Exploration of the Robustness and Generalizability of the Additive Factors Model
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F20%3A00115226" target="_blank" >RIV/00216224:14330/20:00115226 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1145/3375462.3375491" target="_blank" >https://doi.org/10.1145/3375462.3375491</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3375462.3375491" target="_blank" >10.1145/3375462.3375491</a>
Alternative languages
Result language
angličtina
Original language name
Exploration of the Robustness and Generalizability of the Additive Factors Model
Original language description
Additive Factors Model is a widely used student model, which is primarily used for refining knowledge component models (Q-matrices). We explore the robustness and generalizability of the model. We explicitly formulate simplifying assumptions that the model makes and we discuss methods for visualizing learning curves based on the model. We also report on an application of the model to data from a learning system for introductory programming; these experiments illustrate possibly misleading interpretation of model results due to differences in item difficulty. Overall, our results show that greater care has to be taken in the application of the model and in the interpretation of results obtained with the model.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
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 10th International Conference on Learning Analytics and Knowledge
ISBN
9781450377126
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
472-479
Publisher name
Association for Computing Machinery
Place of publication
New York, NY, USA
Event location
Frankfurt, Germany
Event date
Jan 1, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000558753800059