Exploration of the Robustness and Generalizability of the Additive Factors Model

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Exploration of the Robustness and Generalizability of the Additive Factors Model

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Exploration of the Robustness and Generalizability of the Additive Factors Model

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 10th International Conference on Learning Analytics and Knowledge

ISBN

9781450377126

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

472-479

Název nakladatele

Association for Computing Machinery

Místo vydání

New York, NY, USA

Místo konání akce

Frankfurt, Germany

Datum konání akce

1. 1. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000558753800059