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Observational Learning from Video-based Expert Models in Multimedia Learning Environments

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Title Observational Learning from Video-based Expert Models in Multimedia Learning Environments
Period 09 / 2003 - 09 / 2007
Status Completed
Dissertation Yes
Research number OND1297878
Data Supplier Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO)

Abstract

Modern instructional theories tend to focus on realistic tasks as the driving force for learning. In such theories, the observation of expert models is typically seen as an important instructional tool. Often, video-based expert models are used to show an expert who is constructing a solution for a realistic problem and explaining how he is doing it and why he is doing it in this particular way. Such expert models combine three information sources (i.e., a solution; steps for reaching this solution, and heuristics for selecting useful steps) that must be mentally integrated by the learner and may thus easily yield a high cognitive load that interferes with meaningful learning. The main research question of the proposed project is how to control cognitive load and optimise learning from video-based expert models in multimedia learning environments. It studies modality effects in relation to learner control over the pacing of video segments that correspond with solution steps (Experiment 1); in relation to physical integration of explanations of procedural information ( how-to ?) and heuristic information ( why ?; Experiment 2), and in relation to sequencing the presentation of pictorial information and textual information over time (Experiment 3). The project will yield practical guidelines for the design of video-based expert models in multimedia learning environments.

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Classification

A84200 Education
A84400 Cognitive development, perception
D16500 User interfaces, multimedia
D16600 Artificial intelligence, expert systems
D51000 Psychology
D52000 Educational theory

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