Elo-based Learner Modeling for the Adaptive Practice of Facts

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F17%3A00095908" target="_blank" >RIV/00216224:14330/17:00095908 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/s11257-016-9185-7" target="_blank" >http://dx.doi.org/10.1007/s11257-016-9185-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11257-016-9185-7" target="_blank" >10.1007/s11257-016-9185-7</a>

Result language
angličtina
Original language name
Elo-based Learner Modeling for the Adaptive Practice of Facts
Original language description
We investigate applications of learner modeling in a computerized adaptive system for practicing factual knowledge. We focus on areas where learners have widely varying prior knowledge. We propose a modular approach to the development of such adaptive practice systems: decomposing the system design into estimation of prior knowledge, estimation of current knowledge, and construction of questions. We provide a detailed discussion of learner models for both estimation steps, including a novel use of the Elo rating system for learner modeling. We implemented the proposed approach in a system for practice of geography facts; the system is widely used and allows us to perform evaluation of all three modules. We compare predictive accuracy of different learner models, discuss insights gained from learner modeling, and also impact of different variants of the system on learners engagement and learning.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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