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

mdonimisi@kfupm.edu.sa

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

Thank YouThank You&&

QUESTIONSQUESTIONS