Engaging students in active learning: The case for personalized multimedia messages

  • Moreno R
  • Mayer R
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The authors tested the hypothesis that personalized messages in a multimedia science lesson can promote deep learning by actively engaging students in the elaboration of the materials and reducing processing load. Students received a multimedia explanation of lightning formation (Experiments 1 and 2) or played an agent-based computer game about environmental science (Experiments 3, 4, and 5). Instructional messages were presented in either a personalized style, where students received spoken or written explanations in the lst-and 2nd-person points of view, or a neutral style, where students received spoken or written explanations in the 3rd-person point of view. Personalized rather than neutral messages produced better problem-solving transfer performance across all experiments and better retention performance on the computer game. The theoretical and educational implications of the findings are discussed. Classic research in psychology has shown that people react differently to situations that involve personal reference. This is the case of the well-known cocktail party effect, in which a person who is attending to one conversation is able to detect his or her own name in a separate conversation that is taking place simulta-neously in the same room (Cherry, 1953). Another robust phenom-enon is the self-referential effect, in which retention is facilitated by having people process information by relating it to aspects of themselves (Rogers, Kuiper, & Kirker, 1977). As with other factors in human learning and memory, the question arises as to whether personal reference effects can be extended and used in the area of multimedia learning. For exam-ple, it is possible to teach a computer-based science lesson with higher or lower levels of self-referencing by varying the commu-nication style used in the explanations (Turco, 1996). In the studies reported in the literature, high self-referencing has been created by

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  • Roxana Moreno

  • Richard E. Mayer

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