Guest Editorial: Managing Cognitive Load in Technology-Based Learning Environments

By Kalyuga, Slava; Liu, Tzu-Chien | Educational Technology & Society, October 2015 | Go to article overview

Guest Editorial: Managing Cognitive Load in Technology-Based Learning Environments


Kalyuga, Slava, Liu, Tzu-Chien, Educational Technology & Society


Introduction

Technology-based learning environments offer a wide range of educational opportunities that could not be achieved in traditional face-to-face forms of learning and instruction. For example, learners could be flexible in using their time, have increased levels of control over the content and pace of presentation, and have information independently delivered to a convenient place. However, as with all technology applications, the use of technology by itself may not guarantee sufficient benefits for learning to warrant investing into the development of such applications. In particular, technology applications may pose additional processing demands on learners' cognitive resources, which may negatively affect the construction of new knowledge. In general, empirical research into technology-based learning has produced mixed findings that support the above concern and make clear that the analyses of associated cognitive processes and structures are essential for enhancing the effectiveness of technology-based learning environments. The cognitive load imposed on learners in technology-based environments is the main focus of this special issue, and cognitive load theory is the main theoretical framework for all the contributing papers.

Over the past decades, cognitive load theory has advanced rapidly and has been applied to enhance the effectiveness of learning and instruction in various subject areas (see Sweller, Ayres, & Kalyuga, 2011, for the most recent overview of the theory). In addition to a great number of individual papers reporting theoretical and empirical studies within a cognitive load framework published every year in multiple journals in the areas of educational psychology, learning and instruction, and the related fields, there have been several special edited issues intended to guide the new directions for the development of cognitive load theory and also document the contributions of cognitive load theory to instructional design across different disciplines, for example, the special issues published in Educational Psychologist (2003), Educational Technology Research and Development (2005), Learning and Instruction (2009), Educational Psychology Review (2007, 2010, 2015), Instructional Science (2010). Although a number of recent papers have considered cognitive load factors influencing learning in technology-based environments (e.g., Liu, Lin, Tsai, & Paas, 2012; Kalyuga, 2012), this is essentially the first special issue specifically devoted exclusively to discussing the innovative technology-supported learning from a cognitive load perspective.

Cognitive load theory

Cognitive load theory (CLT) is an instructional theory developed to coordinate instructional design and learning procedures with human cognitive architecture (Sweller, Van Merrienboer, & Paas, 1998; Sweller et al., 2011). In the most general form, this cognitive architecture consists of two main components: (1) working memory as our main processor of information with a very limited capacity and duration when dealing with novel, unorganized information, and (2) effectively unlimited long-term memory storing cognitive schemas (knowledge structures we use to categorize information for intended use) that vary in their degree of complexity and automation. These two components of human cognitive architecture are closely interrelated with each other.

On the one hand, new knowledge structures that are consciously constructed in working memory become a part of the knowledge base in long-term memory. The conscious construction in working memory is the main way of the acquisition of new knowledge in educational settings. It is different from the way we acquire common skills such as speaking and listening in everyday native language, basic social interaction skills, etc. We are believed to be genetically predisposed to acquire such skills in an unconscious, intuitive way by being immersed in the corresponding social environments (Geary, 2007). …

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