chapter 2 quality excellence models -...

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CHAPTER 2

QUALITY EXCELLENCE MODELS

2.1 INTRODUCTION

Quality in engineering education is defined in terms of its ability to

satisfy the current and future needs of the stake holders namely, students, staff,

Industry and society. A balance has to be struck in meeting the demands of

industry and Society. Engineering educators agree that achieving quality in

education is the need of the hour. There exist many models which aim at

achieving quality excellence in different domains. But one has to be aware that

these models at times may convey inappropriate messages and should not be

extended beyond their range of applicability (Hank Grant 1993). Some of the

most commonly used and the most effective models are discussed below.

2.2 QUALITY EXCELLENCE MODELS

In order to identify the elements of quality, a review of literature on

quality management and services has been undertaken. In 1951, Japan began

honouring quality practices through the establishment of the Deming Award

(http://perso.wanadoo.fr/deming/Demingprize.html).On the successful implementation

in Japan, several other countries established programs to recognize the quality

practices in organizations through quality awards. Even though there are many

quality awards being promoted by various countries in areas such as

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manufacturing, services and business, the Malcolm Baldrige National Quality

Award (MBNQA), European Foundation for Quality Management (EFQM) and

Kanji Business Excellence Model (KBEM) are popular awards.

Sixteen countries have framed their National Quality Awards based on

the Malcolm Baldrige framework, EFQM and Deming awards. Majority of these

International Quality Awards were framed between 1996 and 2004 and are given

in Table 2.1.

Table 2.1 International Quality Excellence Awards

No Name of the Award Country

1 Brazilian National Quality Award (BNQA) Brazil

2 Rajiv Gandhi National Quality Award (RGNQA) India

3 New Zealand National Quality Award (NZNQA) New Zealand

4 Swedish Quality Award (SwQA) Sweden

5 Argentine National Quality Award (ArgNQA) Argentina

6 United Kingdom Quality Award (UKQA) United Kingdom

7 Canadian Awards For Excellence (CAE) Canada

8 Chilean National Quality Award (CNQA) Chile

9 Egypt Quality Award (Eg-EQA) Egypt

10 European Quality Award (EQA) Europe

11 Malcolm Baldrige National Quality Award (MBNQA) USA

12 European Quality Award for SMEs (EQA for SMEs) Europe

13 Aruba Quality Award (AruQA) Aruba

14 Australian Business Excellence Award (ABEA) Australia

15 Japan Quality Award (JQA) Japan

16 National Industrial Quality Award (NIQA) Israel

Table 2.1 continues

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17 Prime Ministers Quality Award (PMQA) Malaysia

18 South African Business Excellence Award (SABEA) South Africa

19 Hong Kong Management Association Quality Award

(HKMAQA)

Hong Kong

20 Mauritius National Quality Award (MNQA) Mauritius

21 Singapore Quality Award (SQA) Singapore

22. Srilanka National Quality Award (SLNQA) Srilanka

All these quality award models can be used as assessment models for

quality management of a corporate. These award criteria are the way to judge, as

it provides basic template on how to implement the quality process. These critical

award factors have been repeatedly recognized and represented in various

literatures on quality management. Based on the literature review, the various

dimensions have been identified as critical success factors for quality

management in institutions. Kanji business excellence Model (KBEM) has been

experimented in one hundred and forty higher educational institutions throughout

the world (Kanji et al 1999).

2.2.1 The Deming Award

The Deming award is Japans national quality award for industry. It

was established in 1951 by the Japanese Union of Scientists and Engineers

(JUSE) and it was named after W. Edwards Deming. He brought statistical

quality control methodology to Japan after World War II. The Deming Award is

the worlds oldest and most prestigious of such awards

(http://www.qimpro.com/FAQ/deming.htm). Its principles are to seek out a

national competition and commend those organizations making the greatest

strides each year in quality, or more specifically, TQC. The Award has three

award categories. They are Individual Award, the Deming Application Award,

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and the Quality Control Award for factory. The Deming Application Awards are

given to private or public organizations and are subdivided into small enterprises,

divisions of large corporations, and overseas companies. There are 143

companies which have won this award so far. Among them, only once has the

Deming Prize been awarded to a non-Japanese company: Florida Power and

Light in 1989.

