Enhancing Teamwork and Collaboration: A Systematic Review of Algorithm-Supported Pedagogical Methods

• Published in: Education sciences Vol. 14; no. 6; p. 675
• Main Authors: Wang, Xunan, Song, Ge, Ghannam, Rami
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Academic achievement; algorithm; Algorithms; Cognitive style; collaboration; Collaborative learning; Cooperative learning; Design; Educational evaluation; Educational objectives; Educational Practices; Genetic algorithms; Group work in education; Homogeneous Grouping; Keywords; learning; Learning Processes; Learning strategies; Machine learning; Pedagogy; Skills; Social interaction; Social networks; Students; Systematic review; Teachers; Teaching; Teaching Methods; Team learning approach in education; Teams; Teamwork
• ISSN: 2227-7102; 2227-7102
• DOI: 10.3390/educsci14060675

In today’s interconnected world, teamwork and collaboration are becoming essential competencies across all disciplines. This review examines various pedagogical strategies aimed at nurturing these skills, with a specific focus on integrating algorithms into educational practices. While traditional approaches classify teamwork strategies as either instructor-led or student-led, this review introduces a third method that is based on ML algorithms, which are promising methods for optimizing team composition based on both static and dynamic student characteristics. We investigate the effectiveness of these algorithms in enhancing collaborative learning outcomes compared to conventional grouping methods. In fact, this review synthesizes the findings from 20 key studies on the implementation of these technologies in educational settings, evaluating their impact on learning outcomes, student motivation and overall satisfaction. Our findings suggest that computer-enhanced strategies not only improve the academic and collaborative experience but also pave the way for more personalized and dynamic educational environments. This review aims to provide educators and curriculum developers with comprehensive insights into leveraging advanced computational tools to foster effective teamwork and interdisciplinary collaboration, thereby enhancing the overall quality of education and preparing students for the collaborative demands of the professional world.