NDLI: Designing adaptive learning itineraries using features modelling and swarm intelligence

Content Provider	Springer Nature Link
Author	Marquez Vazquez, Jose Manuel Ortega Ramirez, Juan Antonio Gonzalez Abril, Luis Velasco Morente, Francisco
Copyright Year	2011
Abstract	In this paper, Bayesian network (BN) and ant colony optimization (ACO) techniques are combined in order to find the best path through a graph representing all available itineraries to acquire a professional competence. The combination of these methods allows us to design a dynamic learning path, useful in a rapidly changing world. One of the most important advances in this work, apart from the variable amount of pheromones, is the automatic processing of the learning graph. This processing is carried out by the learning management system and helps towards understanding the learning process as a competence-oriented itinerary instead of a stand-alone course. The amount of pheromones is calculated by taking into account the results acquired in the last completed course in relation to the minimum score required and by feeding this into the learning tree in order to obtain a relative impact on the path taken by the student. A BN is used to predict the probability of success, by taking historical data and student profiles into account. Usually, these profiles are defined beforehand; however, in our approach, some characteristics of these profiles, such as the level of knowledge, are classified automatically through supervised and/or unsupervised learning. By using ACO and BN, a fitness function, responsible for automatically selecting the next course in the learning graph, is defined. This is done by generating a path which maximizes the probability of each user’s success on the course. Therefore, the path can change in order to adapt itself to learners’ preferences and needs, by taking into account the pedagogical weight of each learning unit and the social behaviour of the system.
Starting Page	623
Ending Page	639
Page Count	17
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	20
Issue Number	5
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-02-03
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Ant colony optimization Bayesian networks Features modelling OSGi E-learning Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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