2.2.2 ISO Framework

ISO 9000 is another framework, which is a procedural approach to

quality assurance. The standard of quality is defined according to the stated and

implied customer requirements, with procedures written and followed to assure

that customer requirements are consistently delivered. ISO 9000 establishes

patterns of working through procedures and audits. ISO 9000 certification

enables the streamlining of processes, procedures and internally strengthens the

system. It is easily recognized as the stamp of quality by industry

(http://www.iso.org).

An evolving quality programme can be a vehicle for the continuous

improvement of the service, as well as a means of ensuring that an organization

operates on value for money principles (Bensimon 2004).

2.2.3 Malcolm Baldrige National Quality Model

The Baldrige Model was established in 1987 to promote quality

awareness, understand the requirements for quality excellence, and share

information about successful quality strategies and benefits. The Baldrige

Criteria and assessment processes help organizations to identify, understand, and

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manage the factors that determine their success. Baldrige is relevant for all types

and sizes of organizations. It is being used by healthcare organizations,

manufacturers, service companies, small businesses, K-12 school districts,

colleges, universities, government agencies, non-profits, and others.

The Malcolm Baldrige Criteria provide the basis for assessment,

feedback to organizations and create the foundation for organizations continuous

improvement journey. The model summarizes the following as key excellence

indicators of quality management which is quite insightful.

Leadership

Strategic and operational Planning

Business Process Management

Customer focus and satisfaction

Human Resource Development and Management

Quality and Performance results

The MBNQ criteria take a multifaceted view of quality management

and encourage organizations to broaden their view of quality management from a

product quality focus to an organizational focus. In doing this, the criteria

emphasize the organizational infrastructure that is essential to maintain and

improve core quality processes. The framework provides a model that reflects the

relationships of the various aspects of management that determine an

organizations performance in Figure 2.1. The strong focus on customer

and employees, as well as effective leadership and information management

are clearly shown to be essential for organizational success

(http://www.quality.nist.gov).

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Figure 2.1 Baldrige Criteria Frame Work

2.2.4 European Foundation for Quality Management (EFQM) Model

The success of the Baldrige Model (USA) and the Deming award

(Japan) encouraged the formation of the European Foundation for Quality

Management (EFQM) in 1988. The EFQM excellence model was introduced in

1991 with the European Quality Award being awarded for the first time in 1992

(Hides 2004). Initially, it was mainly implemented by industrial organizations.

These organizations have currently built up with much experience in the issues to

be addressed when aiming for successful implementation of the model. Till now

it has been used in various industries such as schools, hospitals, police and public

organizations (http://www.efqm.org).

The EFQM Excellence Model is based on nine (9) major criteria as

shown in Figure 2.2. The fist five major criteria are addressed as Enablers

(Leadership, Policy & Strategy, Partnership & Resources, and Processes). They

show the structural preconditions of superior performance. The remaining four

Leadership

Information Management

Human Resources Management

Product and Process Management

Business Results Strategic Quality Planning

Customer Focus and Relationship Management

Customer Satisfaction

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major criteria are Results (People Results, Customer Results, Society Results

and Key Performance Results) to measure the organizations performance and

success from different stakeholders perspectives.

According to Zink et al (1995), for all institutions of higher education

in all over Europe, the EFQM Excellence Model should be the first choice. It

provides a basis for benchmarking and its validity for higher education

institutions in particular has already been demonstrated. This model is very

suitable for special types of organizations like universities, colleges and other

institutions of higher learning.

Figure 2.2 European Foundations for Quality Management (EFQM) Model

EFQM Model inherently stimulates organizational learning and

innovation and should be adopted as the recommended management

methodology serving the core purposes of HE (Osseo-Asare 2005). In particular,

it satisfies the HEs requirement that the institutional effectiveness review

process should focus on relevance to Society and Quality (1) embrace all of

Enablers Results

Innovation & Learning

Leadership

People

Policy & Strategy

Partnerships & Resources

Processes

People Results

Customer Results

Society Results

Key Performance

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HE institutions functions and activities, (2) be based on internal self-evaluation

capable of external review, (3) take into account diversity and avoid uniformity

and (4) involve stakeholders (especially students) as an integral part of the

process.

2.2.5 Kanji Business Excellence Model

Kanjis Business Excellence Model, (KBEM ) Figure 2.3, based on

Kanjis pyramid principles of Total Quality Management, links together the

prime (Leadership), four principles (Delight of the customer, Management by

Fact, People Based Management and Continuous Improvement), and the eight

core concepts, to provide the forces of excellence in an organization

(Kanji Gopal 2002). This allows organizations to compare themselves with

competing organizations. Kanji has given a holistic way of looking at

performance measurement system, which can be used to drive success by

focusing organizations efforts on the forces of excellence.

