Evaluating Learning Style-Based Grouping Strategies in Real-World Collaborative Learning Environment

Collaborative learning is defined as situations where multiple learners participate in solving common problems. Collaborative learning provides a way of building knowledge through activities of collaboration with others. Group work is a representative form of collaborative learning and has been used in higher education. In group work, however, one of the widely discussed issues is group composition. Students have different attributes including learning styles, background knowledges, gender, and so on. Typical group formations are homogeneous and heterogeneous compositions. Numerous work addressed the problem and evaluated how learning outcome varies between different group formations both in online and physical environments. In this study, we focus on the group formation for real-world collaboration. We introduce different types of grouping into a class of a theme-based course and discuss the effects of different learning styles in collaborative learning environment. Students are characterized according to Kolb’s learning style inventory and then grouped by homogeneous, heterogeneous, and random strategies. We investigate how intra-group interactions varies with different types of composition; we monitor the activity levels of every group and have students peer-review each other for quantitative evaluation of contributions. We find typical patterns of activities and contributions, and discuss their association to grouping strategies.

[1]  Benjamin Fonooni Rational-Emotional Agent Decision Making Algorithm Design with OWA , 2007 .

[2]  Chuen-Tsai Sun,et al.  DIANA: A computer-supported heterogeneous grouping system for teachers to conduct successful small learning groups , 2007, Comput. Hum. Behav..

[3]  T. C. Bello COMPARISON OF ELEVEN MAJOR LEARNING STYLES MODELS: VARIABLES, APPROPRIATE POPULATIONS, VALIDITY OF INSTRUMENTATION, AND THE RESEARCH BEHIND THEM , 1990 .

[4]  F. Sudweeks,et al.  Teaching for e-Learning in the knowledge society: Promotingconceptual change in academics’ approaches to teaching , 2006 .

[5]  D. Kolb Experiential Learning: Experience as the Source of Learning and Development , 1983 .

[6]  Demetrio Arturo Ovalle Carranza,et al.  A genetic algorithm approach for group formation in collaborative learning considering multiple student characteristics , 2012, Comput. Educ..

[7]  David W. Johnson,et al.  AN OVERVIEW OF COOPERATIVE LEARNING , 2002 .

[8]  D. Harrison,et al.  Time, Teams, and Task Performance: Changing Effects of Surface- and Deep-Level Diversity on Group Functioning , 2002 .

[9]  Yen-Ting Lin,et al.  An automatic group composition system for composing collaborative learning groups using enhanced particle swarm optimization , 2010, Comput. Educ..

[10]  Kyparisia A. Papanikolaou,et al.  A Group Formation Tool in an E-Learning Context , 2007 .

[11]  Juan Manuel Adán Coello,et al.  Forming Groups for Collaborative Learning of Introductory Computer Programming Based on Students' Programming Skills and Learning Styles , 2011, Int. J. Inf. Commun. Technol. Educ..

[12]  Katsuaki Suzuki,et al.  Group Formation for Web-Based Collaborative Learning with Personality Information , 2005 .

[13]  Estefanía Martín,et al.  Using Learning Styles for Dynamic Group Formation in Adaptive Collaborative Hypermedia Systems , 2004, ICWE Workshops.

[14]  The Effect of Learning Styles on Group Work Activities , 2012 .

[15]  Gwo-Jen Hwang,et al.  A Learning Style Perspective to Investigate the Necessity of Developing Adaptive Learning Systems , 2013, J. Educ. Technol. Soc..

[16]  Chih-Ping Chu,et al.  A Genetic Algorithm-Based Multiple Characteristics Grouping Strategy for Collaborative Learning , 2013, ICWL Workshops.

[17]  P. Dillenbourg What do you mean by collaborative learning , 1999 .

[18]  B. Davis Tools for Teaching , 1993 .

[19]  S. Jackson,et al.  Recent Research on Team and Organizational Diversity: SWOT Analysis and Implications , 2003 .