Publication Details
Ouroboros: Early identification of at-risk students without models based on legacy data
Student Retention, Predictive Analytics, Self-Learning, Imbalanced data, Learning
Analytics
This paper focuses on the problem of identifying students, who are at risk of
failing their courses, with the absence of the data from previous runs of the
courses, which are usually used for training the machine learning models. This
problem is typically related to newly opened courses. To tackle this issue we
present the "Ouroboros" based on the concept of "Self-Learning". It builds the
machine learning models from the data in the currently running course. Moreover
most of the students who fail the course withdraw in the first weeks of the
course, therefore the focus is on identifying at-risk students as early as
possible. The approach utilises information about already submitted assessments.
This raises another problem that needs to be treated -- the presence of
imbalanced data for training and testing the classification models.
There are three main contributions in this paper -- 1) the concept of training
the models for identifying at-risk students using the data from the same running
course, 2) specifying the problem as a classification task and 3) tackling the
problem of imbalanced data, which appears both in training and testing set.
The results prove validity of the concept and show that it stands a comparison
with traditional approaches of learning the models from legacy course data.
@inproceedings{BUT134240,
author="Martin {Hlosta} and Zdeněk {Zdráhal} and Jaroslav {Zendulka}",
title="Ouroboros: Early identification of at-risk students without models based on legacy data",
booktitle="LAK '17 Proceedings of the Seventh International Learning Analytics & Knowledge Conference",
year="2017",
pages="6--15",
publisher="Association for Computing Machinery",
address="Vancouver",
doi="10.1145/3027385.3027449",
isbn="978-1-4503-4870-6",
url="http://dl.acm.org/citation.cfm?id=3027449"
}