1. 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.

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

3. Introduction (1) What is Assessment? What is Assessment? - placement - placement - - classification problem Why is Assessment required? 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) Fuzzification Decision making mechanism (Fuzzy reasoning) Defuzzification 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) S/NScaleRemark 18 - 10Strong (S) 2 6 - 7Competent (C) 34 - 5Marginal ( M ) 41 - 3Unsatisfactory (U) S/NScaleRemark 10 - 45poor 245 - 60Fair 365 - 80Good 480 - 100Excellent Table 2 : Teaching method and Presentation Evaluation Scale Table 1 : Performance evaluation scale

10. The evaluation model(2) No Criteria 1Organization of Lesson plan: organised progression from each activity to the next 2Use of class timing: Puntuality and use of class time 3Classroom management: control of Class room environment 4Subject Matter Expertise: Mastery of and currency in subject 5Teaching Methodologies (Pedagogy/Adragogy) Mastery of teaching skill and skill 6Presentation and Delivery: Awareness of demeanor, vocabulary and articulation 7Student Involvement: evidence of active engagement and participation by students 8Learning Environment: Creates an environment conducive for learning Table 3: Performance Evaluation Criteria

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

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) Teaching Method Scale (0 -10) Presentation and Delivery Scale (0 -10) Performance Scale (0 – 100) Remark (Class) 11.481.9917.7Poor 22.923.5818.9Poor 33.624.3445Fair 45.0 45.1Fair 55.065.7348.7Fair 65.886.8765Good 77.397.574.6Good 88.717.586.5Excellent 98.49.287.7Excellent 101.975.5931Poor 112.86.6845.1Fair 120.966.6845.1Fair 139.040.5945.1Fair 147.570.86445.0Fair 158.580.86445.1Fair 168.583.5945Fair 177.667.7780Excellent 1810 87.7Excellent

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