Use learner skill levels to determine when to transition from worked examples to problem solving in tutoring systems¶

Strategy · All strategies
Evidence · 3 claims (3 for) · 6 studies (3 review, 2 causal, 1 theoretical), q1–q3 · 0 of 6 report an effect size · 2 claims rest on one study

Description¶

The article reports that current skill levels, in terms of self-explanation performance while studying examples and problem solving performance, can be employed to determine appropriate learning conditions, informing adaptation schedules in intelligent tutoring systems. A fading approach adaptive to a learner's current skill level was expected to be more effective than a predetermined fading approach for structuring the transition from studying examples to problem solving.

Design Implications¶

Context¶

Requirements¶

• Continuous assessment of learners' current skill levels (self-explanation and problem-solving performance) to trigger fading

Constraints¶

• Classroom replication held on the delayed posttest but not on the immediate posttest

Target Learners¶

• high school students learning geometry in intelligent tutoring systems

Target Learning Goals¶

• effective transition from example study to independent problem solving

Claims¶

• Adaptive fading of worked examples based on current skill level produced better learning outcomes than fixed fading or tutored problem solving [+W]
• The relative effectiveness of instructional methods reverses as levels of learner knowledge in a domain change (the expertise reversal effect) [+W]
• Successively fading out worked solution steps is effective for facilitating the transition from learning from worked examples to problem solving, compared to example-problem pairs [+W]

Related Strategies¶

• Worked Example Fading
• Design AI educational systems that fade support and require learner justification to preserve independent problem solving
• Faded Guidance

Examples¶

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Key Sources¶

• Slava Kalyuga, Alexander Renkl. (2010). Expertise reversal effect and its instructional implications: introduction to the special issue. Instructional Science. https://doi.org/10.1007/s11251-009-9102-0