Quality Function Deployment for Designing a Course By S. O. Duffuaa, U. Al-Turki and M. Hawsawi.

Quality Function Deployment for Designing a Course

By

S. O. Duffuaa, U. Al-Turki and M. Hawsawi

Presentation Plan

Introduction Literature review Paper objectives Problem statement Quality function deployment Application of QFD for designing

statistics course Conclusion and further research

Introduction

Academic programs are one of the main ingredient in quality of graduates

Programs are drastically affected by courses design

Courses design is essential element for building quality academic programs

Introduction

Course design is usually made by intuition and experience

Courses delivery is based on experience and ad hoc consultation

Industry/employers are usually consulted in programs design, but rarely in course design

Students input is rarely thought in the process of course delivery

A need exist to systematize the process of course design and delivery

Literature

Literature will be confined to the design and delivery of basic statistics courses

Molinero advocates teaching philosophical and conceptual aspects of statistics to OR and MS students

Hogg, Khamis provide suggestions on how to teach a basic statistics course

Macnaughton outlines goals for an introductory course

Literature QFD has been used in designing ME

programs

Problem Statement

Experience has shown in our department that our students are having problems in understanding and retaining the basic probability and statistics concepts

The concepts in basic statistics are necessary for them to succeed in advance level courses

These concept are essential for their success as industrial engineers

Objectives

To enhance the design and delivery of a basic statistics course with the following aims: To meet industrial needs for basic

statistics To improve students learning and aid

them in retaining the probability and statistics concepts

Approach

Use the methodology of quality function deployment (QFD) to design and deliver this basic course

QFD is a planning technique that is born in Japan as a strategy for assuring that quality is built into new processes. It helps organization to take the voice of the customer and factor their wants and needs into organization product and process planning

Quality Function Deployment

QFD uses matrices to help organization satisfy customer requirements

The Most important matrix is the house of quality (HOQ) that consists of several sub-matrices

Other matrices are the process planning matrix and the design concept evaluation

External and Internal Customers

Employers/Organization are used as external customers to specify their needs

Students are used as external customers to determine delivery requirements

Faculty are used as designers for the course technical requirements

Customer requirements The employers/organizations have identified

the following topics are the most important to them: Summarization of data Estimation of parameters Test of hypothesis Distribution identification Knowledge of statistics software

Students Technical Requirements  1. Knowledgeable and experienced faculty

members. 2.  Communicate well and write excellent notes. 3. Faculty members who solve homework problems

and examples. 4.     Small class size. 5. Textbook with simple language, clearly organized, and contains many examples. 6.  Statistical package use. 

Technical Requirements

The following requirements are identified to be of most importance by the faculty:

• Syllabus• Student preparation• Faculty• Teaching methods• Class size

Table 1 levels of the syllabusSub-requirement Level 1 Level 2 Level 3 Level 4 Level 5

1. Descriptive Statistics Available Available Available Available Available

2. Basics of probability Available Available Available Available Available

3. Random Variables Available Available Available Available Available

4. Sampling Distribution Available Available Not Available Available Available

5. Estimation Available Available Available Not Available Available

6. Test of Hypothesis Available Available Available Available Not Available

7. Statistical Package Use Available Not Available Available Available Available

Table 2 Levels of prerequisites

Sub-requirement Level 1 Level 2 Level 3

1. Calculus Available Available Not Available

2.College Algebra

Available Not Available Not Available

Table 3 levels of grade in prerequisites

Sub-requirement Level 1

Level 2

Level 3

Level 4

Average grade in Perquisites (G)

G>B G=C G < C B G and G >C 

Levels Education Years of Experience

Communication Skills

Level 1 Ph.D 10 E

Level 2 Ph.D 10 VG

Level 3 Ph.D 10 G

Level 4 Ph.D 5 and < 10 E

Level 5 Ph.D 5 and < 10 VG

Level 6 Ph.D 5 and < 10 G

Level 7 Ph.D < 5 E

Level 8 Ph.D < 5 VG

Level 9 Ph.D 10 G

Level 10 MS. 10 E

Level 11 MS. 10 VG

Level 12 MS. 10 G

Level 13 MS. 5 and < 10 E

Level 14 MS. 5 and < 10 VG

Level 15 MS. 5 and < 10 G

Level 16 MS. < 5 E

Level 17 MS. < 5 VG

Level 18 MS. < 5 G

Level 19 MS. < 5 E

Level 20 MS. < 5 VG

Table 4 Faculty levels

Table 5 Levels of teaching methodsSub-requirement Level 1 Level 2 Level 3 Level 4 Level 5

