Student Drop-out Modelling Using Virtual Learning Environment Behaviour Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11410%2F18%3A10380975" target="_blank" >RIV/00216208:11410/18:10380975 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/18:00322655

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-319-98572-5_13" target="_blank" >https://doi.org/10.1007/978-3-319-98572-5_13</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-98572-5_13" target="_blank" >10.1007/978-3-319-98572-5_13</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Student Drop-out Modelling Using Virtual Learning Environment Behaviour Data

Popis výsledku v původním jazyce

With the rapid advancement of Virtual Learning Environments (VLE) in higher education, the amount of available student data grows. Universities collect the information about students, their demographics, their study results and their behaviour in the online environment. By applying modelling and predictive analysis methods it is possible to predict student outcome or detect bottlenecks in course design. Our work aims at statistical simulation of student behaviour in the VLE in order to identify behavioural patterns leading to drop-out or passive withdrawal i.e. the state when a student is not studying, but he has not actively withdrawn from studies. For that purpose, the method called Markov chain modelling has been used. Recorded student activities in VLE (VLE logs) has been used for constructing of probabilistic representation that students will perform some activity in the next week based on their activities in the current week. The result is an instance of the family of absorbing Markov chains, which can be analysed using the property called time to absorption. The preliminary results show that interesting patterns in student VLE behaviour can be uncovered, especially when combined with the information about submission of the first assessment. Our analysis has been performed using Open University Learning Analytics dataset (OULAD) and research notes are available online (https://bit.ly/2JrY5zv). (C) 2018, Springer Nature Switzerland AG.

Název v anglickém jazyce

Student Drop-out Modelling Using Virtual Learning Environment Behaviour Data

Popis výsledku anglicky

With the rapid advancement of Virtual Learning Environments (VLE) in higher education, the amount of available student data grows. Universities collect the information about students, their demographics, their study results and their behaviour in the online environment. By applying modelling and predictive analysis methods it is possible to predict student outcome or detect bottlenecks in course design. Our work aims at statistical simulation of student behaviour in the VLE in order to identify behavioural patterns leading to drop-out or passive withdrawal i.e. the state when a student is not studying, but he has not actively withdrawn from studies. For that purpose, the method called Markov chain modelling has been used. Recorded student activities in VLE (VLE logs) has been used for constructing of probabilistic representation that students will perform some activity in the next week based on their activities in the current week. The result is an instance of the family of absorbing Markov chains, which can be analysed using the property called time to absorption. The preliminary results show that interesting patterns in student VLE behaviour can be uncovered, especially when combined with the information about submission of the first assessment. Our analysis has been performed using Open University Learning Analytics dataset (OULAD) and research notes are available online (https://bit.ly/2JrY5zv). (C) 2018, Springer Nature Switzerland AG.

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

<a href="/cs/project/GJ18-04150Y" target="_blank" >GJ18-04150Y: Prediktivní modelování studentova výkonu s využitím výukových zdrojů</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

978-3-319-98571-8

ISSN

0302-9743

e-ISSN

neuvedeno

Počet stran výsledku

6

Strana od-do

166-171

Název nakladatele

Springer Verlag

Místo vydání

Švýcarsko

Místo konání akce

Leeds, United Kingdom

Datum konání akce

3. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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