Towards an Ontology-based Recommender System for Assessment in a Collaborative Elearning Environment

BackgroundProject‐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.

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Towards an Ontology-based Recommender System for Assessment in a Collaborative Elearning Environment

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Content Recommendations for Knowledge Refreshing: a Case Study

Content Recommendations for Knowledge Refreshing: a Case Study

Intelligent Collaborative Assessment for Cyberspace eLearning Environments

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

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