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Improving Generative Cognitive Processing through Germane Cognitive Load: A Qualitative Research

Document Type : Research

Authors

1 Ph.D., Student, Educational Psychology, Kharazmi University, Tehran, Iran

2 Professor, Educational Psychology, Kharazmi University, Tehran, Iran

3 Associate Professor, Psychology, Kharazmi University, Tehran, Iran

4 Assistant Professor, Educational Psychology, Kharazmi University, Tehran, Iran

Abstract

One of the main challenges in learning environments is learners' mastery of educational content and the application of new knowledge in real life. Generative learning involves actively making sense of to-be-learned information by mentally reorganizing and integrating it with one’s prior knowledge, thereby enabling learners to apply what they have learned to new situations. Due to the novelty of the concept of generative processing, there is a need for additional research on methods to enhance this type of learning.Therefore, the present study tried to identify and explain the strategies for strengthening generative processing (GL) through germane cognitive load (GCL). The research method was qualitative, conducted with thematic analysis method. The study area was all written and digital Persian and English materials on GCL. Considering the theoretical saturation in the last ten years, thirty two papers were selected and analyzed as the sample, according to the professors using content analysis and purposive approach in the field of GL and cognitive load. The results were categorized as basic themes (codes and key points of the text), organizational themes (themes obtained from the composition and summarizing the basic themes) and inclusive themes (excellent themes containing the principles governing the text as a whole) and the network of themes was planned. After data analysis, five main themes including multimedia application, personalization, feedback, thought and guided learning, and 53 sub-themes emerged that strengthen optimal generative processing and cognitive load.

Keywords

References
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Research in School and Virtual Learning
Volume 8, Issue 2 - Serial Number 30
December 2020
Pages 33-48
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