Kanji explored the importance given to the principles and core

concepts in its own higher educational model. It has been found that there are

nine critical success factors for higher educational institutions, which are taken to

the healthcare service since it deals with knowledge and skills (Kanji et al 1999).

It classifies the customers of higher education and secondary groups on the basis

of their locations, i.e. whether internal or external, and the frequency of

interactions that the institution has with them. While the educator (as employee)

is defined as the primary internal customer, the student (as educational partner) is

the secondary internal customer. Similarly, the student is also the primary

external customer and the Government, industry and parents are the secondary

external customers. Kanjis Excellence models have been tested successfully in

more than 180 higher educational institutions around the globe.

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Figure 2.3 Kanji Business Excellence Model (KBEM)

2.2.6 Triangulated Learning Model

The Triangulated Learning Model is a frame work for implementing

the engineering analysis and design process with middle school students in their

science classrooms. This model is inquirybased and integrates the components

of simulation, construction and connection. The TLM was developed from best

practices found in the engineering education literature and from our collective

experience (Pamela 2004). One of the key features of the modules is that they

emphasize the principles of engineering design by introducing the students to the

concept of iterative design process which consists methodical implementations of

planning, design, analysis and experiment (Hmelo 2000).

Prime Principles Core Concepts Excellence

L E ADE RSHIP

Delight the customer

People based management

Management by fact

Continuous Improvement

Internal customers are real

Customer Satisfaction

Team Work People make Quality

All Work is processMeasurement

Continuous improvement cycle

Prevention

B U S I N E S S

E X C E L L E N C E

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The complete model includes learning activities and assessments that

are broadly categorized under three headings, Simulation, Construction and

Connection.

2.2.6.1 Simulation

The teachers developed story boards for a web- based simulation that

would provide their students with critical understanding of the system of

variables to be used in their projects. This allowed the students to have a

conceptual understanding of the factors that affected the working system.

2.2.6.2 Construction

This phase involves building a working prototype of the system.

During this phase, the iterative engineering analysis and design process were

enacted. Formative assessment was critical during this phase in order to inform

ongoing instructions. 2.2.6.3 Connection

This phase occurs embedded throughout every aspect of the modules.

2.2.7 Boyers Model

This approach departs from the traditional research versus teaching

debate, by introducing four spheres of complementary activities. They are:

teaching, discovery, application and integration.

2.2.7.1 Boyers Model of Scholarship

Boyers attempt through this definition of the scholarship of academic

work is not to present distinctly separate parts, but to suggest that these are

overlapping qualities of academic work. It includes, firstly, the idea of critic and

conscience of society. Secondly, it is extended to embrace the role of the

university and its academy. Finally, the scholarship of teaching is concerned with

the critically reflective dissemination of knowledge to students

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(Ahmed Al-Jumaily 2000). Boyer argues that teachers must be well informed,

and steeped in the knowledge of their fields. Further, their work must not simply

to transmit knowledge, but to transform and extend it as well.

The scholarship of teaching focuses on just two important aspects.

Firstly, the notion of learning by both student and lecturer. Secondly, engineering

education as professional education that is intended to produce professional

engineers who can contribute effectively to our society. This requires the specific

involvement and commitment of industry to be involved with engineering

programs. To achieve this, industry and education partnerships must be

developed (Marchant 1994).

Scholarship of discovery comes closest to what academics speak of as

research. Research was at the conception of the university and continues to be

central to the work of higher learning (Buckeridge 1998).

The scholarship of application has two components. The first is

community service. It is not enough to teach our students to understand the

social, economic and environmental consequences of engineering activities, and

then the faculty would have a responsibility to serve the interests of the larger

community through serious scholarship. The second aspect of application, as

applied to engineering academic work, relates to the aspect of consultation.

Consultancy provides one of the ways to maintain and increase this level

of industrial experience on both personal and institutional levels

(Ahmed Al-Jumaily 2000).

