ant colony optimization in collaborative learning environment using netlogo by, a.safia #1, t.mala*...
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Ant Colony Optimization in Collaborative Learning Environment Using NetLogo
By,By, A.SAFIA A.SAFIA #1,#1, T.MALA* T.MALA*22
##1 1 Teaching Fellow, Department of Information Technology,Teaching Fellow, Department of Information Technology,MIT Campus, Anna University, Chennai, India.MIT Campus, Anna University, Chennai, India.
**22 Assistant Professor (Sr.G), Department of Information Science Assistant Professor (Sr.G), Department of Information Science and Technology,and Technology,
CEG Campus, Anna University, Chennai, India.CEG Campus, Anna University, Chennai, India.11 [email protected]
Abstract
A Collaborative Learning Environment emanates from multiple individuals’ consent to share knowledge and experiences for the collective goodwill of all. It proves effective as the scope for a deep sense of understanding increases multi-fold in a collaborative environment. This however, can be optimised by identifying the Point Of Interest (POI) of each member and thus, grouping individuals with similar POI. Considering multiple samples from across multiple regions would prove effective in forming highly efficient groups. With the Swarm Intelligence of Ant Colony Optimization, the various experiences and learning of each member of a group can prove helpful for other members. And with the cumulative analysis of everyone’s learning, the group as a whole can be benefitted. This paper shows how Ant Colony Optimization can be effectively used in a Collaborative Learning Environment for improving the knowledge level of individual implemented and this is proved using NetLogo simulation.
Agenda
• Existing Work
• Proposed Work
• Implementation
• Performance Evaluation
• Conclusions and Future work
Collaborative Learning
Collaborative Learning is a method of instruction in which multiple students are grouped together to achieve a common academic goal. The students may come from different locations and backgrounds. The very purpose of Collaborative Learning is that different students master different aspects of learning or knowledge-content and thus, when grouped, each individual may provide his/her knowledge and learning abilities for the best use of the rest of the members. This way, each member gets to utilise all the resources available in the group thereby, enriching his/her knowledge multi-fold, which wouldn’t have been possible individually.
Features of Collaborative Learning Environment
• Heterogeneous Grouping
• Peers Interaction
• Individual Accountability
• Positive Interdependence
• Cooperative Skills
Ant Colony Optimization
• Swarm Intelligence techniques.
• Solving computational problems using probabilistic approach
• Ants -search of food.
• Found- bring food almost
• other ants - source of food.
• Way - all ants find -source of food.
NetLogo
• NetLogo is a multi-agent programming language
• Simulating natural and social phenomenon.
• Developing large and complex systems.
• NetLogo is Logo dialect -support agents.
Existing Work
• Collaborative Learning in E-Learning - (ELMS) [10].
• M2Learn [7]
• Scalable Framework for Large-Scale Distributed Collaboration (CSCW) [8]• Web-based Framework for On-line Collaborative Learning and browsing
[3] [5].• Social Learning (Omega Network) - User-based nearest neighbour
algorithms [6]
Proposed Work
• Ant Colony Optimization algorithm - leaner’s interest and maturity level.
• Point Of Interest (POI), synchronisation with his/her vested interests.
• Resembles a colony of ants which is in search of food
• Path - Efficient learning Attract s
• Dynamic mechanism - solutions to learning.
Algorithmprocedure ACO_in_CLE
initialise intensity matrix and feasibility matrix
while (sufficient knowledge of subject not gained)
do
Let path be initialised to current path
while ( other paths remain to be considered )
do
apply Probabilistic decision rule to an available path
if (switching to available path is more desirable )
then
path is equal to available path
else
path remains unmodified
done
end
end
Implementation
• Micro-level & Macro-level patterns - Interaction [1][2]
• Ant Colony Optimization - Natural phenomenon – Many agents Simulated using NetLogo.
• Level of maturity.
• Following is a code sample that depicts the members of a group as ants (turtles of NetLogo),
to setup-food ;; patch procedure
;; setup one food source on the right
if ((distance (0.7 * max-pxcor) 0) < 5)
[ set food-source-number 1 ]
end
Performance Evaluation
• IL – Individual Learning
• CL – Collaborative Learning
• CL using ACO – Collaborative Learning using Ant Colony Optimization
• CKG – Content Knowledge Gained
• CA – Content Analysed
• HOT – Higher Order Thinking involved
• Thus, it is very clear that CL and CL using ACO score more than IL on all the parameters.
• Higher Order Thinking.
• Correlation of knowledge gained and application of it is more prominent in case of Collaborative Learning using Ant Colony Optimization.
CONCLUSIONS & FUTURE WORK
We have represented the group members as the ants in the Ant Colony Optimization model, wherein each member’s pursuit of knowledge is equated to the search of each ant for food; thus, throwing light on the fact that as a collective unit, the work of each individual may be put to the best use of all. To implement the same, we have chosen the Ant Colony Optimization (or any other user-defined equivalent) model from the model libraries of NetLogo. We have simulated the Collaborative Learning environment through an Ant Colony and shown how members optimise their learning. We know that the intensity of possibility to follow a given path of acquiring knowledge may reduce if other members don’t follow it. However, the rate of degradation may also vary depending on the means of learning under consideration and the composition of the group. Thus, if the rate of degradation is taken care of dynamically, the system would then be adaptable to a wider range of real-world scenarios. This remains our area of research for future.
REFERENCES
• [1] Baloche, L. (1998). “The cooperative classroom: Empowering learning”. Upper Saddle River, NJ: Prentice Hall.
• [2] Chon-Kit Leong, Chien-Tsai Liu, “A Framework for Collaborate SCORM-Compliant Web-Based Authoring System”. 2007 IEEE.
• [3] Guillermo de Jesus Hoyos-Rivera, Roberta Lima-Gomes, Jean-Pierre Courtiat, “A Flexible Architecture for Collaborative Browsing”. Proceedings of the Eleventh IEEE International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE’02).
• [4] Jacobs, G.M., & Hannah, D. (2004). “Combining cooperative learning with reading aloud by teachers”. International Journal of English Studies, 4, 97-118.
• [5] Lidan Shou, Bin Cui, Gang Chen, Jinxiang Dong, “A Web-based Framework For On-line Collaborative Learning”. Proceedings of the 10th International Conference on Computer Supported Cooperative Work in Design
• [6] Matteo Dominoni, Stefano Pinardi, Giorgio Riva, “Omega Network: an Adaptive Approach to Social Learning”. 2010 10th International Conference on Intelligent Systems Design and Applications.
• [7] Sergio Martin, Ivica Boticki, George Jacobs, Manuel Castro and Juan Peire, “Work in Progress – Support for Mobile Collaborative Learning Applications”. 40th ASEE/IEEE Frontiers in Education Conference.
• [8] Shengwen Yang, Jinlei Jiang, Meilin Shi, “A Scalable Framework for Large-Scale Distributed Collaboration”. Proceedings of the 10th International Conference on Computer Supported Cooperative Work in Design.
• [9] Zar Chi Su Su Hlaing, May Aye Khine, “An Ant Colony Optimization Algorithm for Solving Traveling Salesman Problem”. 2011 International Conference on Information Communication and Management, IPCSIT vol. 16 (2011).
• [10] Zhi Liu, Hai Jin, Zhaolin Fang, “Collaborative Learning in E-Learning based on Multi-Agent Systems”. Proceedings of the 10th International Conference on Computer Supported Cooperative Work in Design