Applying collaborative cognitive load theory to computer-supported collaborative learning: towards a research agenda

Abstract

Research on computer-supported collaborative learning (CSCL) has traditionally investigated how student-, group-, task-, and technological characteristics affect the processes and outcomes of collaboration. On the other hand, cognitive load theory has traditionally been used to study individual learning processes and to investigate instructional effects that are present during individual learning (e.g., expertise reversal effect). In this contribution we will argue that cognitive load theory can be applied to CSCL. By incorporating concepts such as collective working memory (i.e., individuals share the burden of information processing), mutual cognitive interdependence (i.e., individuals learn about each other’s expertise and become dependent on their partners’ expertise), and transaction costs (i.e., the burden placed on individuals working memory capacity when communicating and coordinating collaborative activities), collaborative cognitive load theory (CCLT) can be used to formulate testable hypotheses for pressing issues in CSCL research. The aim of this paper is to develop a research agenda to guide future CSCL research from a CCLT perspective. We highlight how variables associated with student-, group-, task-, and technological characteristics may be investigated using CCLT. We also address important steps CSCL research needs to make with respect to the measurement of variables and the methodologies used to analyze data.