Towards Design-Loop Adaptivity: Identifying Items for Revision
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F22%3A00128708" target="_blank" >RIV/00216224:14330/22:00128708 - isvavai.cz</a>
Result on the web
<a href="https://jedm.educationaldatamining.org/index.php/JEDM/article/view/600" target="_blank" >https://jedm.educationaldatamining.org/index.php/JEDM/article/view/600</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5281/zenodo.7357331" target="_blank" >10.5281/zenodo.7357331</a>
Alternative languages
Result language
angličtina
Original language name
Towards Design-Loop Adaptivity: Identifying Items for Revision
Original language description
We study the automatic identification of educational items worthy of content authors’ attention. Based on the results of such analysis, content authors can revise and improve the content of learning environments. We provide an overview of item properties relevant to this task, including difficulty and complexity measures, item discrimination, and various forms of content representation. We analyze the potential usefulness of these properties using both simulation and analysis of real data from a large-scale learning environment. We also describe two case studies where we practically apply the identification of attention-worthy items. Based on the analysis and case studies, we provide recommendations for practice and impulses for further research.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Educational Data Mining
ISSN
2157-2100
e-ISSN
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Volume of the periodical
14
Issue of the periodical within the volume
3
Country of publishing house
US - UNITED STATES
Number of pages
25
Pages from-to
1-25
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85145995438