Risk prediction using e-learning data
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This document summarizes research on developing an algorithm to predict students' risk of failing a course based on their activity in an e-learning platform. The researchers analyzed data from a blended course at a university, including number of interactions and final marks over two years. They found a correlation between interactions and marks, and that the trend component of time series analysis could predict performance. The algorithm calculates trends for students in a training year and classifies students in a test year based on similarities to trends of passing/failing students. It detected over 84% of at-risk students in the first checkpoint and over 93% in the third checkpoint.
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Risk prediction using e-learning data
- 1. Am I failing this course? Risk prediction using e-learning data Celia González Nespereira Ana Fernández Vilas Rebeca P. Díaz Redondo Information & Computing Laboratory AtlanTIC Research Center University Of Vigo
- 2. Objectives Study 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.
- 3. Index Dataset Obtaining the indicators. Correlation Time series Risk detection algorithm Conclusions
- 4. Dataset Data 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 students Fail students Withdrawals Total 2012/2013 92 29 31 152 2013/2014 43 57 71 171
- 5. Correlation Correlation between number of events and final mark: Values between 0.02 and 0.4 There are relation, but not very clear
- 6. Time series Students with highest grades Students with lowest grades
- 7. Time Series Trend component: Conclusion: Use the trend component as predictor.
- 8. Risk detection algorithm Two academic years: Academic year 12/13 as training course. Academic year13/14 as test course. Three control points:
- 9. Risk detection algorithm Algorithm: Calculate the trend component of the academic year 12/13 until the control point. Fist control point Second control point Third control point
- 10. Risk detection algorithm Algorithm: 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.
- 11. Results Global By groups
- 12. 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
- 13. Future work Extend 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.
- 14. Questions?
Editor's Notes
- -In this paper we studied the relation between the student’s interaction with the e-learning platforms and their final marks in order to obtain some indicators that allow us to predict the students’ behaviour and results. -Moreover we developed an algorithm to detect the students that are in risk of failing the course, in order to be able to warn them and their professors and allow them to change the situation before it was too late.
- The organization of this presentation is the following: -Firstly we will explain the dataset that we use in this experiment -Secondly we explain the experiments that we did to obtain the risk detection indicators. -Then we will present our algorithm to detect the students that are in risk of fail the course. -And finally we will present our conclusions and future lines.
- -In our experiment we use the data coming from an e-learning platform based on moodle of the Univesity of Vigo. We use the data of two consecutive academic years of one blended subject of the second course of the Telecommunication Engineering degree. The first academic year (12/13) was used as training data and the second academic year (13/14) as test data.
- - The firs step was calculated the correlation between the student’s interactions on the platform and their final marks. As Moodle stores in its database different type of events depending on the actions that students perform So, We calculate the correlation between the number of occurrences of each event and the final mark. -As we can see the correlation is always positive. This means that there is a relation between number of interactions (of any type) and final mark. However, the values are between 0.02 and 0.4, so the relation is not very clear.
- -To know more about this relation, we’ve calculated the time series of each students based on their daily number of interactions. -After that, we decompose these time series in their components: Trend, seasonal noise and auto regresive component. -Here we can see the decompose of the time series of the students with highest grades (left) and lowest grades (right). -we can see that the trend component of students with the highest grades is higher than the students with the lowest grades
- -Here, we can see the two trend components plotted in the same graph. So, as a conclusion, we can use the trend component as a predictor of the students risk of failing
- Now we are going to explain the risk detection algorithm We use two academic years. The first (12/13) was used to train our system. The second (13/14) is used as test course We should fix three control points when we run our algorithm and warn students and professor if is necessary. We’ve decided to fix these three points. The first point is set after the first lab and practical quiz, when we have enough reliable information about the students behaviour to give a first prediction. The second point is set in the 12th week when we have enough information to give a good prediction before it becomes too late. The third point, in the 14th week is like a last warn for students in risk.
- The first part of the algorithm consist on calculate the trend component of the students of the academic year 12/13 until the control point. We divide the students into four groups in base of their final marks: students which finally withdraw the course, students with grades between 0 and 3, 3 and 6 and 6 and 10. We calculate the trend component of the average number of access of the students in each group.
- -The second part of the algorithm consists on calculate the trend of each student of the test year until the control point. -After that we search between the trends obtained in the training course what is the most similar to the objective trend. -If the trend is similar to the students that last year gave up the course, we classify this student as in risk of withdraw, if the trend is similar to the students that last year obtained between 0 and 3 as final grade we classify him as high risk, , if the trend is similar to the students that last year obtained between 3 and 6 as final grade we classify him as low risk. Finally if the trend is similar to the students that the last year obtained more than 6 he is classified as not in risk. -We should comment that a student pass the course with a 5 as final grade. However, we decided to warn also students with trends between 5 and 6 because it’s better to warn to a student that will pass the course than not warn to other that will fail the course. -To test our algorithm we check if the students that the algorithm detect as in risk, finally fail the course.
- -The results of our experiment were very good. The 84% of the students that were detected as in risk of failing the course in the 1st control point, finally fail the course. This value rise until the 94% of the last CP. -The results dividing in groups are not so accurate. However, we obtain good results detecting the students that give up the course, with an 80% of success from the second control point. Furthermore only the 1.40% of the students that finally gave up the subject are not detected as in risk after the 2nd control point
- As a conclusions we can say that there is a relation between number of interactions with the e-learning platform and students’ success. We can use the trend component of the temporal series analysis to detect the students in risk of failing the subject Based on this component, we’ve created a risk detection algorithm, that is able to detect successfully more than 84% of the students in risk of failing the course in the first CP, and more than 93% in the 3th CP
- In the future we will extend our study to other courses in order to see if different course characteristics lead us to the same results. Until now we have set the grade thresholds and control points manually. In the future we will set this points automatically, trying to minimize the error. As a practical application we will develop a moodle plugin to put in practice our algorithm, warning students and professor when the students are in risk of failing. Finally, we will apply deep learning techniques to improve the prediction algorithm and to create a system that could learn and improve by itself with new data