Do Learners Really Know Best? Urban Legends in Education

This article takes a critical look at three pervasive urban legends in education about the nature of learners, learning, and teaching and looks atwhat educational and psychological research has to say about them. The three legends can be seen as variations on one central theme, namely, that it is the learner who knows best and that she or he should be the controlling force in her or his learning. The first legend is one of learners as digital natives who form a generation of students knowing by nature how to learn from new media, and for whom “old” media and methods used in teaching/learning no longer work. The second legend is the widespread belief that learners have specific learning styles and that education should be individualized to the extent that the pedagogy of teaching/learning is matched to the preferred style of the learner. The final legend is that learners ought to be seen as self-educators who should be given maximum control over what they are learning and their learning trajectory. It concludes with a possible reason why these legends have taken hold, are so pervasive, and are so difficult to eradicate

Learner-controlled selection of tasks with different surface and structural features: Effects on transfer and efficiency

Surface task features are more salient than structural task features and thus easier to recognize for novices. It is predicted that the more salient the task features the better learners can choose personally relevant and varied tasks, which enhances learning transfer. To investigate this prediction, a 2 x 2 factorial experiment with 72 participants studied the effects of control over tasks that differ in their surface features (learner, program) and in their structural features (learner, program). Learner control over the selection of tasks with salient surface features enables learners to select personally relevant and varied tasks. This is believed to yield higher effectiveness (i.e., higher near and far transfer test performance) as well as higher efficiency (i.e. higher transfer test performance combined with lower associated mental effort). Learner control over the selection of tasks with non-salient structural features does not enable learners to select personally relevant and varied tasks and is therefore not expected to yield beneficial effects on learning. The results show positive effects of learner control over the selection of tasks with salient surface features for efficiency on the far transfer test but not for effectiveness. Theoretical and practical implications are discussed

Cognitive Architecture and Instructional Design: 20 Years Later

Cognitive load theory was introduced in the 1980s as an instructional design theory based on several uncontroversial aspects of human cognitive architecture. Our knowledge of many of the characteristics of working memory, long-term memory and the relations between them had been well-established for many decades prior to the introduction of the theory. Curiously, this knowledge had had a limited impact on the field of instructional design with most instructional design recommendations proceeding as though working memory and long-term memory did not exist. In contrast, cognitive load theory emphasised that all novel information first is processed by a capacity and duration limited working memory and then stored in an unlimited long-term memory for later use. Once information is stored in long-term memory, the capacity and duration limits of working memory disappear transforming our ability to function. By the late 1990s, sufficient data had been collected using the theory to warrant an extended analysis resulting in the publication of Sweller et al. (Educational Psychology Review, 10, 251-296, 1998). Extensive further theoretical and empirical work have been carried out since that time and this paper is an attempt to summarise the last 20 years of cognitive load theory and to sketch directions for future research

Checklists improve experts' diagnostic decisions

Context Checklists are commonly proposed tools to reduce error. However, when applied by experts, checklists have the potential to increase cognitive load and result in expertise reversal'. One potential solution is to use checklists in the verification stage, rather than in the initial interpretation stage of diagnostic decisions. This may avoid expertise reversal by preserving the experts' initial approach. Whether checklist use during the verification stage of diagnostic decision making improves experts' diagnostic decisions is unknown. Methods Fifteen experts interpreted 18 electrocardiograms (ECGs) in four different conditions: undirected interpretation; verification without a checklist; verification with a checklist, and interpretation combined with verification with a checklist. Outcomes included the number of errors, cognitive load, interpretation time and interpretation length. Outcomes were compared in two analyses: (i) a comparison of verification conditions with and without a checklist, and (ii) a comparison of all four conditions. Standardised scores for each outcome were used to calculate the efficiency of a checklist and to weigh its relative benefit against its relative cost in terms of cognitive load imposed, interpretation time and interpretation length. Results In both analyses, checklist use was found to reduce error (more errors were corrected in verification conditions with checklists [0.29 +/- 0.77 versus 0.03 +/- 0.61 errors per ECG], and fewer net errors occurred in all conditions with checklists [0.39 +/- 1.14 versus 1.04 +/- 1.49 errors per ECG];