Research on composite instructional designs should connect studies via a common theoretical model and may exploit machine-learning-based process data¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The editorial recommends that future research connect multiple studies through a common theoretical model, so that preparation-effect assumptions can be tested coherently and advance theory building. It also expects that developments in methodology, "especially in data collection and evaluation utilizing machine learning strategies", can give a more detailed picture of learning processes in complex instructional designs.

Design Implications¶

Context¶

Requirements¶

• A common theoretical model shared across studies, and an appropriate balance between ecological validity and controlled implementation when using machine-learning methods

Constraints¶

• Current special-issue studies constitute independent efforts rather than a coherent test of preparation-effect assumptions

Target Learners¶

• researchers and designers of multi-phase instruction

Target Learning Goals¶

• systematic theory building on preparation effects in composite instructional designs

Affordances¶

• Cid Framework Composite Instructional Designs

Related Strategies¶

• Prioritize process-data instrumentation, open collaboration, and dissemination in learning platforms

Examples¶

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Key Sources¶

• Katharina Loibl, Valentina Nachtigall, Timo Leuders. (2025). Advances in research on composite instructional designs – investigating intermediate knowledge and preparation effects. Instructional Science. https://doi.org/10.1007/s11251-025-09751-6