2014 e learning innovations conference

1. 18/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

2. PRESENTER
Elizaphan Muuro Maina
Lecturer Kenyatta University
Department of Computing And Information
Technology
PhD Student in University of Nairobi
(Computer Science : Intelligent Systems)
28/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

3. Integration of Artificial Intelligence
Systems in e-learning
• Why
• Era of Cognitive Computing
– E-learning platforms
• Adaptive learning
• Personalized learning
• A.I Tutors
• Example
– Collaborative learning in Moodle
• Intelligent grouping algorithm
38/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

4. Era of Cognitive Computing
• New era in IT:
– Computing systems that will understand the world in the way that
humans do: through senses, learning, and experience.
– E.g. IBM Watson
• System generate a lot of data.
• What can we do with it?
– Data mining:
• Statistical analysis
• Classification
• Clustering
• Prediction
• Visualization
• Need to do a paradigms shift to intelligent e-learning platforms
48/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

5. Adaptive e-learning systems (AeLS)
• The aim of adaptive e-Learning is to provide the students the
appropriate content at the right time, means that the system is
able to determine the knowledge level, keep track of usage, and
arrange content automatically for each student for the best
learning result.
• Two types:
• Adaptivity: System which initiates, system adjust its
presentation according to the student characteristics
automatically,
• Adaptability: Student who initiates, capability of the
system to support user adjustment
• Example
• Based on student Learning Styles
58/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

6. References
Surjono, H. D. (2014). The Evaluation of a Moodle
Based Adaptive e-Learning System. International
Journal of Information & Education Technology,
4(1).
Esichaikul, V., Lamnoi, S., & Bechter, C. (2011).
Student Modelling in Adaptive E-Learning
Systems. Knowledge Management & E-Learning:
An International Journal (KM&EL), 3(3), 342-355.
68/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

7. Personalized e-learning
• Automatically adapt to the interests and levels of learners.
– E.g. User profiling where user profile including interests,
levels and learning patterns can be assessed during the
learning process. Based upon the profile, personalized
learning resource could be generated to match the
individual preferences and levels.
• Furthermore, learners with the common interests and
levels can be grouped, and feedbacks of one person can
serve as the guideline for information delivery to the other
members within the same group.
• These systems respond to the needs of the student, putting
greater emphasis on certain topics, repeating things that
students haven’t mastered, and generally helping students
to work at their own pace, whatever that may be.
78/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

8. References
• Lu, F., Li, X., Liu, Q., Yang, Z., Tan, G., & He, T. (2007). Research on
personalized e-learning system using fuzzy set based clustering
algorithm. In Computational Science–ICCS 2007 (pp. 587-590).
Springer Berlin Heidelberg.
• Gong, M. (2008, January). Personalized E-learning System by Using
Intelligent Algorithm. In Knowledge Discovery and Data Mining,
2008. WKDD 2008. First International Workshop on (pp. 400-401).
IEEE.
• Li, X., & Chang, S. K. (2005, September). A Personalized E-Learning
System Based on User Profile Constructed Using Information
Fusion. In DMS (pp. 109-114).
• Castro, F., Vellido, A., Nebot, À., & Mugica, F. (2007). Applying data
mining techniques to e-learning problems. In Evolution of teaching
and learning paradigms in intelligent environment (pp. 183-221).
Springer Berlin Heidelberg.
88/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

9. A.I Tutors
• Fewer instructors
• Tutorial fellows are not there in e-learning
• Need for A.I tutors
• Intelligent tutoring systems
98/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

10. Example: Collaborative learning in
Moodle
• Social constructivist pedagogy (Vygotsky ,1978)
• The learning systems in particularly have shifted from
normal paradigms to more social constructionist
• Moodle e-learning platform:
– Forums: Students can participate in group discussion
– Wikis: Students can create wiki page and come up with a
group product or edit content as a group
– Chats: Chats rooms for student to meet and exchange
ideas.
– Workshops: Students can engage in a peer assessment
activity
108/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

11. Data mining in Moodle
• LAMS- Moodle
• Weka Jar lib- Clustering Agorithms (Skmeans
and EM):
http://www.cs.waikato.ac.nz/ml/weka/downl
oading.html
118/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

12. Implementation
• Moodle 2.4 was installed on the Windows Server
• Weka Jar lib was added to the Windows Server
• Weka Jar lib was invoked from the Moodle PHP code
• Preprocessing the Moodle forum Data
• The summary table is stored as text file with .cvs extension and it
has the following columns:
• User id(taken from mdl_role_assignments by checking the role and enroll
conditions)
• Number of posts (taken from mdl_forum_posts)
• Number of replies(taken from mdl_forum_posts)
• Forum ratings(taken from mdl_rating)
• This summary table is fed as an input to the Weka.php program
which has the clustering algorithms.
• Applying clustering algorithms to create collaboration competence
levels
128/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

13. Example
• Applying clustering algorithms to create
collaboration competence levels
• 3 Clusters:
• Cluster O
• Cluster 1
• Cluster 2
• Create heterogeneous groups
• Ranked Array
• IGCC algorithm picks students from different collaborative
levels as per the rank and assigns them to one group
• Group data into Moodle tables
138/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014

14. Interesting research areas in A.I and
e-learning
• Text analysis
• Yet to be realized in e-learning platforms
148/16/2014
E.Maina: eLearning Innovations Conference
& Expo 29th-31st july 2014