DocumentCode :
3703075
Title :
An automation of the course design based on mathematical modeling and genetic algorithms
Author :
Alexey Dukhanov;Maria Karpova;Vadim Shmelev
Author_Institution :
Department of High Performance Computing, ITMO University, St. Petersburg, Russian Federation
fYear :
2015
Firstpage :
1
Lastpage :
4
Abstract :
This work in progress describes a technique of course design in the form of learning objects (LO) sequences. We defined the distance between LOs (metric of learning objects space based on revised Blooms Taxonomy) and used it to design an objective function. This function is a sum of distances between LOs in the given sequence learning prerequisites of a course and its learning outcomes. In addition, the function contains penalties for the violations of the learning sequence, when, for example, one or more learning prerequisites for the given LO are absent in learning outcomes of previous LOs and course´s prerequisites. A genetic algorithm was used to find LOs sequences complied with the given requirements of the course. To provide the series of experiments, this algorithm was realized as a computer-aided application in the development environment Microsoft Visual Studio 12. These series showed that the suggested technique is suitable for automated course design based on big amount LOs. Several suggestions to develop the technique are given at the end of this paper.
Keywords :
"Gravity","Genetic algorithms","Calibration","Linear programming","Taxonomy","Sociology"
Publisher :
ieee
Conference_Titel :
Frontiers in Education Conference (FIE), 2015. 32614 2015. IEEE
Print_ISBN :
978-1-4799-8454-1
Type :
conf
DOI :
10.1109/FIE.2015.7344325
Filename :
7344325
Link To Document :
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