Ouroboros: early identification of at-risk students without models based on legacy data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F17%3A00309665" target="_blank" >RIV/68407700:21730/17:00309665 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26230/17:PU123061
Result on the web
<a href="http://dl.acm.org/citation.cfm?id=3027449" target="_blank" >http://dl.acm.org/citation.cfm?id=3027449</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3027385.3027449" target="_blank" >10.1145/3027385.3027449</a>

Result language
angličtina
Original language name
Ouroboros: early identification of at-risk students without models based on legacy data
Original language description
This paper focuses on the problem of identifying students, who are at risk of failing their course. The presented method proposes a solution in the absence of data from previous courses, which are usually used for training machine learning models. This situation typically occurs in new courses. We present the concept of a "self-learner" that builds the machine learning models from the data generated during the current course. The approach utilises information about already submitted assessments, which introduces the problem of imbalanced data for training and testing the classification models. There are three main contributions of this paper: (1) the concept of training the models for identifying at-risk students using data from the current course, (2) specifying the problem as a classification task, and (3) tackling the challenge of imbalanced data, which appears both in training and testing data. The results show the comparison with the traditional approach of learning the models from the legacy course data, validating the proposed concept.
Czech name
—
Czech description
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Project
<a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the Seventh International Learning Analytics & Knowledge Conference
ISBN
978-1-4503-4870-6
ISSN
—
e-ISSN
—
Number of pages
10
Pages from-to
6-15
Publisher name
ACM
Place of publication
New York
Event location
Vancouver
Event date
Mar 13, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000570180700002

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