An Exact Algorithm for Group Formation to Promote Collaborative Learning

Sun, Zhiru; Chiarandini, Marco

doi:10.1145/3448139.3448196

Cited by 4 publications

(5 citation statements)

References 15 publications

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“…In this regard, it exceeded the traditional methods (self-grouping and random grouping methods) that have been adopted in building learning groups. Similarly, these results agree with what is presented in [16], [19], [25], and [27], which propose to improve both the internal and external relationships of the groups. In spite of the similarities in the obtained results, the methods used for measuring the effectiveness of algorithms were different.…”

Section: Discussionsupporting

confidence: 92%

“…In spite of the similarities in the obtained results, the methods used for measuring the effectiveness of algorithms were different. For instance, [16], [19], [25], and [27] measure the effect of the grouping algorithm on the students' abilities, while this work measures intra-and inter-homogeneity of groups. The measure of students' ability was allowed for the former because they intended a small group made up of five students for collaborative work, whereas the latter was designed to deal with any size of group that may be greater than twenty students.…”

Section: Discussionmentioning

confidence: 99%

“…Some research has focused on intra-and inter-group relationships. In this context, [16], [19], [25], and [27] propose approaches to achieve groups with members that are as similar as possible (inter-homogeneous) but also to enable individual differences among students within such groups (intra-heterogeneous).…”

Section: Literaturementioning

confidence: 99%

“…[16], [20], [21], and [25], developed automatic grouping methods that achieve collaborative learning outcomes. The phrase "cold start" was used in [13] to denote the problem of the inaccessibility of students' characteristics, such as personality traits, communication skills, and leadership capacities, at the starting point.…”

Section: Literaturementioning

confidence: 99%

See 3 more Smart Citations

An Algorithm Based on Self-balancing Binary Search Tree to Generate Balanced, Intra-homogeneous and Inter-homogeneous Learning Groups

Ammar¹,

Minalla²

2023

IJACSA

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This paper presents an algorithm, based on the self-balancing binary search tree, to form learning groups. It aims to generate learning groups that are intra-homogeneous (student performance similarity within the group), interhomogeneous (group performance similarity between groups), and of balanced size. The algorithm mainly uses the 2-3 tree and the 2-3-4 tree as two implementations of a self-balancing binary search tree to form student blocks with close GPAs (grade point averages) and balanced sizes. Then, groups are formed from those blocks in a greedy manner. The experiment showed the efficiency of the proposed algorithm, compared to traditional forming methods, in balancing the size of the groups and improving their intra-and inter-homogeneity by up to 26%, regardless of the used version of the self-balancing binary search tree (2-3 or 2-3-4). For small samples of students, the use of the 2-3-4 tree was distinguished for improving intra-and interhomogeneity compared to the 2-3 tree. As for large samples of students, experiments showed that the 2-3 tree was better than the 2-3-4 tree in improving the inter-homogeneity, while the 2-3-4 tree was distinguished in improving the intra-homogeneity.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Literaturementioning

confidence: 99%

Section: Literaturementioning

confidence: 99%

See 2 more Smart Citations

An Algorithm Based on Self-balancing Binary Search Tree to Generate Balanced, Intra-homogeneous and Inter-homogeneous Learning Groups

Ammar¹,

Minalla²

2023

IJACSA

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“…Sun & Chiarandini [22] propose a novel method to form intra-heterogeneous and inter-homogeneous groups based on relevant students' characteristics. This method allows the consideration of multiple characteristics of the students and can handle both numerical and categorical types of characteristics simultaneously.…”

Section: Related Workmentioning

confidence: 99%

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

Sánchez¹,

Ordóñez²,

Duque³

2021

IxD&A

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Considering that group formation is one of the key processes when developing activities in collaborative learning contexts, this paper aims to propose a technique based on an approach of genetic algorithms to achieve homogeneous groups, considering the students' personality traits as grouping criteria. For its validation, an experiment was designed with 132 first semesters engineering students, quantifying their personality traits through the “Big Five Inventory”, forming workgroups and developing a collaborative activity in initial Programming courses. The experiment made it possible to compare the results obtained by the students applying the proposed approach to those obtained through other group formation strategies. It was demonstrated through the experiment that the homogeneous groups generated by the proposed technique produce better academic results compared to the grouping technique by students’ preference, traditionally used by the teachers when developing a collaborative activity.

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Dynamic Group Formation With Intelligent Tutor Collaborative Learning: A Novel Approach for Next Generation Collaboration

et al. 2021

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An Exact Algorithm for Group Formation to Promote Collaborative Learning

Cited by 4 publications

References 15 publications

An Algorithm Based on Self-balancing Binary Search Tree to Generate Balanced, Intra-homogeneous and Inter-homogeneous Learning Groups

An Algorithm Based on Self-balancing Binary Search Tree to Generate Balanced, Intra-homogeneous and Inter-homogeneous Learning Groups

Group formation in collaborative learning contexts based on personality traits: An empirical study in initial Programming courses

Dynamic Group Formation With Intelligent Tutor Collaborative Learning: A Novel Approach for Next Generation Collaboration

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