Hierarchical Diagnostic Reasoning (HDR)¶
Product or Programme · All products and programmes
Evidence · 3 claims (3 for) · 1 study (1 design),q3· 0 of 1 report an effect size · 3 claims rest on one study
Description¶
A structured case-based assessment proposed by Takahashi et al. that embeds intentional knowledge misuses and requires learners to identify and explain the errors to diagnose applied cognitive skills.
Components¶
Claims¶
- ChatGPT-generated HDR problems measure the same ability as human-created problems, with high internal consistency (Cronbach's α = 0.78) and no significant difficulty difference [+M]
- HDR scores correlate only weakly with reading comprehension (α = 0.36 and 0.41), indicating the task measures practical application ability rather than reading skill [+M]
- Automated HDR problem generation makes repeated and longitudinal assessment feasible by producing structurally equivalent cases with different contexts [+W]
Related Products and Programmes¶
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Key Sources¶
- Satoshi Takahashi, Atsushi Yoshikawa, Megumi Kose, Kenichi Suzuki, Chieko Inoue, Yumi Watanabe, and Mari Sawada. (2026). Automating Constructive Assessment with Large Language Models: Toward Scalable and Repeated Evaluation of Practical Competence. arXiv. https://arxiv.org/abs/2609.25790
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
hierarchical-diagnostic-reasoning-hdr |
title |
Hierarchical Diagnostic Reasoning (HDR) |
description |
A structured case-based assessment proposed by Takahashi et al. |
status |
review |
product_kind |
assessment |
generated |
by process:settle-candidates on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
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