The details matter: methodological nuances in the evaluation of student models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00104013" target="_blank" >RIV/00216224:14330/18:00104013 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11257-018-9204-y" target="_blank" >http://dx.doi.org/10.1007/s11257-018-9204-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11257-018-9204-y" target="_blank" >10.1007/s11257-018-9204-y</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The details matter: methodological nuances in the evaluation of student models

Popis výsledku v původním jazyce

The core of student modeling research is about capturing the complex learning processes into an abstract mathematical model. The student modeling research, however, also involves important methodological aspects. Some of these aspects may seem like technical details not worth significant attention. However, the details matter. We discuss three important methodological issues in student modeling: the impact of data collection, the splitting of data into a training set and a test set, and the details concerning averaging in the computation of predictive accuracy metrics. We explicitly identify decisions involved in these steps, illustrate how these decisions can influence results of experiments, and discuss consequences for future research in student modeling.

Název v anglickém jazyce

The details matter: methodological nuances in the evaluation of student models

Popis výsledku anglicky

The core of student modeling research is about capturing the complex learning processes into an abstract mathematical model. The student modeling research, however, also involves important methodological aspects. Some of these aspects may seem like technical details not worth significant attention. However, the details matter. We discuss three important methodological issues in student modeling: the impact of data collection, the splitting of data into a training set and a test set, and the details concerning averaging in the computation of predictive accuracy metrics. We explicitly identify decisions involved in these steps, illustrate how these decisions can influence results of experiments, and discuss consequences for future research in student modeling.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

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

1573-1391

Svazek periodika

28

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

29

Strana od-do

207-235

Kód UT WoS článku

000444730900001

EID výsledku v databázi Scopus

2-s2.0-85050248811