1. Clear Presentation Available Available Available Available Available

2. Excellent Notes Available Available Available Available Available

3. Using of educational notes

Available Available Not Available

Available Available

4. Relating Topics to Real Life

Available Available Available Not Available

Available

5. Solving Examples Available Available Available Available Not Available

6.Assigning Homework and Quizzes

Available Available Available Not Available

Available

7. Reporting Progress to Students

Available Not Available

Available Available Available

Table 6 Level of class size

Sub-requirement Level 1 Level 2 Level 3 Level 4

1. Class Size (CS) CS ≤ 20 20 <CS ≤ 30 30< CS ≤ 40 CS > 40

Syllabus Pre-

requisite Stud. Prep.

Class size

Faculty Current practice

Customer Requirements

Ratin

g

Row#

Desc

riptiv

e St

atist

ics

Ba

sics o

f Sta

tistic

s

Rand

om V

ariab

les

Sam

pling

Dist

ribut

ion

Es

timati

on

Test

of hy

pothe

sis

Stat

istic

al Pa

ckag

e us

e

Calcu

lus an

d coll

ege

algeb

ra

Colle

ge a

lgebr

a

Gaine

d gra

des i

n the

pre

-re

quisi

tes

Stud

y hab

it

Ph.D

in st

atisti

cs or

relat

ed

fields

Ma

ny ye

ars o

f exp

erien

ce

Exce

llent

comm

unica

tion s

kills

Poor

Good

Exce

llent

9 1 O O 7 2 O O O O O O 3 3 O O O O 6 4 O O O O

Comp

any R

espo

nse

1. Summarization of Data 2. Estimation of parameters 3. Test of Hypothesis 4. Distribution identification 5. Knowledge of Statistical software 7 5 O O

8 6 O 9 7 8 8 O 8 9 8 1

0 O O O O O O

1. Knowledgeable faculty 2. Communication and excellent notes 3. Solve homework problems 4. Small size class 5. Simple and clearly organized

textbook& examples 6. Statistical software use 8 1

1 O St

uden

t Res

pons

e

7 9 9 9 8 7 6 7.6 7 5 4.8 5 4 7 8

Design Concepts

A design concept is a selection of a level from the technical requirements to come up with a design that best satisfies companies and student’s requirements. As an example, a design concept can have a first level syllabus, second level student preparation, third level of pre-requisite, sixth level of faculty, first level of teaching method and a second level of class size.

 Requirements

Design concepts

1 2 3 4 5 6 7 8 9 10 11 *

Syllabus level 1 2 3 4 5 1 2 3 4 5 2

Student preparation level

1 2 3 4 1 2 3 4 1 2 4

Prerequisite level

1 2 3 4 2 3 1 2 3 1 1

Faculty level 1 2 3 4 5 6 7 8 9 10 1

Teaching methods level

1 2 3 4 5 1 2 3 4 5 1

Class size level

1 2 3 4 1 2 3 4 1 2 4

Table 8 Course design concepts

Source of Requirements

Concept Requirements

Con

cept

11

C

urre

nt P

ract

ice

Con

cept

1

Con

cept

2

Con

cept

3

Con

cept

4

Con

cept

5

Con

cept

6

Con

cept

7

Con

cept

8

Con

cept

9

Con

cept

10

Companies

Syllabus + S - - - + S - - -

Student preparation

+ + + S + + + S + +

Perquisites S - - S - - S - - S Faculty S - - - - - - - - -

Students and

Teaching Methods

S - - - - S - - - -

faculty Class size + + + S + + + S + + + 3 2 2 0 2 3 2 0 2 2

Totals S 3 1 0 3 0 1 2 2 0 1

- 0 3 4 3 4 2 2 4 4 3

Table 9 Design concepts evaluation

Conclusion and Further Research

QFD is an effective tool for designing and delivering courses.

It matches customer requirements with technical requirements.

The use of QFD provides a better understanding of the course design process.

The new course design is a balanced one.

More work could be done to identify more design concepts for evaluation.

AHP or a more sophisticated evaluation process can be used to evaluate resulting design concepts.

An awareness program must be launched before applying QFD in process, product or service design.

Conclusion and Further Research

Thank you.

Any Questions or Comments?