Adapt assistance reactively to moment-to-moment fluctuations in affect and performance¶

Strategy · All strategies
Evidence · 4 claims (4 for) · 5 studies (3 review, 1 causal, 1 theoretical), q1–q3 · 1 of 5 report an effect size · 3 claims rest on one study

Description¶

At the microscale (minutes to hours), the article recommends assessing fluctuations in task performance or affective-motivational factors 'on-line' (parallel to task progress) and reacting by giving or withholding assistance. Examples include GazeTutor, which uses eye-tracking to detect boredom and reengage learners via dialog/animation, and expert human tutors who use off-topic conversation or positive feedback to counteract significant negative affect.

Design Implications¶

Context¶

Requirements¶

• On-line assessment of affective-motivational or process variables during task processing

Constraints¶

• High amplitude in intraindividual fluctuations indicates a need for instructional adaptations; fluctuations differ between people in amplitude and frequency

Target Learners¶

• learners in digital tutoring environments and classrooms

Target Learning Goals¶

• maintaining engagement and supporting task progress despite affective fluctuations

Affordances¶

• Dynamic Framework Personalized Education

Claims¶

• Assistance and task difficulty imply an inverted U-shaped relation to learning progress [+W]
• The expertise reversal effect: instructional parameters helping novices can impede experts [+W]
• Formative assessment shows positive effects on learning, with d = 0.32 in regular classrooms [+W]
• Intelligent tutoring systems with dynamic student modeling increase learning gains over regular instruction [+W]

Related Strategies¶

• Progress Monitoring

Examples¶

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Key Sources¶

• Leonard Tetzlaff, Florian Schmiedek & Garvin Brod. (2021). Developing Personalized Education: A Dynamic Framework. Educational Psychology Review. https://doi.org/10.1007/s10648-020-09570-w