Beyond binary correctness: Classification of students’ answers
in learning systems

J 2020

Beyond binary correctness: Classification of students’ answers in learning systems

PELÁNEK, Radek a Tomáš EFFENBERGER

Základní údaje

Originální název

Beyond binary correctness: Classification of students’ answers in learning systems

Autoři

PELÁNEK, Radek a Tomáš EFFENBERGER

Vydání

User Modeling and User-Adapted Interaction, Springer, 2020, 0924-1868

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 4.412

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/20:00116670

Organizační jednotka

Fakulta informatiky

Klíčová slova anglicky

adaptive learning; student modeling; answer classification; response time

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 10. 9. 2021 07:55, doc. Mgr. Radek Pelánek, Ph.D.

Anotace

V originále

Adaptive learning systems collect data on student performance and use them to personalize system behavior. Most current personalization techniques focus on the correctness of answers. Although the correctness of answers is the most straightforward source of information about student state, research suggests that additional data are also useful, e.g., response times, hints usage, or specific values of incorrect answers. However, these sources of data are not easy to utilize and are often used in an ad hoc fashion. We propose to use answer classification as an interface between raw data about student performance and algorithms for adaptive behavior. Specifically, we propose a classification of student answers into six categories: three classes of correct answers and three classes of incorrect answers. The proposed classification is broadly applicable and makes the use of additional interaction data much more feasible. We support the proposal by analysis of extensive data from adaptive learning systems.

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

MUNI/A/1076/2019, interní kód MU

Název: Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 20 (Akronym: SKOMU)

Investor: Masarykova univerzita, Zapojení studentů Fakulty informatiky do mezinárodní vědecké komunity 20, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

PELÁNEK, Radek a Tomáš EFFENBERGER. Beyond binary correctness: Classification of students’ answers in learning systems. User Modeling and User-Adapted Interaction. Springer, 2020, roč. 30, č. 5, s. 867-893. ISSN 0924-1868. Dostupné z: https://doi.org/10.1007/s11257-020-09265-5.

@article{1685467,
   author = {Pelánek, Radek and Effenberger, Tomáš},
   article_number = {5},
   doi = {https://doi.org/10.1007/s11257-020-09265-5},
   keywords = {adaptive learning; student modeling; answer classification; response time},
   language = {eng},
   issn = {0924-1868},
   journal = {User Modeling and User-Adapted Interaction},
   title = {Beyond binary correctness: Classification of students’ answers in learning systems},
   url = {https://doi.org/10.1007/s11257-020-09265-5},
   volume = {30},
   year = {2020}
}

TY  - JOUR
ID  - 1685467
AU  - Pelánek, Radek - Effenberger, Tomáš
PY  - 2020
TI  - Beyond binary correctness: Classification of students’ answers in learning systems
JF  - User Modeling and User-Adapted Interaction
VL  - 30
IS  - 5
SP  - 867-893
EP  - 867-893
PB  - Springer
SN  - 09241868
KW  - adaptive learning
KW  - student modeling
KW  - answer classification
KW  - response time
UR  - https://doi.org/10.1007/s11257-020-09265-5
L2  - https://doi.org/10.1007/s11257-020-09265-5
N2  - Adaptive learning systems collect data on student performance and use them to personalize system behavior. Most current personalization techniques focus on the correctness of answers. Although the correctness of answers is the most straightforward source of information about student state, research suggests that additional data are also useful, e.g., response times, hints usage, or specific values of incorrect answers. However, these sources of data are not easy to utilize and are often used in an ad hoc fashion. We propose to use answer classification as an interface between raw data about student performance and algorithms for adaptive behavior. Specifically, we propose a classification of student answers into six categories: three classes of correct answers and three classes of incorrect answers. The proposed classification is broadly applicable and makes the use of additional interaction data much more feasible. We support the proposal by analysis of extensive data from adaptive learning systems.
ER  -

PELÁNEK, Radek a Tomáš EFFENBERGER. Beyond binary correctness: Classification of students’ answers in learning systems. \textit{User Modeling and User-Adapted Interaction}. Springer, 2020, roč.~30, č.~5, s.~867-893. ISSN~0924-1868. Dostupné z: https://doi.org/10.1007/s11257-020-09265-5.

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