This document discusses using machine learning to predict students' skill levels based on an online collaborative learning course. It describes the course, which focused on remote group work and using ICT tools. Student data was collected through questionnaires and used to train a random forest model in Microsoft Azure Machine Learning. The model aimed to classify students as having acquired or not acquired teamwork skills based on their course experiences and evaluations. Evaluation results showed the model could accurately predict students' skill levels with over 80% accuracy, demonstrating the potential of using machine learning to help improve students' skills.

1. Predicting the student skills’ level based
on an online collaborative learning
T. Daouas - B. Rojas Medina - M.J. García Meseguer

2. Introduction
• Collaborative Learning:
Knowledge joint construction between peers / teacher
• Predictive modeling for teaching and learning:
Student success / Academic achievement
• Computational techniques:
Involved to predict student performance
• How can we improve the performance of students' skills?
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3. Online Collaborative Course
• Course name “Tools of Collaborative Work” (TCW)
• List of works reflecting activities of a team in a company
• Using tools of collaborative work at a distance
• 2 learning objectives: Remote group work and Use of maximum of ICT tools
• Online base of ICT tools in disposal
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Team Work is the
evaluated Skill

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Sequential Steps
• Each team had a project work:
A theme of ICT domain to be developed
• Same Scenario for all teams:
Using ICT
tools

5. Machine Learning Problem
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• ML one of areas of AI: Enable computer to learn knowledge and then
apply it to perform tasks (human reasoning)
• Supervised learning in Classification Problem: Existing data to train
the model. Classifying students to 2 classes : Acquired /Not Acquired Skills
• Random Forests Algorithm:
– Only depend on small parameters’ number easy to calibrate
– Known for accuracy and ability to process data sets composed
of few observations and many variables

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Microsoft Azure Machine Learning
• Design, test and evaluate the model
• Simple and powerful creation environment
• no coding is required
• Creation of an Experiment followings steps

7. Tom Mitchell, 1997
1. Define problem
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• Not all students after the course acquire required skills
• Q: How can we improve student skills’ level ?
• We need to know student level
“A computer program is said to learn from
experience E with respect to some class of
tasks T and performance measure P, if its
performance at tasks in T, as measured by P,
improves with experience E.”
 T: Predicting if student acquire team work skills
 P: Accuracy of the prediction
 E: Student evaluation at end of the course

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2. Recover necessary Data
Students gathered not belonging to teams
a) Fill in a questionnaire reflecting the team work skill
b) Choose one classmate knowing him the most who will fill for
him the same questionnaire. (2 evaluations per student)
c) Discussions between both students to adjust evaluations
d) Student fill in an on line form with definitive evaluation

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3. Prepare the Data
20-22
Age
87-32
Degree
License-Master
81-38
Gender
F-M
Student
number
119
First experience in team work
First experience in distance learning 1
Student answers on questionnaire:
“Never” –
“Sometimes” –
“Almost Always” –
“Always” –

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4. Explore the Data
• Data codification in numbers between 0 and 1
• MAML system use information to compare with prediction
• CSV (Comma-Separeted Values) Format
“Never” –
“Sometimes” –
“Almost Always” –
“Always” –
0,05
0,3
0,75
1
AVG >= 0,6
AS
NAS
Yes
No

11. Create Predictive Model
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Group Team Skill Prediction Model

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• Scored Probability (SP): probability that a student belongs to the
positive Class (NAS)
• Scored label: Predicted Class
• If SP >= 0.5, predicted as Class NAS. Otherwise, as Class AS
Scoring (Prediction) Results

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• Indicates how successful the scoring of a dataset has been
Evaluation Results
Correctly classified
student to AS
Incorrectly classified
student to NAS
Incorrectly classified
student to AS
Correctly classified
student to NAS
Proportion of correctly
Classified students
True positive
rate of classifier
Proportion of positive
Classified correctly
Harmonic mean
of Precision and Recall
Area under the curve

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• Thanks to MAML:
– Playing with parameters (threshold and others)
– Calibrate them
– See immediate results
• Predicting two other skill acquisition:
– ICT use
– Interculturality
Research to persue
SKILLS

15. Acknowledgements
• Thanks to my Colleagues and friends
• Tunisia For All Association
– Non-governmental organization
– Ongoing moral and financial support
– URL : tunisiaforall.org
• ICERI Organizers
Bélen Rojas Medina Maria José Garcia Meseguer

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Predicting the student skills’ level based
on an online collaborative learning