Boyer maintains that scholars who give meaning to isolated facts,

putting them in perspective, making connections across the disciplines, placing

the specialties in larger context, illuminating data in a revealing way and often

educating non-specialists are essential ingredients of the system internally. In

response to calls for integrated and inclusive curricula, many new and revised

curricula have moved away from the traditional reductions approach to

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engineering knowledge and practice. Those who work on program development

teams to shape a core curriculum or prepare cross-disciplinary seminars are to

engage in integration. Engineering curricula integrates multi-skilled graduates

with research, financial, environmental and management skills (Clark 1997).

Boyers model reiterates the primacy of teaching but integrates this

with discovery, application and integration. The application of engineering

knowledge particularly through consultancy is more clearly recognized.

Engineering faculties need to reach out, establish networks and strategic

alliances. Building these into long-term relationships will enhance their ability to

produce graduates who are technologically competent and intellectually

confident about their place in the global economy. To do so engineering faculties

are to be empowered so as to expose their academic talent, in particular those

spheres currently ignored or neglected, by redefining what it means to be an

academic.

2.2.8 The Telemark Model

The Telemark Model was developed about 1980 and adapted as the

pedagogic method for the entire Telemark State University, Norway from 1982.

The Telemark Model has tended to stimulate academic diversity and personal

growth. Students at large feel quite comfortable with this way of learning

(Clausen 1996). The Telemark Model is characterized by the group, the project,

the adviser, the documentation, and the evaluation.

This model realizes objectives like

teaching the fundamentals

helping the students how to learn, and

giving the students some training in solving problems

The model describes the ideal role of the teacher as an adviser is: The

real challenge in college is not covering the material for the students; it's

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uncovering the material teaching with the students. The adviser should be the

insightful indirect leader letting things happen (Clausen 1994).

2.2.8.1 Curriculum Change

The partial shift of responsibility from the teacher to student groups

will lead to the growth of new curricula containing several elements necessary

to cope with the realities in the world of today (Clausen 1995). The "new"

curriculum may include:

1. Among the tangible aspects, some are training in practical

leadership, applied to handling and following up formal meetings,

making oral presentations, basic technical writing including style,

grammar, spelling etc. This also involves training in finding and

applying appropriate technical solutions even in fields not taught

at the college.

2. Some intangible parts of the "new" curriculum include experience

with a variety of group psychology processes, development of

personal attributes as creativity, social adjustment, responsibility,

flexibility, initiative, courage and perseverance (Clausen 1996). 2.2.9 Wisconsin Model Academic Standards

The clarity of academic standards provides meaningful, concrete goals

for the achievement of students with disabilities and accelerated needs consistent

with all other students. Wisconsin means that all students reaching their full

individual potential, every school being accountable, every parent a welcomed

partner, every community supportive, and no excuses. As students apply their

knowledge both within and across the various curricular areas, they develop the

concepts and complex thinking of educated persons. Teachers in every class

promote the learning of the subject content and extend learning across the

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curriculum (http://www.dpi.state.wi.us). These applications fall into five general

categories:

Application of the Basics

Ability to Think

Skill in Communication

Production of Quality Work

Connections with Community

2.2.10 Malaysia Model

This engineering education model developed in Malaysia is expected

to be capable of achieving global recognition and accreditation for excellence in

engineering practice as well as educating future leaders. This includes

strengthening the scientific and professional competency base of the engineering

studies, and the inclusion of various humanistic, industrial, practical, global and

strategic skills (Megat Johari 2002).

In this model, engineers must demonstrate good scientific knowledge

with the development of general skills, such as those related to self-directed

knowledge acquisition, so that engineers will be able to cope with the rapidly

expanding amount of new knowledge in the world. Engineering graduates must

be equipped with management and related skills to ensure better chances to reach

top management post in the industry. Engineers must be able to adapt with the

changing emphasis of scientific fields, for instance in information technology and

bioengineering. In arriving at the Malaysian engineering education model, two

elements were evaluated. These were input (entrance qualification) and output

(type of engineers needed) elements. Based upon these two elements, the third

element i.e., the formation process was determined.

The new Malaysian Engineering Education Model aims to produce

graduates with a strong scientific base, innovative, professionally competent,

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multi-skilled and well respected. Their progression to successful industry leaders

would become a natural consequence. The engineering degree programme must

be enhanced to a four year programme and begin with a strong emphasis in

scientific competency (engineering sciences) and on this strong scientific

foundation shall be built the global and strategic, industrial, humanistic, practical

and professional skills and competencies (Megat Johari 2002).

