Dissertation Title: Computer-Based Productivity Assessment of Academic Staff Using Fuzzy Analytic Hierarchy Process and Fuzzy Topsis Method. This study develops an algorithmic approach by integrating the fuzzy Multi-Criteria Decision Making (MCDM) and the fuzzy TOPSIS methods to estimate the productivity of academic staff in tertiary institutions.
- Introduction
- The Problems Associated with Current Estimation Models
- The need for quantitative indicators in productivity estimation
- Research objectives
- The Research Design
- Rationale for the study
- Outline of chapters
- Conclusion
Some universities use a computerized performance management system (PMS) to estimate productivity of academic staff. Investigate the need to adopt proper techniques to estimate productivity of academic staff and academic departments;.
Introduction
A MCDM approach on Academic Department Productivity Estimation Problem
The Durban University of Technology has a center called ‘The Center for Quality Promotion and Assurance (CQPA)’ (Sattar, 2012). The method to be used to calculate the fuzzy weight vector was already discussed in section 5.5.5 (a).
Intellectual Capital Evaluation
The Analytic Hierarchy Process (AHP)
𝐶𝑛 are the criteria by which alternative performance is measured, 𝑥𝑖𝑗 is the alternative Ai score for criterion 𝐶𝑗 and 𝑤𝑗 is the weight of criterion 𝐶𝑗. 1) System evaluation criteria are defined and are related to goals. Strongly agree Agree Neutral Disagree Strongly disagree .. a) The position of the messages on the screen is consistent.
Evaluating performance of academic departments using the AHP
Fuzzy logic and fuzzy set theory
- Introduction
- Fuzzy logic
- Fuzzy logic and Boolean logic
- A mathematical representation of a fuzzy set
- Fuzzy logic membership functions
- Logical operators used in fuzzy logic
- If-then rules in fuzzy logic
- Fuzzy Inference Systems
- Conclusion
Knowledge Base Rule Base Data Base. c) The fuzzification and defuzzification modules, which fuzzify crisp inputs into fuzzy data and then defuzzify the processed data into crisp outputs. A design science (DS) approach should not only focus on developing IS applications, but the emphasis should also be on developing research instruments that can be used to evaluate the utility (or utility) of the artifact.
Technique for order preference by similarity to ideal solution (TOPSIS)
- A MCDM model and conventional TOPSIS
- TOPSIS method with fuzzy data
- A numerical example using fuzzy TOPSIS
Conclusion
In Chapter 1, problems related to productivity estimation methods currently used in universities were discussed. This chapter formed the basis for developing the productivity estimation model using Design Science Research Methodology (DSRM), which is discussed in Chapter 3.
The activities necessary in Design Science Research Methodology
The design and development of the model (artifact) includes the third activity (depicted in Figure 3-1) in the Design Science Research Methodology (DSRM). The results of the hand weighting system indicated that this academic (𝐴3) requires improvement in Teaching and Supervision (𝐶2) while the results of the fuzzy AHP method indicated that this academic needs improvement in Writing and Publication (𝐶4).
Model Development
- Methodology for productivity estimation of academic departments
Conclusion
- Introduction
- Using fuzzy logic to extend the classical object-oriented paradigm
- Fuzzy objects and classes
- The three levels of fuzziness
- Different types of relationships between classes in fuzzy object-oriented programming
- Polymorphism
- Conclusion
Therefore, the classical object-oriented approach to software development must be extended to accommodate these imprecise and ambiguous requirements (Tsaur et al., 2002). Therefore, this chapter reviews the important features of the classical object-oriented approach and then discusses how these features can be extended to accommodate fuzzy requirements. This section describes how the classic object-oriented programming approach can be extended to include requirements that are imprecise or ambiguous.
The relationships used in the classical object-oriented approach are discussed first followed by a discussion of the fuzzy object-oriented approach. The classical object-oriented approach to aggregation can be extended to consider imprecise and ambiguous requirements.
Introduction
The reliability test results for the data from Table 6-2 are shown in Table 6-3. The results of the manual weighting system showed that academic 𝐴2 is an average performer (compared to the other 2 academics) as he had the lowest performance values in 3 areas which are, Administration (𝐶1), Consultancy (𝐶5) and Services provided and External Engagement (𝐶6).
The Centre for Quality Promotion and Assurance
Key performance criteria with tangible and intangible sub-criteria
Objectives of the demonstration
The evaluation
- Develop the Hierarchy Structure
- Convert precise ratings of tangible sub-criteria into fuzzy numbers
- The intangible sub-criteria are measured
- The fuzzy judgment matrix is attained
- Calculating the CR, the Fuzzy Performance Matrix and ranking the criteria
In this section, the fuzzy weight vector is calculated for each individual pairwise comparison matrix (for each decision maker). All the steps are indicated by calculating the fuzzy weight vector of the first decision maker (D1) in relation to the six criteria. The fuzzy weight vector and consistency ratio of the first decision maker (D1) were calculated in Sections 5.5.5 (a) and 5.5.5 (b).
The normalized form fuzzy weight vector calculations, as well as all other consistency ratio (CR) calculations, are shown in the matrix below. The same method is used to calculate the fuzzy weight vector of the extended comparison matrix.
Using the Fuzzy Performance Matrix to meet the objectives
Similarly, one can conclude that academic 𝐴2 performs averagely as most of the GNP values are below the average score for each criterion. This academic can therefore be chosen as head of the research and innovation area of the department. Calculates the proximity coefficient CCI for the alternatives * @param dNeg vector of the di values.
