CYAIHCMay 8, 2025

How Instructional Sequence and Personalized Support Impact Diagnostic Strategy Learning

arXiv:2507.17760v11 citationsh-index: 4AIED
Originality Incremental advance
AI Analysis

This addresses the challenge of enhancing diagnostic strategy learning for pharmacy technician apprentices, but it is incremental as it builds on existing scenario-based learning methods.

The study tackled the problem of optimizing instructional sequences in scenario-based learning to improve diagnostic reasoning, finding that providing instruction after problem-solving (PS-I) leads to significantly higher performance in transfer tasks compared to before problem-solving (I-PS).

Supporting students in developing effective diagnostic reasoning is a key challenge in various educational domains. Novices often struggle with cognitive biases such as premature closure and over-reliance on heuristics. Scenario-based learning (SBL) can address these challenges by offering realistic case experiences and iterative practice, but the optimal sequencing of instruction and problem-solving activities remains unclear. This study examines how personalized support can be incorporated into different instructional sequences and whether providing explicit diagnostic strategy instruction before (I-PS) or after problem-solving (PS-I) improves learning and its transfer. We employ a between-groups design in an online SBL environment called PharmaSim, which simulates real-world client interactions for pharmacy technician apprentices. Results indicate that while both instruction types are beneficial, PS-I leads to significantly higher performance in transfer tasks.

Foundations

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