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Am I failing this course?
Risk prediction using e-learning
dataCelia González Nespereira
Ana Fernández VilasRebeca P. Díaz Redondo
Information & Computing Laboratory AtlanTIC Research Center
University Of Vigo
ObjectivesStudy the relationship between the students’
activity in the e-learning platforms and their final marks.
Obtain some indicators to predict the students’ behaviour and results.
Create an algorithm to detect the students that are in risk of fail the course.
IndexDatasetObtaining the indicators.
CorrelationTime series
Risk detection algorithmConclusions
DatasetData of an e-learning platform based on Moodle
of the University of Vigo.The study is centred in one blended subject of
the second course of the Telecommunication Engineering Degree.
We use the data of two consecutive academic years: 12/13 as training data y 13/14 as test data.Year Pass
studentsFail students
Withdrawals
Total
2012/2013 92 29 31 1522013/2014 43 57 71 171
CorrelationCorrelation between number of events and final
mark:
Values between 0.02 and 0.4 There are relation, but not very clear
Time seriesStudents with highest grades
Students with lowest grades
Time SeriesTrend component:
Conclusion: Use the trend component as predictor.
Risk detection algorithmTwo academic years:
Academic year 12/13 as training course.Academic year13/14 as test course.
Three control points:
Risk detection algorithmAlgorithm:
Calculate the trend component of the academic year 12/13 until the control point.
Fist control point
Second control point
Third control point
Risk detection algorithmAlgorithm:
For each student of academic year 13/14:Calculate the trend of this student.Obtain which is the most similar trend between those
obtained in the academic year 12/13.Classify the student in the group that corresponds.
Testing:Compare if the students that the algorithm detect
as in risk, finally fail the course.
ResultsGlobal
By groups
Conclusions The number of interactions with the e-learning
platform is related to the students’ success.The trend component of the temporal series analysis
can be used as a detector of students in risk of failing the subject.
We use this trend component to create a risk detection algorithm Detecting more than 84% in 1st CP and more than 93%
in the 3th CP
Future workExtend our study to other courses. Improve the algorithm to detect the grade thresholds
and the control points automatically, to minimize the error.
Develop a Moodle plugin to trigger alarms when the students are in risk of fail the subject.
Use Deep Learning techniques to: Improve the prediction algorithm Create a system that could learn and improve by itself
with new data.
Questions?