Exploration of the Robustness and Generalizability of the
Additive Factors Model

D 2020

Exploration of the Robustness and Generalizability of the Additive Factors Model

EFFENBERGER, Tomáš, Radek PELÁNEK and Jaroslav ČECHÁK

Basic information

Original name

Exploration of the Robustness and Generalizability of the Additive Factors Model

Authors

EFFENBERGER, Tomáš (203 Czech Republic, guarantor, belonging to the institution), Radek PELÁNEK (203 Czech Republic, belonging to the institution) and Jaroslav ČECHÁK (203 Czech Republic, belonging to the institution)

Edition

New York, NY, USA, Proceedings of the 10th International Conference on Learning Analytics and Knowledge, p. 472-479, 8 pp. 2020

Publisher

Association for Computing Machinery

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United States of America

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/20:00115226

Organization unit

Faculty of Informatics

ISBN

978-1-4503-7712-6

DOI

http://dx.doi.org/10.1145/3375462.3375491

UT WoS

000558753800059

Keywords in English

student modeling; learning curves; knowledge components; introductory programming

Abstract

V originále

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.

Links

MUNI/A/1050/2019, interní kód MU

Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IX (Acronym: SV-FI MAV IX)

Investor: Masaryk University, Category A

Citovat

EFFENBERGER, Tomáš, Radek PELÁNEK and Jaroslav ČECHÁK. Exploration of the Robustness and Generalizability of the Additive Factors Model. Online. In Proceedings of the 10th International Conference on Learning Analytics and Knowledge. New York, NY, USA: Association for Computing Machinery, 2020, p. 472-479. ISBN 978-1-4503-7712-6. Available from: https://dx.doi.org/10.1145/3375462.3375491.

@inproceedings{1607941,
   author = {Effenberger, Tomáš and Pelánek, Radek and Čechák, Jaroslav},
   address = {New York, NY, USA},
   booktitle = {Proceedings of the 10th International Conference on Learning Analytics and Knowledge},
   doi = {http://dx.doi.org/10.1145/3375462.3375491},
   keywords = {student modeling; learning curves; knowledge components; introductory programming},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {New York, NY, USA},
   isbn = {978-1-4503-7712-6},
   pages = {472-479},
   publisher = {Association for Computing Machinery},
   title = {Exploration of the Robustness and Generalizability of the Additive Factors Model},
   url = {https://doi.org/10.1145/3375462.3375491},
   year = {2020}
}

TY  - JOUR
ID  - 1607941
AU  - Effenberger, Tomáš - Pelánek, Radek - Čechák, Jaroslav
PY  - 2020
TI  - Exploration of the Robustness and Generalizability of the Additive Factors Model
PB  - Association for Computing Machinery
CY  - New York, NY, USA
SN  - 9781450377126
KW  - student modeling
KW  - learning curves
KW  - knowledge components
KW  - introductory programming
UR  - https://doi.org/10.1145/3375462.3375491
L2  - https://doi.org/10.1145/3375462.3375491
N2  - 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.
ER  -

EFFENBERGER, Tomáš, Radek PELÁNEK and Jaroslav ČECHÁK. Exploration of the Robustness and Generalizability of the Additive Factors Model. Online. In \textit{Proceedings of the 10th International Conference on Learning Analytics and Knowledge}. New York, NY, USA: Association for Computing Machinery, 2020, p.~472-479. ISBN~978-1-4503-7712-6. Available from: https://dx.doi.org/10.1145/3375462.3375491.

Detailed Information on Publication Record