Group instruction and web-based instructional approaches for training student employees

Gibbs, William J.; Chen, Carrie; Bernas, Ronan S.

doi:10.1007/bf02940945

Cited by 2 publications

(1 citation statement)

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“…As previous studies indicated, there are two major challenges that automatic grouping methods need to address, namely the uneven group size problem (Holenko Dlab et al, 2020 ), and the data inaccessibility (“Cold Start”) problem (Lika et al, 2014 ; Pliakos et al, 2019 ). In CSCL, instructors usually configure a group with the size of 3 or 4 members, as prior research results have showed that a large group size would weaken the group performance (Gibbs et al, 2001 ). But in actual educational situations, a challenge is raised: the class size cannot be divided evenly (i.e., the uneven grouping size problem).…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the effect of a genetic algorithm-enabled grouping method on collaborative performances, processes, and perceptions

Ouyang

Chen

2022

J Comput High Educ

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Group formation is a critical factor which influences collaborative processes and performances in computer-supported collaborative learning (CSCL). Automatic grouping has been widely used to generate groups with heterogeneous attributes and to maximize the diversity of students’ characteristics within a group. But there are two dominant challenges that automatic grouping methods need to address, namely the barriers of uneven group size problem, and the inaccessibility of student characteristics. This research proposes an optimized, genetic algorithm-based grouping method that includes a conceptual model and an algorithm module to address these challenges. Through a quasi-experiment research, we compare collaborative groups’ performance, processes, and perceptions in China’s higher education. The results indicate that the experimental groups outperform the traditional grouping methods (i.e., random groups and student-formed groups) in terms of final performances, collaborative processes, and student perceptions. Based on the results, we propose implications for implementation of automatic grouping methods, and the use of collaborative analytics methods in CSCL.

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