2.2.11 Engineering Learning Model

This is a novel inter-disciplinary model of learning from an engineers

point of view where the flow of information during learning is described.

Learning theories that are favored by psychologists and by industry for education

of adult learners turn out to be too simplistic for application to engineering

education. An integrated learning model that is taking into account recent results

from cognitive psychology, from neurophysiology, and from information

processing is not available. This interdisciplinary model of learning is from an

engineer's point of view. Learning was described as an adaptive and nested

feedback control process that comprises different levels of learning, as reacting

automatically to recognized situations, training of skillfully handling decisions,

or handling abstract ideas. Thus, it might be explained why learning to

understand abstract ideas takes considerably more time than learning to handle

situations by rote (Hoffmann 2005). From that, conclusions might be drawn

concerning mediating knowledge in classroom, or on designing complete

curricula for engineering education. The Consequences and conclusions:

Learning is a multiple-step process on different levels: Learning is

a process that consists of multiple sub-processes of recognition,

assessing and detection.

Learning success needs motivation and repetition: Learning needs

attention. So it is supported by motivation. Moreover, if a pattern is

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not repeated several times then it is not detected as something

worth learning.

Learning needs time and thought: Conceptual, canonical and

strategic.

Knowledge on a higher level supposes active comparison of

patterns, of procedures, and of real and imaginary episodes. This

needs considerable amount of time and thought.

Curricula might be optimized for learning success: In order to

educate students optimally, enough time must be provided in the

curricula for consequently building-up knowledge of all types, and

to network them.

While all the quality frameworks discussed in the previous sections do

bring out certain qualities of the programme under consideration, they need to be

customized to meet the requirements of respective engineering education

programmes (www.qaa.ac.uk).

2.3 EXTRACT OF THE MODELS Each of the discussed quality excellence models has their own unique

characteristics and domain of applications. The critical success factors of various

quality excellence models are summarized in Table 2.2.

The major critical success factors identified from Various Quality

excellence models Process Management, People Management, Planning /Policy,

Leadership, Continuous Improvement, Focused Approach, Performance/Results,

Measurement of Resources, Leadership, Team work and Inter Disciplinary.

Engineering education is a large and complex enterprise in any country and

deserves to be looked at independently without treating it similar to an industrial

enterprise. In the present context, it would be appropriate to develop a

customized model for achieving excellence in affiliated colleges based on the

extract of these proven models.

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2.4 QUALITY TOOLS

Conventionally, engineering degree programs are offered by university

departments or by technical universities or engineering colleges which are

affiliated to universities. An affiliated college would teach the courses in-house

whereas the conduct of exam and award of the degree will be by the affiliating

university. If the affiliated college is autonomous, the framing of the curriculum

and syllabus and conduct of the exams will all be done in-house whereas the

award of the degree alone will be by the affiliating universities. This thesis

addresses the issue of enhancing quality excellence in an affiliated technical

institution.

Identifying sub-areas and critical success factors for enhancing quality

of education in an affiliated type of technical institution is an important factor. In

order to identify those areas and factors, quality tools such as literature survey,

questionnaire construction, questionnaire response, interviews, reliability study

and validity of the processes were used. Analytical Hierarchy Process was then

used to prioritize the areas and factors. So that a customized quality excellence

model is arrived at for an affiliated type of technical institutions.

2.4.1 Questionnaire

It is a quality tool for gathering information from a number of people.

It is used to find out what an extended group of people are thinking and saying. It

will be a waste of time and resources, if the information is collected from a

selection of personal contacts.

2.4.1.1 Types

Open Ended: These questions are asked to gather information about

how a particular thing is going on.

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e.g. How is project management being done in your organization?

Multiple Choice: To a question, a set of different answers are given

and the person answering has to choose one among them.

e.g. The size of your shoe is

(a) 10

Scaled: These questions ask the person answering to give a particular

rank to the asked question on some fixed scale.

e.g. what do you say about the Indian cricket team's last six months

performance.

(a) Very bad (b) bad (c) average (d) good (e) Very good

Generally, the scale taken is Odd point scale (i.e. five point scale or 7

point scale). This is done as it helps in dividing the ranks in two equal

halves.

2.4.1.2 Ground Rules

Frame Questions: Questions should be framed keeping in mind what

information is needed, who are the target audience and what is the

method of analysis.

