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The expertise reversal effect: cognitive load and motivational explanations
Günter Daniel Rey1, Florian Buchwald2
1Department Psychology IV.
The expertise reversal effect shows that adding information helps novices but hinders experts. This effect is explained by cognitive load differences, not just motivation, impacting instructional design.
Area of Science:
- Cognitive Psychology
- Educational Technology
- Instructional Design
Background:
- The expertise reversal effect challenges standard instructional design principles, like the redundancy principle, by showing how learner expertise can alter their effectiveness.
- While the expertise reversal effect is well-documented, its underlying causes, particularly cognitive load versus motivational factors, remain debated.
Purpose of the Study:
- To investigate whether cognitive load or motivational differences better explain the expertise reversal effect concerning the redundancy principle.
- To test the impact of additional explanatory text on learning outcomes for novices and experts in a specific instructional context.
Main Methods:
- A 2x2 between-subjects factorial design was employed with 104 students learning about gradient descent.
- Participants were categorized as novices or experts based on prior exposure to the instructional design.
- The study manipulated the presence or absence of additional text explaining animations, measuring retention and transfer.
Main Results:
- The expertise reversal effect was replicated: novices with additional text outperformed those without, while experts showed the opposite trend.
- Learner cognitive load differences, rather than overall motivation, were found to be a significant explanatory factor for the observed effect.
- A partial overlap was identified between the motivational dimension 'probability of success' and cognitive load measures.
Conclusions:
- Cognitive load is a more direct explanation for the expertise reversal effect than general motivation.
- Instructional designers must tailor design principles, considering both learner expertise and associated cognitive load.
- Findings have implications for developing adaptive learning environments that dynamically adjust content based on learner expertise and cognitive state.
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