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UNDERSTANDING LEARNER’S
DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti

Massive Open Online Course (Premium members)
Send feedback to learners
to prevent drop-out
Provide personalized
feedback to teachers
Contributions
A predictive system that can detect at-risk droppers at different instants of the
learner’s interaction with the course
New features associated with learners’ trajectory of engagement with the
course.
Suitable intervention strategies for both teachers and learners.
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Context
2
“ The structure of “a course” may not be helpful
to all participants and supporting different patterns of
engagement may be beneficial [Onah et. al, 2014]
Deficiency in some skills
(digital skills or prerequisite)
Bad MOOC design
(inefficient material, high workload)
Lack of intention to complete
(material hunter, curious explorers)
Inaccurate expectations
(misunderstanding about the objectives or
content)
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Problems
3
“Create your Website with HTML” (HTML5) 2015-2016
11,520 active learners
Completers: 38%
(4,333)
Droppers: 62%
(7,187)
“Understanding the Web” (Web) 2015-2016
7,160 active learners
Completers: 71%
(5,085)
Droppers: 29%
(2,075)
Features
Completed activities
Time spent on each activity
Grades obtained
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
CASE STUDY
Openclassroom Dataset
4
Creation of behavioral features
3 new attributes are created for each pair
of activities
Back jumps
(a step back from the scenario planned by
the teacher)
Forward jumps
(intended transition between 2 activities or
skipped activities)
Inactivity time
(for each jump, inactivity time between
activities)
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
CASE STUDY
Features creation
5
CRISP-DM Process Data Preparation
New behavior features
Modeling
Stratified training and test set
Algorithms : random forest, gradient
boosting, decision tree, logistic
regression
For each algorithm:
Hyperparameter tuning
K-fold cross-validation
Performance metrics : F-measure,
AUC
Evaluation
Provide personalized feedback to
learners and teachers
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
CASE STUDY
Predictive process
6
Experiments goals
Evaluate algorithms performance
considering the 3 following contexts
Impact of the prediction at 25% or 50% of
the course progression
  Impact of Behavorial features
Impact of the course
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
CASE STUDY
Experiments
7
Percentage of completed activities 25% 50%
Behavorial features with without with without
Create your site with HTML5 RF
0.76 ± .02 0.85 ± .01
GB
DT
LR
Understanding the web RF
0.25 ± .01
0.91 ± .01
GB
DT
LR 0.46
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
RESULTS
Overview (F-measure metric)
8
Logistic Regression (6 most influential features in absolute value)
Decision Tree (pruned)
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
RESULTS
Models readability
9
The most discriminating feature is a descriptive indicator
(completed activity, grades, time spent)
  
Diagnostic:
Is the activity motivating? [Viau, 2000]
Does the activity respect constructive alignment?
The activity is a lesson or an exercise:
Identify difficulties (unannounced prerequisites, ...)
Check the target audience of the course
The activity is an assessment :
Identify difficulties (complex statements, ...)
Check the activity (workload, kind of assessment, ...)
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
RESULTS
Situation 1 Doing an activity increases the probability of drop-out
10
The most discriminating feature is a behavioral indicator
(back jump, forward jumps, inactivity time)
  
The feature is a forward-jump that respects the recommended progression
Check activity to identify a wrong constructive alignment
Suggest new routes to students and change course progression
The feature is a back-jump or a forward jump skipping the recommended
progression
Remind students of the importance of following recommended progression
Add self-assessment activities to help to identify relevant jumps (back and
forward)
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
RESULTS
Situation 2 Doing an jump increases the probability of drop-out
11
At a given course progression the prediction is bad and so no feature
is really relevant
None of the activity over the period allow to anticipate successes and failures :
Activities could be designed to build student self-confidence
... but we could check a wrong constructive alignment!
Prediction performance increases at a latter instant
The most predictive activities are often those that require some commitment
(exercices, assemnents):
No such activities during the first 25% of course progression
The course lack activities to help student to assess their progress
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
RESULTS
Situation 3 Prediction performance increases a lot between two different instants
12
Contributions
A predictive system that can detect at-risk droppers at different instants of the
learner’s interaction with the course
New features associated with learners’ trajectory of engagement with the
course
Suitable intervention strategies for both teachers and learners
Perspectives
Use business knowledge: learner model, course structure, learning outcomes
(e-education)
Focus on social interactions and group dynamics in the learning mechanism
(social network analysis)
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
CONCLUSION & PERSPECTIVES 13
[Onah et al., 2014] Onah, D. F. O., Sinclair, J., & Boyatt, R. (2014). Dropout rates of
massive open online courses behavioural patterns. 6th International Conference on
Education and New Learning Technologies, 5825–5834.
https://doi.org/10.13140/RG.2.1.2402.0009
[Viau, 2000] Viau, R. (2000). Conditions to be respected to encourage student
motivation. La revue web sur la valorisation du français en milieu collégial. Retrieved
from https://goo.gl/Cva6KH
UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS
Alya Itani, Laurent Brisson, Serge Garlatti
The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018
REFERENCES 14