KIS: Keep it Short. Questions should not be too lengthy. People loose

interest in reading lengthy questions.

Non-leading Questions: Questions should be checked to see that they

are not leading to the answers that you will get.

Test It: Questions should be tested before being sent to the target

audience i.e. whether questions are understandable, it is not taking too

much time and it is phrased in manner that it presents what you want to

ask.

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2.4.1.3 Merits

It is the most economic way to gather information from a large

group of people.

Information straight from the people who matter i.e. the end user,

customer etc.

Provides information on what they are thinking instead of just

impressions.

2.4.1.4 Demerits

Sometimes different people answering the same question may have

different understanding about the same question leading to biased or wrong

result/analysis and Considered as wastage of time by people who fill it. Hence,

the result is biased.

2.4.2 Analytical Hierarchy Process

Analytic Hierarchy Process (AHP) is a decision approach designed to

aid in the solution of complex multiple criteria problems in a number of

application domains. This method has been found to be an effective and practical

approach that can consider complex and unstructured decisions. AHP uses a five-

step process to solve decision problems. They are,

Create a decision hierarchy by breaking down the problem into a

hierarchy of decision elements.

Collect inputs by a pair wise comparison of decision elements.

Determine whether the input data satisfy a consistency test. If not,

go back to step 2 and redo the pair wise comparisons.

Calculate the relative weights of the decision elements.

Aggregate the relative weights to obtain scores and hence rankings

for the decision alternatives.

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Step 1 Formation of hierarchy: The decision hierarchy is formulated by

breaking down the problem into a hierarchy of decision elements.

Step 2 Collection of inputs: Consulting more experts will avoid bias that

may be present when the judgments considered are from a single

expert. The nominal-ratio scale of 1 to 9 (Saaty 1994) is adopted based

on the three criteria easiness, clarity, and extraction of correct

responses.

Step 3 Consistency Test: The local priorities of the alternatives can be

calculated and consistency of the judgments can be determined from

the judgmental matrix. It has been generally agreed (Saaty 1980, 2000)

that the priorities of criteria can be estimated by finding the principle

eigen vector w of the matrix A.

Aw= ( max) w

When the vector w is normalized, it becomes the vector of priorities

of the criteria with re max is the largest eigen vector w

contains only positive entries. The consistency of the judgmental matrix can be

determined by a measure called the consistency ratio (CR), defined as:

CR= CI/RI,

Where CI called the consistency index and RI, the Random Index.

CI is defined as: CI= ( max-n)/ (n-1), where n is the size of the matrix.

RI is the consistency index of a randomly generated reciprocal matrix

from the 9-point scale, with reciprocals forced. (Saaty 1980, 2000) has provided

average consistencies (RI values) of randomly generated matrices for a sample

size of 500. If CR value of the matrix is higher, it means that the input judgment

is not consistent and hence is not reliable. In general, a consistency ratio of 0.1 or

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less is considered acceptable. If the value is higher, the judgments may not be

reliable and have to be elicited again.

Eigen vector and Eigen value are calculated from the judgmental

matrices.

Step 4 &5 Calculation of relative weights and ranking of alternatives:

Geometric mean method has been the most widely applied method in

AHP for aggregation of individual preferences when more than one

expert is involved in the decision making. Geometric means of

individual opinions are calculated and entered in the final judgmental

matrix for finding out the ranks of the alternatives.

2.5 COLLECTION OF EXPERT OPINIONS

Responses were collected in the developed questionnaire from

Principals of Engineering Colleges, Faculty from Indian Institute of Technology

Chennai, Indian Institute of Science Bangalore, National Institute of Technology

Trichy and Social organization through personal interviews. Faculty from higher

learning institutes were also included so that the areas identified are of the class

practiced in those institutes. The number of experts from the respective

institutions is given in Table 2.3

Table 2.3 Number of Experts from Respective Institutions

Field of Expertise Number of Experts

IIT 1

IISc 1

NIT 3

Engineering College 4

Social Organization 1

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The experts identified the factors that would influence on the quality of

education in affiliating type of institutions. They were then asked to rank the

alternatives and to mark the relative importance of alternatives with respect to the

critical factors. The ranking of alternatives and the individual attention of the

expert to each of the responses at the time of taking responses itself, assured a

forced consistency among individual responses.

The identified factors for quality excellence in technical institutions

include Teaching and Learning, Research and Development, Industry Institute

Interaction, Student Activities, Physical Resources, Financial Resources and

Support Processes.

