Impact of Methodological Choices on the Evaluation of Student
Models

D 2020

Impact of Methodological Choices on the Evaluation of Student Models

EFFENBERGER, Tomáš a Radek PELÁNEK

Základní údaje

Originální název

Impact of Methodological Choices on the Evaluation of Student Models

Autoři

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

Vydání

Cham, Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. od s. 153-164, 12 s. 2020

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/20:00116669

Organizační jednotka

Fakulta informatiky

ISBN

978-3-030-52236-0

ISSN

DOI

http://dx.doi.org/10.1007/978-3-030-52237-7_13

UT WoS

000885049000013

Klíčová slova anglicky

adaptive learning; student modeling; intelligent tutoring systems; introductory programming

Štítky

core_A, firank_A

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 16. 8. 2023 13:14, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

The evaluation of student models involves many methodological decisions, e.g., the choice of performance metric, data filtering, and cross-validation setting. Such issues may seem like technical details, and they do not get much attention in published research. Nevertheless, their impact on experiments can be significant. We report experiments with six models for predicting problem-solving times in four introductory programming exercises. Our focus is not on these models per se but rather on the methodological choices necessary for performing these experiments. The results show, particularly, the importance of the choice of performance metric, including details of its computation and presentation.

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 a Radek PELÁNEK. Impact of Methodological Choices on the Evaluation of Student Models. Online. In Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163. Cham: Springer, 2020, s. 153-164. ISBN 978-3-030-52236-0. Dostupné z: https://dx.doi.org/10.1007/978-3-030-52237-7_13.

@inproceedings{1685465,
   author = {Effenberger, Tomáš and Pelánek, Radek},
   address = {Cham},
   booktitle = {Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163.},
   doi = {http://dx.doi.org/10.1007/978-3-030-52237-7_13},
   editor = {Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E.},
   keywords = {adaptive learning; student modeling; intelligent tutoring systems; introductory programming},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-030-52236-0},
   pages = {153-164},
   publisher = {Springer},
   title = {Impact of Methodological Choices on the Evaluation of Student Models},
   url = {https://doi.org/10.1007/978-3-030-52237-7_13},
   year = {2020}
}

TY  - JOUR
ID  - 1685465
AU  - Effenberger, Tomáš - Pelánek, Radek
PY  - 2020
TI  - Impact of Methodological Choices on the Evaluation of Student Models
PB  - Springer
CY  - Cham
SN  - 9783030522360
KW  - adaptive learning
KW  - student modeling
KW  - intelligent tutoring systems
KW  - introductory programming
UR  - https://doi.org/10.1007/978-3-030-52237-7_13
L2  - https://doi.org/10.1007/978-3-030-52237-7_13
N2  - The evaluation of student models involves many methodological decisions, e.g., the choice of performance metric, data filtering, and cross-validation setting. Such issues may seem like technical details, and they do not get much attention in published research. Nevertheless, their impact on experiments can be significant. We report experiments with six models for predicting problem-solving times in four introductory programming exercises. Our focus is not on these models per se but rather on the methodological choices necessary for performing these experiments. The results show, particularly, the importance of the choice of performance metric, including details of its computation and presentation.
ER  -

EFFENBERGER, Tomáš a Radek PELÁNEK. Impact of Methodological Choices on the Evaluation of Student Models. Online. In Bittencourt I., Cukurova M., Muldner K., Luckin R., Millán E. \textit{Artificial Intelligence in Education. AIED 2020. Lecture Notes in Computer Science, vol 12163.}. Cham: Springer, 2020, s.~153-164. ISBN~978-3-030-52236-0. Dostupné z: https://dx.doi.org/10.1007/978-3-030-52237-7\_{}13.

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