2014 e learning innovations conference maina muuro keynoteaddress 31st_july_2014

18/16/2014E.Maina: eLearning Innovations Conference

& Expo 29th-31st july 2014

PRESENTER

Elizaphan Muuro MainaLecturer Kenyatta University

Department of Computing And Information Technology

PhD Student in University of Nairobi (Computer Science : Intelligent Systems)



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



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



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



References

Surjono, H. D. (2014). The Evaluation of a MoodleBased 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.



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 thelearning process. Based upon the profile, personalizedlearning resource could be generated to match theindividual preferences and levels.

• Furthermore, learners with the common interests andlevels can be grouped, and feedbacks of one person canserve as the guideline for information delivery to the othermembers within the same group.

• These systems respond to the needs of the student, puttinggreater emphasis on certain topics, repeating things thatstudents haven’t mastered, and generally helping studentsto work at their own pace, whatever that may be.



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.



A.I Tutors

• Fewer instructors

• Tutorial fellows are not there in e-learning

• Need for A.I tutors

• Intelligent tutoring systems



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



Data mining in Moodle

• LAMS- Moodle

• Weka Jar lib- Clustering Agorithms (Skmeansand EM): http://www.cs.waikato.ac.nz/ml/weka/downloading.html



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



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



Interesting research areas in A.I and e-learning

• Text analysis• Yet to be realized in e-learning platforms

