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Type :thesis
Main Author :Anter, Sulaiman Abd
Title :The development of a decision matrix for selecting university research assistants in engineering and information technology fields using multi criteria analysis
Place of Production :Tanjong Malim
Year of Publication :2021
Corporate Name :Universiti Pendidikan Sultan Idris
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Abstract : Universiti Pendidikan Sultan Idris
Selecting suitable students as research assistants is important to improve their research skills and to reduce the cost of research projects. As such, this study sets out to develop a general decision matrix to determine the most appropriate criteria for evaluating and selecting qualified students as research assistants for research funded by university grants. In addition, the study attempts to explore the quality of such research assistants and the challenges facing supervisors in selecting the right candidates. In this study, a questionnaire consisting of 47 criteria adopted from a literature survey was administered to 23 experts at two Iraqi universities taken as a case study to identify the appropriate skills that research assistants should possess before they could be considered for recruitment. The Fuzzy Delphi Method (FDM) was used to reveal 16 appropriate criteria which were applied to a sample of 30 students using the Multi Criteria Decision Making method including the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The findings showed that the integration of FDM, AHP, and TOPSIS was effective in identifying qualified research assistants. In particular, the statistical analysis carried out in the validation and evaluation phases showed some variations in the mean and standard deviation of such criteria for the first, second, and third group of students, the calculated percentages of which were 65% and 4.5%, 52% and 3.4%, and 40% and 4.3%, respectively. In conclusion, the decision matrix managed to distinguish and prioritize students with high levels of skills over those with lower levels of skills. These findings imply that the developed decision matrix could be used to support existing expert system applications in the research ecosystem in the university

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