Consider students' perceived control over fine-grained task facets (technology, pedagogy, content) when designing ITS learning experiences¶

Strategy · All strategies
Evidence · 1 claim (1 for) · 1 study (1 associational), q3 · 1 of 1 report an effect size · 1 claim rests on one study

Description¶

Based on the finding that topic-related self-efficacy showed no relation to task emotions in the ITS, the article recommends that designers attend to momentary, task-level control rather than only general competence beliefs. The authors state this "calls for more fine-grained considerations of students’ perceived control over the various facets of the task", which "may include technology-related features (i.e., navigational properties, amount of accessible resources), pedagogy-related features (degree of structuration of the learning support, clarity of learning goals), and content-related features (i.e., causal maps, clarity of the knowledge content)". This strategy applies to designing open-ended ITS learning experiences.

Design Implications¶

Context¶

Requirements¶

• The ITS task facets whose control learners perceive must be identifiable, including navigational properties, learning support structure, and knowledge content clarity

Constraints¶

• The article notes ITS ill-defined goals are known to place high demands on cognition and self-regulation, so perceived topic-level control may show weaker relations with emotions than task demands and coping ability

Target Learners¶

• Secondary school students using intelligent tutoring systems

Target Learning Goals¶

• Supporting positive emotional experience (enjoyment, low boredom) during autonomous digital science learning

Affordances¶

• Cvt Sequence In Its Context

Claims¶

• Topic-related interest, but not importance, utility, or self-efficacy, predicted task-related enjoyment while secondary students learned with an ITS [+W]

Related Strategies¶

• Reduce cheating holistically by enhancing perceived assessment purpose, fostering self-efficacy, and increasing perceived costs of cheating

Examples¶

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Key Sources¶

• Chevalère, J., Yun, H. S., Henke, A., Pinkwart, N., Hafner, V., & Lazarides, R. (2023). A sequence of learning processes in an intelligent tutoring system from topic-related appraisals to learning gains. Learning and Instruction, 87, 101799. https://doi.org/10.1016/j.learninstruc.2023.101799