Validity and Reliability of Student Models for Problem-Solving Activities
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00121402" target="_blank" >RIV/00216224:14330/21:00121402 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/doi/10.1145/3448139.3448140" target="_blank" >https://dl.acm.org/doi/10.1145/3448139.3448140</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3448139.3448140" target="_blank" >10.1145/3448139.3448140</a>
Alternative languages
Result language
angličtina
Original language name
Validity and Reliability of Student Models for Problem-Solving Activities
Original language description
Student models are typically evaluated through predicting the correctness of the next answer. This approach is insufficient in the problem-solving context, especially for student models that use performance data beyond binary correctness. We propose more comprehensive methods for validating student models and illustrate them in the context of introductory programming. We demonstrate the insufficiency of the next answer correctness prediction task, as it is neither able to reveal low validity of student models that use just binary correctness, nor does it show increased validity of models that use other performance data. The key message is that the prevalent usage of the next answer correctness for validating student models and binary correctness as the only input to the models is not always warranted and limits the progress in learning analytics.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 11th International Conference on Learning Analytics and Knowledge
ISBN
9781450389358
ISSN
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e-ISSN
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Number of pages
11
Pages from-to
1-11
Publisher name
Association for Computing Machinery
Place of publication
New York, NY, USA
Event location
Irvine CA USA
Event date
Jan 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000883342500001