A Novel Knowledge-Based System Based on Combined Sociometry and Genetic Algorithm for Tutoring
Successful tutoring requires useful information concerning students, tutoring knowledge and experience, and suitable tutoring strategies. In this paper, a novel system based on combined sociometry and genetic algorithm (CSGA) is developed to help teachers to tutor their students. Just
needing to collect the choices from students, lots of useful hidden information concerning students which covers three levels (individual, dyad, and group) of social complexity can be obtained using sociometry. Subsequently, a GA combined with the knowledge of teachers in tutoring students
is employed to generate decisions such as the selection on tutoring program, the grouping of learning partners, and the arrangement of tutoring time, and so on. To evaluate the effectiveness of the proposed approach, two real datasets collected from a famous college in Taiwan was utilized.
Experimental results concerning the selection of learning partners show that students are very satisfied with the proposed system and show high intention of repeat use. In addition, a paired sample t-test shows that the overall satisfaction on the proposed CSGA approach is significantly
higher than the random method.
Document Type: Research Article
Publication date: 01 August 2013
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