Data · dataset · 2025
The effectiveness of DataMed Analytics Tutor and the repeatability dataset of AI tools
Listed in ScienceDB
DataQ1_3 Dataset: The DataQ1_3 dataset comprises three parts: Learning Effectiveness Evaluation (Q1), Learning Experience and Satisfaction Evaluation (Q2), and Open-ended Interviews (Q3).
Description
Q1 and Q2 consist of objective questions rated on a 1 to 5 Likert scale, where 1 indicates "strongly disagree" and 5 indicates "strongly agree." Q1 includes 7 items assessing students' performance and learning outcomes in data analysis tasks after using the AI-assisted tool.
Q2, with 9 items, evaluates students' learning experience and their satisfaction with the AI teaching model. Q3 contains 4 open-ended questions to gather students' subjective feedback on preferences between AI-assisted tools and traditional teaching methods, changes in interest and confidence, and the strengths and weaknesses of the AI teaching model.Reliability and Consistency Tests Dataset: The Reliability and Consistency Tests dataset evaluates the reproducibility and reliability of the AI tool.
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The table records the standard answers for different questions alongside multiple AI-generated responses. Consistency metrics were calculated based on the accuracy ratings from evaluators. The results demonstrate high accuracy and consistency across multiple responses, with scores approaching 1, confirming the tool's reliability and stability in educational scenarios.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.28889 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Computer Science & AI · Earth & Environmental Science · Engineering · Humanities · Life Sciences · Medicine & Health · Social Science
- Inferred from text
- Data management and data science 71% · Tabular 65%
Provenance · 1 source records, 13 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.28889 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:group:4605 | enrichment · scidb cn | taxonomy-embedding@1.0.0 | title+keywords+description (71%) |
| concepts[field].local:field:computer-science-ai | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[modality].local:modality:tabular | enrichment · scidb cn | keyword-concept-rules@1.0.0 | title+description (65%) |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license_text | source · scidb cn | connector:scidb_cn@1.0.0 | |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |