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 a Jaroslav ČECHÁK

Základní údaje

Originální název

Exploration of the Robustness and Generalizability of the Additive Factors Model

Autoři

EFFENBERGER, Tomáš (203 Česká republika, garant, domácí), Radek PELÁNEK (203 Česká republika, domácí) a Jaroslav ČECHÁK (203 Česká republika, domácí)

Vydání

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

Nakladatel

Association for Computing Machinery

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/20:00115226

Organizační jednotka

Fakulta informatiky

ISBN

978-1-4503-7712-6

DOI

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

UT WoS

000558753800059

Klíčová slova anglicky

student modeling; learning curves; knowledge components; introductory programming

Štítky

firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 9. 2021 07:56, doc. Mgr. Radek Pelánek, Ph.D.

Anotace

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.

Návaznosti

MUNI/A/1050/2019, interní kód MU

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

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace IX, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

EFFENBERGER, Tomáš, Radek PELÁNEK a 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, s. 472-479. ISBN 978-1-4503-7712-6. Dostupné z: 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 a 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, s.~472-479. ISBN~978-1-4503-7712-6. Dostupné z: https://dx.doi.org/10.1145/3375462.3375491.

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