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Understanding Learner's Drop-out in MOOCs

  • 1. UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti
  • 2.  Massive Open Online Course (Premium members) Send feedback to learners to prevent drop-out Provide personalized feedback to teachers Contributions A predictive system that can detect at-risk droppers at different instants of the learner’s interaction with the course New features associated with learners’ trajectory of engagement with the course. Suitable intervention strategies for both teachers and learners. UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Context 2
  • 3. “ The structure of “a course” may not be helpful to all participants and supporting different patterns of engagement may be beneficial [Onah et. al, 2014] Deficiency in some skills (digital skills or prerequisite) Bad MOOC design (inefficient material, high workload) Lack of intention to complete (material hunter, curious explorers) Inaccurate expectations (misunderstanding about the objectives or content) UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Problems 3
  • 4. “Create your Website with HTML” (HTML5) 2015-2016 11,520 active learners Completers: 38% (4,333) Droppers: 62% (7,187) “Understanding the Web” (Web) 2015-2016 7,160 active learners Completers: 71% (5,085) Droppers: 29% (2,075) Features Completed activities Time spent on each activity Grades obtained UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 CASE STUDY Openclassroom Dataset 4
  • 5. Creation of behavioral features 3 new attributes are created for each pair of activities Back jumps (a step back from the scenario planned by the teacher) Forward jumps (intended transition between 2 activities or skipped activities) Inactivity time (for each jump, inactivity time between activities) UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 CASE STUDY Features creation 5
  • 6. CRISP-DM Process Data Preparation New behavior features Modeling Stratified training and test set Algorithms : random forest, gradient boosting, decision tree, logistic regression For each algorithm: Hyperparameter tuning K-fold cross-validation Performance metrics : F-measure, AUC Evaluation Provide personalized feedback to learners and teachers UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 CASE STUDY Predictive process 6
  • 7. Experiments goals Evaluate algorithms performance considering the 3 following contexts Impact of the prediction at 25% or 50% of the course progression   Impact of Behavorial features Impact of the course UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 CASE STUDY Experiments 7
  • 8. Percentage of completed activities 25% 50% Behavorial features with without with without Create your site with HTML5 RF 0.76 ± .02 0.85 ± .01 GB DT LR Understanding the web RF 0.25 ± .01 0.91 ± .01 GB DT LR 0.46 UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 RESULTS Overview (F-measure metric) 8
  • 9. Logistic Regression (6 most influential features in absolute value) Decision Tree (pruned) UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 RESULTS Models readability 9
  • 10. The most discriminating feature is a descriptive indicator (completed activity, grades, time spent)    Diagnostic: Is the activity motivating? [Viau, 2000] Does the activity respect constructive alignment? The activity is a lesson or an exercise: Identify difficulties (unannounced prerequisites, ...) Check the target audience of the course The activity is an assessment : Identify difficulties (complex statements, ...) Check the activity (workload, kind of assessment, ...) UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 RESULTS Situation 1 Doing an activity increases the probability of drop-out 10
  • 11. The most discriminating feature is a behavioral indicator (back jump, forward jumps, inactivity time)    The feature is a forward-jump that respects the recommended progression Check activity to identify a wrong constructive alignment Suggest new routes to students and change course progression The feature is a back-jump or a forward jump skipping the recommended progression Remind students of the importance of following recommended progression Add self-assessment activities to help to identify relevant jumps (back and forward) UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 RESULTS Situation 2 Doing an jump increases the probability of drop-out 11
  • 12. At a given course progression the prediction is bad and so no feature is really relevant None of the activity over the period allow to anticipate successes and failures : Activities could be designed to build student self-confidence ... but we could check a wrong constructive alignment! Prediction performance increases at a latter instant The most predictive activities are often those that require some commitment (exercices, assemnents): No such activities during the first 25% of course progression The course lack activities to help student to assess their progress UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 RESULTS Situation 3 Prediction performance increases a lot between two different instants 12
  • 13. Contributions A predictive system that can detect at-risk droppers at different instants of the learner’s interaction with the course New features associated with learners’ trajectory of engagement with the course Suitable intervention strategies for both teachers and learners Perspectives Use business knowledge: learner model, course structure, learning outcomes (e-education) Focus on social interactions and group dynamics in the learning mechanism (social network analysis) UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 CONCLUSION & PERSPECTIVES 13
  • 14. [Onah et al., 2014] Onah, D. F. O., Sinclair, J., & Boyatt, R. (2014). Dropout rates of massive open online courses behavioural patterns. 6th International Conference on Education and New Learning Technologies, 5825–5834. https://doi.org/10.13140/RG.2.1.2402.0009 [Viau, 2000] Viau, R. (2000). Conditions to be respected to encourage student motivation. La revue web sur la valorisation du français en milieu collégial. Retrieved from https://goo.gl/Cva6KH UNDERSTANDING LEARNER’S DROP-OUT IN MOOCS Alya Itani, Laurent Brisson, Serge Garlatti The 19th International Conference on Intelligent Data Engineering and Automated Learning - 21-23 November 2018 REFERENCES 14