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E‐learning website evaluation and selection using multi‐attribute decision making matrix methodology
Author(s) -
Garg Rakesh
Publication year - 2017
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.21846
Subject(s) - ranking (information retrieval) , computer science , selection (genetic algorithm) , decision matrix , similarity (geometry) , preference , ideal solution , machine learning , process (computing) , aggregate (composite) , data mining , artificial intelligence , matrix (chemical analysis) , preference learning , information retrieval , operations research , mathematics , statistics , physics , materials science , composite material , image (mathematics) , thermodynamics , operating system
E‐learning website selection is a deliberate decision with a significant influence on the educational sector. E‐learning website selection decision is based on multiple attributes/criteria. In the present research, the various attributes having a huge influence in the evaluation process of E‐learning websites that are termed collectively as ranking criteria were identified and extracted from the available literature at the first step. Now, the relative importance or the significance of each ranking criteria was determined by collecting data through the interviews with experts and applying some aggregate operations on that data. Simply, the priority weight of each ranking criteria was determined at the second step. The third step is concerned with the evaluation work of the alternatives against the identified ranking criteria by considering the priority weights determined at the second step. In the present study, E‐learning websites selection problem is resolved using matrix method which is capable to solve such types of multiple‐attribute decision making (MADM) problems. Further, the results obtained from the proposed methodology are compared with the existing approaches namely Weighted Distance Based Approximation and Technique for Order Preference by Similarity to Ideal Solution to validate the applicability and utility of the proposed method.