Expertise Reversal Effect and Its Implications for Learner-Tailored Instruction

Abstract

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.

Training self-assessment and task-selection skills: A cognitive approach to improving self-regulated learning

“…Finally, given the reported relationship between prior knowledge and accuracy of self-assessment, an interesting question would be whether students with higher levels of prior knowledge than the novices who participated in our studies would still benefit from training self-assessment and task-selection skills, or whether such training would be unnecessary or even harmful for them (i.e., an 'expertise reversal effect' might occur; Kalyuga, 2007;Kalyuga, Ayres, Chandler, & Sweller, 2003).…”

Viewing another person's eye movements improves identification of pulmonary nodules in chest x-ray inspection.

“…It is expected that showing eye movement patterns of an expert to novice radiographers will lead to improved decision-making performance. Conversely, showing an expert's eye movement patterns to experienced radiographers may actually disrupt diagnostic performance (e.g., Donovan et al, 2008;Donovan, Manning, Philips, Higham, & Crawford, 2005), a possibility that links to the "expertise reversal effect" (Kalyuga, 2007), whereby instructional techniques designed to reduce cognitive load may inhibit the application of pre-existing skills. We designed Experiment 1 to examine these latter predictions relating to observer expertise.…”

Knowledge elaboration: A cognitive load perspective

“…Recent theoretical generalizations of the theory (Sweller, 2003(Sweller, , 2004(Sweller, , 2007Van Merriën-boer & Sweller, 2005) and empirical studies related to the expertise reversal effect (for a recent overview, see Kalyuga, 2007) emphasized the significance of schema-based executive guidance as another, although closely related, role of LTM schemas in learning.…”

“…For example, experts may need to turn from automated schema-based processing to consciously controlled processing that requires WM resources. Consequently, less capacity could be available for new knowledge acquisition and elaboration, resulting in the expertise reversal effect (Kalyuga, 2005(Kalyuga, , 2007Kalyuga Ayres, Chandler, & Sweller, 2003). For example, Catrambone and Yuasa (2006) suggested a possible expertise reversal effect when relatively knowledgeable learners were required to generate extended elaborations in a familiar area.…”