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@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 and 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, p.~6-13. ISBN~978-0-9839525-4-1.
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