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When new information is presented to learners, it must be processed in a severely limited working memory. Learning reduces working memory limitations by enabling the use of schemas, stored in long-term memory, to process information more efficiently. Several instructional techniques have been designed to facilitate schema construction and automation by reducing working memory load. Recently, however, strong evidence has emerged that the effectiveness of these techniques depends very much on levels of learner expertise. Instructional techniques that are highly effective with inexperienced learners can lose their effectiveness and even have negative consequences when used with more experienced learners. We call this phenomenon the expertise reversal effect. In this article, we review the empirical literature on the interaction between instructional techniques and levels of learner experience that led to the identification of the expertise reversal effect.

The interactions between levels of learner prior knowledge and effectiveness of different instructional techniques and procedures have been intensively investigated within a cognitive load framework since mid-90s. This line of research has become known as the expertise reversal effect. Apart from their cognitive load theory-based prediction and explanation, patterns of empirical findings on the effect fit well those in studies of Aptitude Treatment Interactions (ATI) that were originally initiated in mid-60s. This paper reviews recent empirical findings associated with the expertise reversal effect, their interpretation within cognitive load theory, relations to ATI studies, implications for the design of learnertailored instructional systems, and some recent experimental attempts of implementing these findings into realistic adaptive learning environments.Keywords Expertise reversal effect . Prior knowledge . Expertise . Cognitive load theory . Learner-tailored instructionAlthough advantages of individualized learner-tailored instruction have been recognized for long time and continue to be aspired (e.g., see VanLehn et al. 2007 for the most recent manifestation) it still remains a mainly unrealized dream for the majority of educators. Most instructional materials are designed in a fixed, one-for-all fashion, and by default, implicitly if not explicitly, assume novices as intended learners. Unavailability of suitable real-time (online) diagnostic assessment techniques has also impeded the development of learnertailored environments. Because of the involvement of many complex factors, issues of managing cognitive load by adapting instructions to individual learners, although recognized as important, have been mostly avoided by recent research projects in the field. On the other side, specific developmental projects in adaptive e-learning have been focused mostly on technical issues of tailoring instructional content to learner preferences, interests, choices, history of previous on-line behavior etc. and not based on fundamental cognitive characteristics of learners and evidence-based principles of instructional design.

Two experiments investigated alternatives to split‐attention instructional designs. It was assumed that because a learner has a limited working memory capacity, any increase in cognitive resources required to process split‐attention materials decreases resources available for learning. Using computer‐based instructional material consisting of diagrams and text, Experiment 1 attempted to ameliorate split‐attention effects by increasing effective working memory size by presenting the text in auditory form. Auditory presentation of text proved superior to visual‐only presentation but not when the text was presented in both auditory and visual forms. In that case, the visual form was redundant and imposed a cognitive load that interfered with learning. Experiment 2 ameliorated split‐attention effects by using colour coding to reduce cognitive load inducing search for diagrammatic referents in the text. Mental load rating scales provided evidence in both experiments that alternatives to split‐attention instructional designs were effective due to reductions in cognitive load. Copyright © 1999 John Wiley & Sons, Ltd.

Cognitive load theory has been traditionally described as involving three separate and additive types of load. Germane load is considered as a learning-relevant load complementing extraneous and intrinsic load. This article argues that, in its traditional treatment, germane load is essentially indistinguishable from intrinsic load, and therefore this concept may be redundant. Contrary to extraneous and intrinsic load, germane cognitive load was added to the cognitive load framework based on theoretical considerations rather than on specific empirical results that could not be explained without this concept. The design of corresponding learning activities always required methods and techniques external to the theory. The article suggests that the dual intrinsic/extraneous framework is sufficient and non-redundant and makes boundaries of the theory transparent. The idea of germane load might have an independent role within this framework if (as recently suggested by John Sweller) it is redefined as referring to the actual working memory resources devoted to dealing with intrinsic rather than extraneous load.Keywords Cognitive load theory . Germane load . Intrinsic load . Working memory load Cognitive load theory (CLT) is a learning and instruction theory that describes instructional design implications of a model of human cognitive architecture based on a permanent knowledge base in long-term memory (LTM) and a temporary conscious processor of information in working memory. Essential characteristics of working memory are its limited capacity and duration. We can consciously process no more than a few items at a time for no longer than a few seconds. If these limits are exceeded, working memory becomes overloaded and learning inhibited. CLT makes specific instructional design prescriptions for managing working memory load as a key issue for successful learning and performance (see Sweller 2003Sweller , 2004Sweller , 2008 for reviews of the major features of human cognitive architecture and their general instructional implications; Paas et al.