Dataset of 434 expert-labelled authentic learner questions augmented to balance class distributions¶
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Evidence · no claims cited
Description¶
A dataset created by Lee et al. containing 434 expert-labelled and class-balanced authentic questions from higher-education information technology students for training and evaluating models that classify inquiry depth.
Components¶
Claims¶
Related Products and Programmes¶
- Asyncform (AF) dataset
- Balanced 192-text evaluation dataset of EFL, professional, AI-generated, and hybrid texts
Key Sources¶
- Lee, H., Atif, A., & Kang, K. (2026). Analysing AI utilisation in education through learner question types: A constructivist approach. Australasian Journal of Educational Technology, 42(1), 79–96. https://doi.org/10.14742/ajet.10657
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
dataset-of-434-expert-labelled-authentic-learner-questions-augmented-to-balance-class-dist |
title |
Dataset of 434 expert-labelled authentic learner questions augmented to balance class distributions |
description |
A dataset created by Lee et al. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
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