An Analytical Model for the Approximation of a Learner’s Performance Prior to Conducting a Learning Experience

Maycock, Keith and Keating, John (2006) An Analytical Model for the Approximation of a Learner’s Performance Prior to Conducting a Learning Experience. In: 13th International Conference on Learning, 22nd-25th June 2006, Montego Bay, Jamaica.

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Abstract

To cater for the expected influx into higher education, learning has to enter into an ondemand framework. It should be possible for users of different Learning Management Systems (LMS) to access instructional material that has been adapted to suit the individual cognitive needs of each user. This paper is concerned with the use of a simulation engine being introduced to our proposed LMS, which will enable a LMS the ability to approximate the learner’s performance prior to conducting a learning experience. The paper details the use of a Genetic Algorithm (GA) that has been adapted to determine the Largest Common Sub-graph (LCS) between two graphs. The graphs are generated once a learning experience has concluded, from the activity tree of a Sharable Content Object Reference Model (SCORM) learning object with corresponding metadata. The GA is used to approximate the isomorphic relevance between two graphs paying particular attention to the structure of the graph. The results show the significant reduction in the computational time required for identifying the relevant isomorphism between the graphs.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Caoimhe Ní Mhaicín
Date Deposited:	24 Jul 2014 13:27
Last Modified:	24 Jul 2014 13:27
URI:	https://norma.ncirl.ie/id/eprint/1485

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