Student Drop-out Modelling Using Virtual Learning Environment Behaviour Data

Result code in 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>

Alternative codes found

RIV/68407700:21730/18:00322655

Result on the web

<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>

Result language

angličtina

Original language name

Student Drop-out Modelling Using Virtual Learning Environment Behaviour Data

Original language description

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leading to drop-out or passive withdrawal i.e. the state when a student is not studying, but he has not actively withdrawn from studies.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.

Czech name

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Czech description

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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)

Project

<a href="/en/project/GJ18-04150Y" target="_blank" >GJ18-04150Y: Predictive modeling of student performance using learning resources</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

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

Article name in the collection

Lecture Notes in Computer Science

ISBN

978-3-319-98571-8

ISSN

0302-9743

e-ISSN

neuvedeno

Number of pages

6

Pages from-to

166-171

Publisher name

Springer Verlag

Place of publication

Švýcarsko

Event location

Leeds, United Kingdom

Event date

Sep 3, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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