Group Formation In Collaborative Learning Research Articles

Exploring the effects of gender in skills acquisition in collaborative learning based on the ontological clustering model

AbstractBackgroundProject‐based collaborative learning (PBCL) is a technique that supports knowledge and skill development through complex, real‐world projects. Understanding factors that influence group performance in PBCL, such as gender composition, is crucial.ObjectivesThis study investigates the impact of gender composition on group performance in PBCL, specifically focusing on the critical thinking, creativity, communication, and collaboration (4Cs) 21st‐century learning skills.MethodsA total of 312 students were divided into distinct Python programming groups for the experiment. Group performance was assessed using an agglomerative clustering algorithm guided by the ontological group learner model, which examined gender composition, communication patterns, collaboration strategies and project assessment outcomes.Results and ConclusionGender‐balanced groups demonstrated positive outcomes in the 4Cs skills, with groups containing a greater proportion of women exhibiting superior performance in collaboration and communication, whereas majority‐male groups performed relatively poorer across all skills. Notably, in the context of PBCL, the presence of more women in small groups enhanced the 4Cs skills project assessment outcomes.TakeawaysFuture research should focus on providing effective support for male online learners in developing the 4Cs skills. The findings offer insights and recommendations for group formation in collaborative learning, facilitating the design of inclusive and impactful learning environments.

An Approach to Group Formation in Collaborative Learning Using Learning Paths in Learning Management Systems

Forming groups in distance education is challenging for teachers because, with this modality, only 20% of the classes are held in person with the students. Thus, it is essential to achieve satisfactory results with automated approaches that can help teachers. In this article, an automated approach is proposed to assist teachers in recommending groups of students to learning management systems. We developed and validated a conceptual framework for group recommendation for collaborative activities using the characterization of learners based on learning paths (LPs). The approach emphasizes the formation of groups by applying the k-means algorithm, associated with three distance metrics of similarity (i.e., Euclidean, Manhattan, and cosine) in conjunction with the attributes derived from LPs. The framework was validated through the implementation of an M-Cluster tool. The M-Cluster presents three solution options, which can be visualized in a descriptive manner or via a bubble graph; it is the teacher who chooses the most acceptable solution for each case. The results of the case study indicate that the tool shows promise for improving the performance of students to up to 75%.

Cooperative Learning: Adaptive Group Formation for Collaborative Learning

Collaborative learning affects with lot of factors like student’s personality, their interaction patterns, learning styles etc. Grouping of students is one of the important factors. It is important to arrange groups by skills and/or backgrounds. Hence it is noteworthy to create groups based on specific skills of students. Generally the students can be randomly grouped or grouped themselves. But this method of grouping students based on certain features like personality traits can improve the efficiency of collaborative learning. The student’s data can be collected from social networking site like Facebook. The personality of each student can be identified by comparing the individual’s chat history with psycholinguistic databases. The main objectives of this paper are to identify the student’s personality. Based on that, the group of students can be formed using k-means clustering algorithm.

A Genetic Algorithm Approach for Inter and Intra Homogeneous Grouping Considering Multi-student Characteristics

This paper addresses the problem of group formation in collaborative learning by considering the students’ characteristics. The proposed solution is based on a Genetic Algorithm (GA), which minimizes an objective function that has two main aims. Indeed, the proposed GA’s fitness function helps to achieve two objectives: Fairness in the formation of different groups, resulting in intergroup homogeneity, and a low gap in the levels of students within a group, which corresponds to intragroup homogeneity. Exhaustive experiments were conducted using three different sizes of randomly generated data sets and several crossover operators. Indeed, the order crossover and the crossovers based on random keys representation are experimented. The reported results show that the proposed approach guarantees the efficient grouping of students. In addition, comparisons with existing approaches based on GA confirm the ability of the proposed approach to provide greater intergroup and intragroup homogeneity. In addition, the uniform crossover based on random keys representation ensures better grouping quality than do the other experimented crossover operators.

Mapeamento Sistemático na Literatura Acadêmico-Científica sobre Abordagens para Formação de Grupos em E-Learning

This work presents the results of a systematic mapping on scientific papers that discuss the group formation for collaborative learning. The aim is to investigate researches associated with approaches and strategies to form group, in the context of E-Learning domains. Our mapping shows that the majority of publications are focused on analyzing the context of students interactions, along collaborative E-learning activities, and use the acquired knowledge to improve the algorithms for groups optimization. Group types were specified for further experiment in homogeneous grouping, heterogeneous grouping and complementary for programming skills from mathematical modeling and implementation of algorithm grouping. The results of this work produce theoretical fundaments to support the investigation of new group formation approaches, which are based on group profiles and associated with criteria and preferences that were not explored yet. Furthermore, the results enable a more precise analysis of interactions and impact of collaboration on the efficiency of e-learning students.

A ‘Mixed’ Approach to Group Formation in Collaborative Learning

A ‘Mixed’ Approach to Group Formation in Collaborative Learning

A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics

Considering that group formation is one of the key processes in collaborative learning, the aim of this paper is to propose a method based on a genetic algorithm approach for achieving inter-homogeneous and intra-heterogeneous groups. The main feature of such a method is that it allows for the consideration of as many student characteristics as may be desired, translating the grouping problem into one of multi-objective optimization. In order to validate our approach, an experiment was designed with 135 college freshmen considering three characteristics: an estimate of student knowledge levels, an estimate of student communicative skills, and an estimate of student leadership skills. Results of such an experiment allowed for the validation, not only from the computational point of view by measuring the algorithmic performance, but also from the pedagogical point of view by measuring student outcomes, and comparing them with two traditional group formation strategies: random and self-organized.