Two tasks of learning analytics: identifying university students at risk of failing and deriving study trajectories leading to success
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00364242" target="_blank" >RIV/68407700:21730/22:00364242 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/SMC53654.2022.9945325" target="_blank" >https://doi.org/10.1109/SMC53654.2022.9945325</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SMC53654.2022.9945325" target="_blank" >10.1109/SMC53654.2022.9945325</a>
Alternative languages
Result language
angličtina
Original language name
Two tasks of learning analytics: identifying university students at risk of failing and deriving study trajectories leading to success
Original language description
Many first-year university students do not complete the study plan and drop out. By investigating how students earn ECTS credits we create a model that makes it possible to predict students who are at risk of failure and drop out of the university. Weekly analysis of student data allows us to identify patterns important for prediction. Early predictions inform students about the potential danger of failure and also allow tutors to intervene. On the other hand, from the data of successful students, it is possible to derive study trajectories leading to the successful completion of the academic year and offer these trajectories to students. The described techniques for student support are demonstrated by examples.
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
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
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
2022 IEEE International Conference on Systems, Man and Cybernetics (SMC)
ISBN
978-1-6654-5258-8
ISSN
1062-922X
e-ISSN
2577-1655
Number of pages
5
Pages from-to
288-292
Publisher name
IEEE
Place of publication
Piscataway
Event location
Prague
Event date
Oct 9, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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