a fuzzy-based assessment model for faculty performance evaluation mohammed onimisi yahaya college of...
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A Fuzzy-Based Assessment Model for A Fuzzy-Based Assessment Model for Faculty Performance EvaluationFaculty Performance Evaluation
Mohammed Onimisi Yahaya
College of Computer Sciences and Engineering
King Fahd University of Petroleum and Mineral
Dhahran 31261, Saudi Arabia
February, 2011.
OUTLINEOUTLINE
IntroductionIntroduction Existing assessment modelExisting assessment model BackgroundBackground The Evaluation ModelThe Evaluation Model ResultsResults ConclusionsConclusions
Introduction (1)Introduction (1)
What is Assessment?What is Assessment? --placementplacement --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
Introduction (2)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
BackgroundBackground
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
How Fuzzy Systems Work (1)How Fuzzy Systems Work (1)
Knowlegde base (rulebase)
Fuzzification
Decision making mechanism
(Fuzzy reasoning)
Defuzzification
Figure 1. Fuzzy logic system
How Fuzzy Systems Work (2)How Fuzzy Systems Work (2)
Figure2 - The features of a membership function
How Fuzzy Systems Work (3)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 termsFuzzy rule base (knowledge base) -The rulebase contains the rules and formsFuzzy Rule Evaluation (inferencing) - determine the firing strength of each ruleDefuzzification -removing the vagueness
The evaluation model (1)The evaluation model (1)
S/N Scale Remark1 8 - 10 Strong (S)2 6 - 7 Competent (C)3 4 - 5 Marginal ( M )4 1 - 3 Unsatisfactory (U)
S/N Scale Remark1 0 - 45 poor2 45 - 60 Fair3 65 - 80 Good4 80 - 100 Excellent
Table 2 : Teaching method and Presentation Evaluation Scale
Table 1 : Performance evaluation scale
The evaluation model(2)The evaluation model(2)No Criteria
1 Organization of Lesson plan: organised progression from each activity to the next
2 Use of class timing: Puntuality and use of class time3 Classroom management: control of Class room environment4 Subject Matter Expertise: Mastery of and currency in subject5 Teaching Methodologies (Pedagogy/Adragogy) Mastery of teaching skill
and skill6 Presentation and Delivery: Awareness of demeanor, vocabulary and
articulation7 Student Involvement: evidence of active engagement and participation by
students8 Learning Environment: Creates an environment conducive for learning
Table 3: Performance Evaluation Criteria
The evaluation model(3)The evaluation model(3)
Expected score
Strength of attribute
The expected score versus the strength of attribute of an ogive function.
The evaluation model(4)The evaluation model(4)
System Appraisal2: 2 inputs, 1 outputs, 16 rules
TM (4)
P&D (4)
performance (4)
Appraisal2
(mamdani)
16 rules
0 1 2 3 4 5 6 7 8 9 10
0
0.2
0.4
0.6
0.8
1
TM
Deg
ree
of m
embe
rshi
p
unsatisfactory marginal competent strong
Figure 3: range and classes of Teaching Method
The evaluation model(5)The evaluation model(5)
0 1 2 3 4 5 6 7 8 9 10
0
0.2
0.4
0.6
0.8
1
P&D
Degre
e o
f m
em
bers
hip
unsatisfactory marginal competent strong
Figure 4: range and classes of Presentation and Delivery
Discussion of Result(1)Discussion of Result(1)
0 10 20 30 40 50 60 70 80 90 100
0
0.2
0.4
0.6
0.8
1
performance
Degre
e of m
embe
rship
poor fair Good Exceptional
Figure 5: range and classes of Teaching Method
Discussion of Result(2)Discussion of Result(2)Teaching MethodScale (0 -10)
Presentation and DeliveryScale (0 -10)
PerformanceScale (0 – 100)
Remark (Class)
1 1.48 1.99 17.7 Poor2 2.92 3.58 18.9 Poor3 3.62 4.34 45 Fair4 5.0 5.0 45.1 Fair5 5.06 5.73 48.7 Fair6 5.88 6.87 65 Good7 7.39 7.5 74.6 Good8 8.71 7.5 86.5 Excellent9 8.4 9.2 87.7 Excellent10 1.97 5.59 31 Poor11 2.8 6.68 45.1 Fair12 0.96 6.68 45.1 Fair13 9.04 0.59 45.1 Fair14 7.57 0.864 45.0 Fair15 8.58 0.864 45.1 Fair16 8.58 3.59 45 Fair17 7.66 7.77 80 Excellent18 10 10 87.7 Excellent
Discussion of Result(3)Discussion of Result(3)
02
46
810
02
46
810
20
40
60
80
TMP&D
perf
orm
ance
Figure 6: Three Dimensional Depiction of the inference rules
Discussion of Result(4)Discussion of Result(4)
Figure 7: Plot to show the effect of Teaching Method and Presentation on performance
ConclusionConclusion
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