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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216305:26230/17:PU123061

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20202 - Communication engineering and systems

Projekt

<a href="/cs/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the Seventh International Learning Analytics & Knowledge Conference

ISBN

978-1-4503-4870-6

ISSN

—

e-ISSN

—

Počet stran výsledku

10

Strana od-do

6-15

Název nakladatele

ACM

Místo vydání

New York

Místo konání akce

Vancouver

Datum konání akce

13. 3. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000570180700002