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

Li, Xu; Ouyang, Fan; Chen, Wenzhi

doi:10.1007/s12528-022-09321-6

Cited by 16 publications

(25 citation statements)

References 63 publications

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“…This research used social network analysis (SNA) and quantitative content analysis (QCA) to analyze those two types of engagement dimensions. On the social engagement dimension, this research used the group-level SNA metrics to reveal groups’ social engagement attributes, including density, average path length (APL), average degree, average closeness, average betweenness, global clustering coefficients (GCC), centralization, the inverse coefficient of variation (ICV) of interaction (refer to Table 2 in Li et al, 2022 ). We first made network data in the excel files to record students’ interaction with peers at the group level.…”

Section: Methodsmentioning

confidence: 99%

Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course

Ouyang

Zheng

et al. 2023

Int J Educ Technol High Educ

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121

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As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI prediction models focus on the development and optimization of the accuracy of AI algorithms rather than applying AI models to provide student with in-time and continuous feedback and improve the students’ learning quality. To fill this gap, this research integrated an AI performance prediction model with learning analytics approaches with a goal to improve student learning effects in a collaborative learning context. Quasi-experimental research was conducted in an online engineering course to examine the differences of students’ collaborative learning effect with and without the support of the integrated approach. Results showed that the integrated approach increased student engagement, improved collaborative learning performances, and strengthen student satisfactions about learning. This research made contributions to proposing an integrated approach of AI models and learning analytics (LA) feedback and providing paradigmatic implications for future development of AI-driven learning analytics.

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Section: Methodsmentioning

confidence: 99%

Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course

Ouyang

Zheng

et al. 2023

Int J Educ Technol High Educ

Self Cite

121

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“…For example, Lambić et al (2018) have designed a variable neighbourhood search algorithm to generate heterogeneous groups that takes into account the pretest scores, interpersonal dynamics, prosocial behaviours, and levels of openness of learners in higher education. In addition, Li et al (2022) have proposed a genetic algorithm‐based heterogeneous grouping approach that tries to maximize the diversity of learners' characteristics within a group in higher education. The current research primarily focuses on the heterogeneous grouping approach, leaving a gap in the literature regarding the effectiveness of homogeneous grouping methods for adult learners.…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of collaborative filtering‐based peer recommendation mechanism on in‐service teachers’ learning performance, knowledge construction, and social network during online training

Gong

Zeng

2023

Computer Assisted Learning

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BackgroundCollaborative learning has become a crucial approach to promoting online in‐service teacher training. Appropriate peer recommendation for group composition is the basis to ensure productive learning outcomes of collaborative learning. However, there is a lack of understanding of the impact of peer recommendation on in‐service teachers' online training.ObjectivesTherefore, based on the collaborative filtering approach, a peer recommendation mechanism for in‐service teachers was constructed in this study. We investigated the effects of the constructed mechanism on in‐service teachers' online learning performance, interactive knowledge construction behavioural patterns and social networks.MethodsIn this study, 82 in‐service teachers were recruited to participate in the study. Participants under the experimental condition (n = 41) were invited to apply collaborative filtering‐based peer recommendation mechanism for grouping, while participants under the control condition (n = 41) were invited to use random grouping method. Participants' interaction data were collected from online collaborative discussion activities in a 5‐week asynchronous online course. The pre‐post knowledge test, content analysis, lag sequential analysis and social network analysis were used to analyse the differences between groups.Results and ConclusionsThe following findings were revealed: (1) the experimental group performed better than the control group in terms of online learning performance; (2) interactive knowledge construction behavioural patterns generated in collaborative activities showed that the experimental group achieved deeper level of knowledge construction in collaboration than the control group; (3) social networks generated in collaborative activities showed that the experimental group had tighter interaction relationships than the control group. Therefore, the peer recommendation mechanism could be useful to improve peer recommendation for group composition and had positive effects on in‐service teachers' online training.ImplicationsThis study can shed light on the construction of peer recommendation mechanism in recommending appropriate peers for in‐service teachers and different learners in online collaborative learning.

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“…Regarding the nature of groups, heterogeneous grouping is the most widely used grouping type because it can better satisfy diverse learning scenarios, especially in cooperative education, as used by [4], [5], [6], [8], [12], [13], [14], [15], [18], [26], and [28] whereas [19] developed an algorithm to generate homogeneous groups. Some research has focused on intra-and inter-group relationships.…”

Section: Literaturementioning

confidence: 99%

“…In these cases, it becomes necessary to use heuristic search methods to find a satisfactory solution with a considerably lower computational effort. A widely used heuristic method is the genetic algorithm (GA), which is used in [5], [11], [13], [15], [16], [19], [27], and [28]. The study [7] used simulated annealing (SA) to form student groups based on past academic records.…”

Section: Literaturementioning

confidence: 99%

“…There are many characteristics of the students on which the group formation depends, such as their knowledge level, personality traits, communication skills, etc. These characteristics were classified in [13] into static, such as gender, age, and knowledge level, and dynamic, such as interaction level or emotional status. The multiplicity of these characteristics and the multiplicity of students make the grouping process an NPhard problem, as confirmed in [16], and so difficult to solve manually.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Cited by 16 publications

References 63 publications

Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course

Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course

Effects of collaborative filtering‐based peer recommendation mechanism on in‐service teachers’ learning performance, knowledge construction, and social network during online training

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

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