Two tasks of learning analytics: identifying university students at risk of failing and deriving study trajectories leading to success

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Two tasks of learning analytics: identifying university students at risk of failing and deriving study trajectories leading to success

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Two tasks of learning analytics: identifying university students at risk of failing and deriving study trajectories leading to success

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2022 IEEE International Conference on Systems, Man and Cybernetics (SMC)

ISBN

978-1-6654-5258-8

ISSN

1062-922X

e-ISSN

2577-1655

Počet stran výsledku

5

Strana od-do

288-292

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Prague

Datum konání akce

9. 10. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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