The development of a decision matrix for selecting university research assistants in engineering and information technology fields using multi criteria analysis
<p>Selecting suitable students as research assistants is important to improve their research</p><p>skills and to reduce the cost of research projects. As such, this study sets out to develop</p><p>a general decision matrix to dete...
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| Format: | thesis |
| Language: | eng |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://ir.upsi.edu.my/detailsg.php?det=7471 |
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| Summary: | <p>Selecting suitable students as research assistants is important to improve their research</p><p>skills and to reduce the cost of research projects. As such, this study sets out to develop</p><p>a general decision matrix to determine the most appropriate criteria for evaluating and</p><p>selecting qualified students as research assistants for research funded by university</p><p>grants. In addition, the study attempts to explore the quality of such research assistants</p><p>and the challenges facing supervisors in selecting the right candidates. In this study, a</p><p>questionnaire consisting of 47 criteria adopted from a literature survey was administered</p><p>to 23 experts at two Iraqi universities taken as a case study to identify the appropriate</p><p>skills that research assistants should possess before they could be considered for</p><p>recruitment. The Fuzzy Delphi Method (FDM) was used to reveal 16 appropriate criteria</p><p>which were applied to a sample of 30 students using the Multi Criteria Decision Making</p><p>method including the Analytic Hierarchy Process (AHP) and Technique for Order</p><p>Preference by Similarity to Ideal Solution (TOPSIS). The findings showed that the</p><p>integration of FDM, AHP, and TOPSIS was effective in identifying qualified research</p><p>assistants. In particular, the statistical analysis carried out in the validation and</p><p>evaluation phases showed some variations in the mean and standard deviation of such</p><p>criteria for the first, second, and third group of students, the calculated percentages of</p><p>which were 65% and 4.5%, 52% and 3.4%, and 40% and 4.3%, respectively. In</p><p>conclusion, the decision matrix managed to distinguish and prioritize students with high</p><p>levels of skills over those with lower levels of skills. These findings imply that the</p><p>developed decision matrix could be used to support existing expert system applications</p><p>in the research ecosystem in the university</p> |
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