Impact of Methodological Choices on the Evaluation of Student
Models

D 2020

Impact of Methodological Choices on the Evaluation of Student Models

EFFENBERGER, Tomáš and Radek PELÁNEK

Basic information

Original name

Impact of Methodological Choices on the Evaluation of Student Models

Authors

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

Edition

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

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/20:00116669

Organization unit

Faculty of Informatics

ISBN

978-3-030-52236-0

ISSN

DOI

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

UT WoS

000885049000013

Keywords in English

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

Abstract

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.

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 and 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, p. 153-164. ISBN 978-3-030-52236-0. Available from: 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 and 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, p.~153-164. ISBN~978-3-030-52236-0. Available from: https://dx.doi.org/10.1007/978-3-030-52237-7\_{}13.

Detailed Information on Publication Record