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A Fuzzy-Based Assessment Model for Faculty Performance Evaluation

A Fuzzy-Based Assessment Model for Faculty Performance Evaluation. Mohammed Onimisi Yahaya College of Computer Sciences and Engineering King Fahd University of Petroleum and Mineral Dhahran 31261, Saudi Arabia mdonimisi@kfupm.edu.sa. February, 2011. OUTLINE. Introduction

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A Fuzzy-Based Assessment Model for Faculty Performance Evaluation

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  1. A Fuzzy-Based Assessment Model for Faculty Performance Evaluation Mohammed OnimisiYahaya College of Computer Sciences and Engineering King Fahd University of Petroleum and Mineral Dhahran 31261, Saudi Arabia mdonimisi@kfupm.edu.sa February, 2011.

  2. OUTLINE • Introduction • Existing assessment model • Background • The Evaluation Model • Results • Conclusions

  3. Introduction (1) • What is Assessment? • -placement • -classification problem • Why is Assessment required? • -required for faculty appraisal • -school placement • -school comparison and ranking • -great role in monitoring and improving the performance of • educational systems

  4. Introduction (2) • Fuzziness in Assessment • questionnaire often contains fuzzy statements such as • -strong • -competent • - unsatisfactory • - agree • - strongly agree etc • Question : How do you measure this ? • - These terms are vague. • Answer: Defuzzify

  5. Background Zhu and Li (2009) presented a combination of fuzzy logic system and neural network model and applied it to teaching quality assessment, Nolan (1998) reported uses of scoring rubrics will help to standardize the grading. Kai et al (2005), investigated and presented the main properties of Fuzzy based assessment models as monotone output property

  6. How Fuzzy Systems Work (1) Knowlegde base (rulebase) Defuzzification Fuzzification Decision making mechanism (Fuzzy reasoning) Figure 1. Fuzzy logic system

  7. How Fuzzy Systems Work (2) Figure2 - The features of a membership function

  8. How Fuzzy Systems Work (3) • What is Fuzzy logic ? • - simple way to arrive at a definite conclusion based upon • vague, ambiguous, imprecise • Fuzzification • - transforming crisp values into grades of membership • for linguistic terms • Fuzzy rule base (knowledge base) • -The rulebase contains the rules and forms • Fuzzy Rule Evaluation (inferencing) • -determine the firing strength of each rule • Defuzzification • -removing the vagueness

  9. The evaluation model (1) Table 1 : Performance evaluation scale Table 2 : Teaching method and Presentation Evaluation Scale

  10. The evaluation model(2) Table 3: Performance Evaluation Criteria

  11. The evaluation model(3) The expected score versus the strength of attribute of an ogive function. Expected score Strength of attribute

  12. The evaluation model(4) Figure 3: range and classes of Teaching Method

  13. The evaluation model(5) Figure 4: range and classes of Presentation and Delivery

  14. Discussion of Result(1) Figure 5: range and classes of Teaching Method

  15. Discussion of Result(2)

  16. Discussion of Result(3) Figure 6: Three Dimensional Depiction of the inference rules

  17. Discussion of Result(4) Figure 7: Plot to show the effect of Teaching Method and Presentation on performance

  18. Conclusion • In summary, • -we reviewed and presented the following some existing assessment model • -Discussed the concept of fuzzy inference system • -Presented an evaluation model for faculty performance measure satisfying the monotone property of assessment model • Finally, we presented some experimental results and discussion

  19. Thank You&QUESTIONS

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