Adaptive Practice of Facts in Domains with Varied Prior
Knowledge

D 2014

Adaptive Practice of Facts in Domains with Varied Prior Knowledge

PELÁNEK, Radek, Jan PAPOUŠEK a Vít STANISLAV

Základní údaje

Originální název

Adaptive Practice of Facts in Domains with Varied Prior Knowledge

Autoři

PELÁNEK, Radek (203 Česká republika, garant, domácí), Jan PAPOUŠEK (203 Česká republika, domácí) a Vít STANISLAV (203 Česká republika, domácí)

Vydání

London, United Kingdom, Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014), od s. 6-13, 8 s. 2014

Nakladatel

International Educational Data Mining Society

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/14:00076647

Organizační jednotka

Fakulta informatiky

ISBN

978-0-9839525-4-1

Klíčová slova anglicky

adaptive learning; student modeling; recommendation; prior knowledge

Štítky

core_B, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 4. 2015 22:21, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

We propose a modular approach to development of a computerized adaptive practice system for learning of facts in areas with widely varying prior knowledge: decomposing the system into estimation of prior knowledge, estimation of current knowledge, and selection of questions. We describe specific realization of the system for geography learning and use data from the developed system for evaluation of different student models for knowledge estimation. We argue that variants of the Elo rating systems and Performance factor analysis are suitable for this kind of educational system, as they provide good accuracy and at the same time are easy to apply in an online system.

Návaznosti

LG13010, projekt VaV

Název: Zastoupení ČR v European Research Consortium for Informatics and Mathematics (Akronym: ERCIM-CZ)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Zastoupení ČR v European Research Consortium for Informatics and Mathematics

MUNI/A/0855/2013, interní kód MU

Název: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace III. (Akronym: FI MAV III.)

Investor: Masarykova univerzita, Rozsáhlé výpočetní systémy: modely, aplikace a verifikace III., DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

PELÁNEK, Radek, Jan PAPOUŠEK a Vít STANISLAV. Adaptive Practice of Facts in Domains with Varied Prior Knowledge. Online. In John Stamper, Zachary Pardos, Manolis Mavrikis, Bruce M. McLaren. Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014). London, United Kingdom: International Educational Data Mining Society, 2014, s. 6-13. ISBN 978-0-9839525-4-1.

@inproceedings{1199904,
   author = {Pelánek, Radek and Papoušek, Jan and Stanislav, Vít},
   address = {London, United Kingdom},
   booktitle = {Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014)},
   editor = {John Stamper, Zachary Pardos, Manolis Mavrikis, Bruce M. McLaren},
   keywords = {adaptive learning; student modeling; recommendation; prior knowledge},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {London, United Kingdom},
   isbn = {978-0-9839525-4-1},
   pages = {6-13},
   publisher = {International Educational Data Mining Society},
   title = {Adaptive Practice of Facts in Domains with Varied Prior Knowledge},
   year = {2014}
}

TY  - JOUR
ID  - 1199904
AU  - Pelánek, Radek - Papoušek, Jan - Stanislav, Vít
PY  - 2014
TI  - Adaptive Practice of Facts in Domains with Varied Prior Knowledge
PB  - International Educational Data Mining Society
CY  - London, United Kingdom
SN  - 9780983952541
KW  - adaptive learning
KW  - student modeling
KW  - recommendation
KW  - prior knowledge
N2  - We propose a modular approach to development of a computerized adaptive practice system for learning of facts in areas with widely varying prior knowledge: decomposing the system into estimation of prior knowledge, estimation of current knowledge, and selection of questions. We describe specific realization of the system for geography learning and use data from the developed system for evaluation of different student models for knowledge estimation. We argue that variants of the Elo rating systems and Performance factor analysis are suitable for this kind of educational system, as they provide good accuracy and at the same time are easy to apply in an online system.
ER  -

PELÁNEK, Radek, Jan PAPOUŠEK a Vít STANISLAV. Adaptive Practice of Facts in Domains with Varied Prior Knowledge. Online. In John Stamper, Zachary Pardos, Manolis Mavrikis, Bruce M. McLaren. \textit{Proceedings of the 7th International Conference on Educational Data Mining (EDM 2014)}. London, United Kingdom: International Educational Data Mining Society, 2014, s.~6-13. ISBN~978-0-9839525-4-1.

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