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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F20%3A00116670" target="_blank" >RIV/00216224:14330/20:00116670 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/s11257-020-09265-5" target="_blank" >https://doi.org/10.1007/s11257-020-09265-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11257-020-09265-5" target="_blank" >10.1007/s11257-020-09265-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

User Modeling and User-Adapted Interaction

ISSN

0924-1868

e-ISSN

—

Svazek periodika

30

Číslo periodika v rámci svazku

5

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

27

Strana od-do

867-893

Kód UT WoS článku

000530578600001

EID výsledku v databázi Scopus

2-s2.0-85085069556