A Bi-level Individualized Adaptive Learning Recommendation System Based on Topic Modeling
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3ALEP47T6W" target="_blank" >RIV/00216208:11320/22:LEP47T6W - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-031-04572-1_10" target="_blank" >https://doi.org/10.1007/978-3-031-04572-1_10</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-04572-1_10" target="_blank" >10.1007/978-3-031-04572-1_10</a>
Alternative languages
Result language
angličtina
Original language name
A Bi-level Individualized Adaptive Learning Recommendation System Based on Topic Modeling
Original language description
Adaptive learning offers real attention to individual students’ differences and fits different needs from students. This study proposes a bi-level recommendation system with topic models, gradient descent, and a content-based filtering algorithm. In the first level, the learning materials were analyzed by a topic model, and topic proportions to each short item in each learning material were yielded as representation features. The second level contains a measurement component and a recommendation strategy component which employ gradient descent and content-based filtering algorithm to analyze personal profile vectors and make an individualized recommendation. An empirical data consists of cumulative assessments that were used as a demonstration of the recommendation process. Results have suggested that the distribution to the estimated values in the person profile vectors were related to the ability estimation from the Rasch model, and students with similar profile vectors could be recommended with the same learning material.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Quantitative Psychology
ISBN
978-3-031-04572-1
ISSN
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e-ISSN
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Number of pages
20
Pages from-to
121-140
Publisher name
Springer International Publishing
Place of publication
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Event location
Cham
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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