Annotated reflective-writing sentence dataset of 5080 sentences from 76 student essays¶
Product or Programme · All products and programmes
Evidence · 4 claims (4 for) · 1 study (1 design),q3· 0 of 1 report an effect size · 4 claims rest on one study
Description¶
A corpus of 5,080 sentence-level annotations from higher-education reflective writings, created by Thomas Daniel Ullmann to support automated assessment of reflective writing.
Components¶
Claims¶
- Machine learning detects five of eight reflective-writing categories with substantial or almost perfect reliability and three with moderate reliability (Cohen's κ .53–.85) [+W]
- The eight-category model of reflective writing can be reliably annotated and showed empirical validity in this context [+W]
- Automated analysis accuracies were on average 10% lower than the accuracies of manual analysis [+W]
- Manual content analysis of reflective writing is time-consuming, constraining pedagogical use and large-scale research [+W]
Related Products and Programmes¶
Key Sources¶
- Thomas Daniel Ullmann. (2019). Automated Analysis of Reflection in Writing: Validating Machine Learning Approaches. International Journal of Artificial Intelligence in Education (2019) 29:217–257. https://doi.org/10.1007/s40593-019-00174-2
Page metadata
| Field | Value |
|---|---|
type |
product |
id |
annotated-reflective-writing-sentence-dataset-of-5080-sentences-from-76-student-essays |
title |
Annotated reflective-writing sentence dataset of 5080 sentences from 76 student essays |
description |
A corpus of 5,080 sentence-level annotations from higher-education reflective writings, created by Thomas Daniel Ullmann to support automated assessment of reflective writing. |
status |
review |
product_kind |
dataset |
generated |
by process:settle-candidates on 2026-10-27 |
trust |
unverified — no one has confirmed this page's content |
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