Returns the comparison of the CCI scores for each alternative to the * average CCI score of the department. From Table 5-26, it can be deduced that 𝐴1 has performed slightly below the department's average score, 𝐴2 has performed above average, while the performance of 𝐴3 is average.
Conclusion
The performance scores of the three academics as well as the average performance score of the IT department (assuming only three academics belong to the IT department) have already been calculated in objectives 6 and 7 respectively.
Introduction
A Design Science approach to evaluation of the artifact
Comparing conventional AHP with fuzzy AHP
- Using absolute values to rate the criteria
- Calculating weights using conventional AHP
- Ranking the criteria weights
- A comparison between conventional and fuzzy AHP in terms of criteria weights
The manual evaluation system using weights
- Rating the six criteria using absolute values
- Calculating the reliability scores for the ratings
- Calculating the intra class correlation index
- Calculating the weights using absolute values
- A comparison of the criteria weights
The aim was to gather their opinions on the dumbbell system and the newly developed system in terms of the input. A comparison was made between the hand weight system and the newly developed system using quantitative techniques in terms of the objectives mentioned in section 5.4. The determination of the ranking of the criteria in terms of "importance intensity" is not mandatory with the current evaluation system.
Criterion scoring was done using absolute values indicating the weighting of the criteria as determined by each rater. Now that reliability and intraclass correlation coefficients have shown acceptable results, the weights of the six criteria can be calculated.
Evaluating academic performance using weights (absolute values)
- The approach adopted in collecting the actual data
- Calculating the inter-rater reliability score
- Evaluators opinions regarding the manual weighting system
- Comparing the two systems in terms of the objectives indicated in section 5.4
- Conclusion
Prior to analysis of the manual weighting system, rater rating patterns were determined using the intraclass correlation coefficient. When a manual weighting system was used for evaluation (Table 6-13), the results showed that Academic 𝐴1 performed well overall in 5 performance areas. The manual weighting system did not take into account the "intensity of importance" of the criteria when raters assigned scores to each sub-criterion or each criterion.
The personality of the evaluators will influence the results when the manual weighting evaluation system is used. A survey of the evaluators was conducted to obtain their opinion on both evaluation methods (see section 6.5.3).
A usability study on the new system
The results for questions (f) to (j) relate to the capabilities and functionality of the new system. One of the objectives (or functionality) of the system is to identify an academic's strengths and weaknesses. However, selecting candidates for an award or a promotion ranks lower compared to other system capabilities.
This was also found when the results of the open question were analyzed (question 10 of the questionnaire in Appendix B). It can be concluded that the results of the usability study show a high level of satisfaction among the respondents in terms of the user interface and system capabilities.
Conclusion
The most important result of the study showed that 93% of respondents preferred the new system because it was easier to enter data using linguistic values (such as 'very weak', . 'weak', 'moderate', ' good' and 'very good') instead of using exact values. The analysis of the open-ended question also concluded that it was the first time that respondents were able to enter linguistic values which resulted in monitoring and processing an academic's performance in terms of the university's key strategic goals, identification and focus points. strong. and weaknesses, and the ranking and selection of candidates for an award or a promotion. The results of the usability study also helped to identify some minor weaknesses in the system, which the researcher improved.
All these shortcomings of the manual weighing system were easily solved by language values for the newly developed soft system. In addition, the results of the usability study of the academic staff of the IT department showed the following results regarding the new soft system: (1) An academic who applied for a promotion or award was evaluated fairly.
Introduction
The Research Approach
Testing the questionnaire
Current evaluation methods can meet the principles (such as standards, quality and excellence) of the National. Current evaluation methods are effective in monitoring and processing performance in terms of the core strategic objectives such as teaching and learning, research and external. The model must be able to identify the strengths and weaknesses in terms of the core.
The model must be able to identify strengths and weaknesses in terms of key strategic objectives. The model should be able to monitor and process academic performance in terms of the core strategy.
Population
Distribution of the questionnaire
However, online distribution was attempted at the satellite campus in Pietermaritzburg and the response rate was low. Five (5) respondents from the Pietermaritzburg campus with a staff of eight (81) completed the questionnaire online. In addition to the online data collection, the researcher therefore decided to make hard copies of the questionnaire so that the respondent could fill it in manually.
Each Dean assigned the task to a member of their respective faculty to distribute and collect the questionnaires.
Ethical Considerations
Statistical Analysis: Statement of findings, interpretation and discussion of the data
- A comparison of the respondents with the population
- The objectives of the questionnaire (research instrument)
- The Research Instrument
- Reliability Statistics
- Descriptive statistics
- Factor and statistical analysis for questions 8 and 9
- Hypothesis Testing
- Correlations
- Regression Models
- Regression models and the TAM
This section describes the survey results for questions 1 to 7 of the research questionnaire. Current assessment methods are able to meet the principles (such as standards, quality and excellence) of the National Quality Framework. Current assessment methods are effective in monitoring and processing performance in terms of core strategic goals such as teaching and learning, research and external engagement.
When looking at the overall results of the survey, it is clear that respondents are dissatisfied with current evaluation methods. The model must be able to monitor and process the performance of an academic staff in terms of the core strategic objectives such as teaching and learning, research and external engagement.
Conclusion
Introduction
Objectives of the study
Findings and discussions
Suggestions for future research
Limitations of the study
Recommendations
Conclusion
Final conclusion
Pearson’s Bivariate Correlation
