Learning Analytics Dashboard Analysing First-Year Engineering Students

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11410%2F18%3A10380973" target="_blank" >RIV/00216208:11410/18:10380973 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21220/18:00322753 RIV/68407700:21230/18:00322753 RIV/68407700:21730/18:00322753

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-319-98572-5_48" target="_blank" >https://doi.org/10.1007/978-3-319-98572-5_48</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-98572-5_48" target="_blank" >10.1007/978-3-319-98572-5_48</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Learning Analytics Dashboard Analysing First-Year Engineering Students

Popis výsledku v původním jazyce

Nowadays, the higher education institutions experience the problem of the student drop-out. In response to this problem, universities started employing analytical dashboards and educational data mining methods such as machine learning, to detect students at risk of failing their studies. In this paper, we present interactive web-based Learning Analytics dashboard - Analyst, which has been successfully deployed at Faculty of Mechanical Engineering (FME), Czech Technical University in Prague. The dashboard provides academic teaching staff with the opportunity to analyse student-related data from various sources in multiple ways to identify those, who might have difficulties to complete their degree. For this purpose, multiple analytical dashboard views have been implemented. It includes summary statistic, study progression graph, and credit completion probabilities graph. In addition, users have the option to export all analysis related graphs for the future use. Based on the outcomes provided by the Analyst, the university successfully ran the interventions on the selected at-risk students and significantly increased the retention rate in the first study year. (C) 2018, Springer Nature Switzerland AG.

Název v anglickém jazyce

Learning Analytics Dashboard Analysing First-Year Engineering Students

Popis výsledku anglicky

Nowadays, the higher education institutions experience the problem of the student drop-out. In response to this problem, universities started employing analytical dashboards and educational data mining methods such as machine learning, to detect students at risk of failing their studies. In this paper, we present interactive web-based Learning Analytics dashboard - Analyst, which has been successfully deployed at Faculty of Mechanical Engineering (FME), Czech Technical University in Prague. The dashboard provides academic teaching staff with the opportunity to analyse student-related data from various sources in multiple ways to identify those, who might have difficulties to complete their degree. For this purpose, multiple analytical dashboard views have been implemented. It includes summary statistic, study progression graph, and credit completion probabilities graph. In addition, users have the option to export all analysis related graphs for the future use. Based on the outcomes provided by the Analyst, the university successfully ran the interventions on the selected at-risk students and significantly increased the retention rate in the first study year. (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

4

Strana od-do

575-578

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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