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?