Learning Analytics Dashboard Analysing First-Year Engineering Students

Result code in 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>
Alternative codes found
RIV/68407700:21220/18:00322753 RIV/68407700:21230/18:00322753 RIV/68407700:21730/18:00322753
Result on the web
<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>

Result language
angličtina
Original language name
Learning Analytics Dashboard Analysing First-Year Engineering Students
Original language description
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.
Czech name
—
Czech description
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Type
D - Article in proceedings
CEP classification
—
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

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