooperation skills within groups. However, such efforts still remain at an amateur level in most cases due to lack of specific expertise by the majority of instructors, due to limitations in terms of creating actual multidisciplinary groups, and due to the little resources available to maximize the benefits of such experiences. Students also show little involvement in this issue, typically opting for approaches that minimize their efforts. Thus, simple and yet effective strategies that can be helpful for instructors to make a meaningful change to the current status quo are necessary. To address this need, in this paper we present the results of a 2-year study where students were forced to team up with other partners based on the results of a computer networking skills-ranking exam. In the first experiment, groups where formed by students achieving a similar performance (homogeneous), while in the second experiment groups were formed so that the average score of group members was the same (mostly heterogeneous). Through detailed survey results taken by students at the end of both courses, we find that, compared to the alternative strategy promoting group heterogeneity, having partners with similar skills does not help at improving the coordination between group members, nor students' perception about the usefulness of the experience towards future jobs. In fact, their rating of the overall experience was mostly the same, and, to our surprise, the partner expertise on the course topics was not a motivating factor, being that students' complaints in both cases were about not being able to pick their partners. Thus, we consider that a simple strategy to define groups (e.g. randomly) suffices, but it should be adopted on all courses since the first year of the degree.

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“…The MDGP has practical applications in circuit design (Chen, 1986) (Feo & Khellaf, 1990), storage of large codes in paged memory (Kral, 1965) and the assignment of students to groups (Weitz & Jelassi, 1992). The best solution method in the literature is a tabu search with strategic oscillation (Gallego, Laguna, Martí, & Duarte, 2011).…”

Section: Maximally Diverse Grouping Problemmentioning

confidence: 99%

A black-box scatter search for optimization problems with integer variables

et al. 2013

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The goal of this work is the development of a black-box solver based on the scatter search methodology. In particular, we seek a solver capable of obtaining high quality outcomes to optimization problems for which solutions are represented as a vector of integer values. We refer to these problems as integer optimization problems. We assume that the decision variables are bounded and that there may be constraints that require that the black-box evaluator is called in order to know whether they are satisfied. Problems of this type are common in operational research areas of applications such as telecommunications, project management, engineering design and the like. Our experimental testing includes 171 instances within four classes of problems taken from the literature. The experiments compare the performance of the proposed method with both the best context-specific procedures designed for each class of problem as well as context-independent commercial software. The experiments show that the proposed solution method competes well against commercial software and that can be competitive with specialized procedures in some problem classes.

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Assigning Students to Groups: A Multi‐Criteria Decision Support System Approach*

Cited by 50 publications

References 18 publications

Erratum: A hybrid genetic algorithmic approach to the maximally diverse grouping problem

Erratum: A hybrid genetic algorithmic approach to the maximally diverse grouping problem

A Comparative Study of Two Automated Workgroup Composition Strategies

A black-box scatter search for optimization problems with integer variables

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