The individual responses were entered in the positive reciprocal matrix

and the geometric mean of these responses were calculated to get the overall

group response in the form of judgmental matrix. The details of the judgmental

matrix, priority Vectors, max, Consistency Index and Consistency Ratio are

given in Table 2.4.

Table 2.4 Judgmental Matrix of Factors Influencing Engineering Education

Factors TLP R&D III SA PR FR SP Weights(local)

TLP 1.00 3.23 4.91 6.71 7.11 6.94 8.77 42.31%R&D 0.30 1.00 3.49 4.91 5.08 4.82 7.36 24.25%III 0.20 0.28 1.00 3.32 3.15 3.49 5.08 13.49%Student Activities 0.14 0.20 0.30 1.00 1.11 1.24 3.15 5.98%Physical

Resource 0.14 0.19 0.31 0.89 1.00 1.39 3.32 5.93%

Financial

Resource 0.14 0.20 0.28 0.80 0.71 1.00 3.49 5.44%

Support Processes 0.11 0.13 0.19 0.31 0.30 0.28 1.00 2.60%

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It is observed that max = 7.3657, C.I. = ( max-n)/(n-1) = 0.0609,

R.I. = 1.32 and C.R. = 4.62% for the identified factors that could influence

engineering education. If CR value of the matrix is higher, it means that the input

judgment is not consistent and hence is not reliable. In general, a consistency

ratio of 0.1 or less is considered acceptable. If the value is higher, the judgments

may not be reliable and have to be elicited again. Among these factors, Teaching

and Learning, Research and Development and Industry Institute Interaction have

received considerably significant weights whereas Student Activities, Physical

Resources, Financial Resources and Support Processes received least

significance. However few of the important aspect of student activities, in the

domain of co-curricular activities were co-opted into Teaching and Learning

Process and activities such as Placement were co-opted into Industry Institute

Interaction so as to enhance the quality process.

The research problem is formulated based on the above findings and

hence the objectives of the research work are set as

To develop a novel model for improving the areas namely

Teaching and Learning, Research and Development, Industry

Institute Interaction

To carry out a pilot study to validate the model.

To make impact analysis of the models for the respective areas.

2.6 PROPOSED MODEL

2.6.1 Preamble

In Indian higher education system, Institutes of higher learning like

Indian Institutes of technologies (IITs), Indian Institute of Science (IISc) and

National Institute of Technologies (NITs) have teaching and research capabilities

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in many domain. But engineering institutions affiliated to an university may not

have the faculty, infrastructure and ambience for promoting teaching and

research in all domains concurrently. But to achieve excellence, as identified by

the experts, an institution should have a dynamic teaching and learning process,

contemporary and appropriate research & development mechanism and

sustainable interaction with industry to meet the societal needs.

2.6.2 Special Interest Group (SIG)

In the proposed approach each department of an institution is allowed

to choose a theme area based on the vision of the college, technology trends,

technical expertise available, interest among the faculty and infrastructure

available and proposed. The department is to identify sub-areas under the theme

areas. The sub areas involving the same department are identified as Special

Interest Group (SIG). Each group would concentrate only in one particular area,

and the group would evolve itself as a specialist in that particular area. Members

of this group include both faculty and students (both UG and PG). Select their

SIG based on their interest and specialization.

An open house is to be conducted during their first year of study,

during which a student get acquainted with the facilities under various special

interest groups. After this they could select their own group.

Once part of the group, the members may be taught the fundamentals

by both the staff and the senior students of that group. Thus, they may be

introduced to the domain area during their second year of engineering itself,

which otherwise would be possible only during the third or final year. Further

they could also start working on projects in their area and could also

continuously update their knowledge on current trends. Each SIG may establish

46

its own laboratory with state of the art equipment. While recruiting the staff

members, their core area and area of specialization could be identified and once

appointed they may be encouraged to start their work in that area.

2.6.3 SIG Model

In the SIG model, identification of the theme area and sub areas are

followed up by the formation of the Special Interest Group (SIG) is shown in

Figure 2.4. For each of three critical influencing areas namely Teaching and

Learning Process, Research and Development and Industry Institute Interaction,

the respective critical success factors will be identified so that suitable

approaches could be taken to achieve excellence in engineering education.

Figure 2.4 Proposed SIG Model

Theme area for the Department

Sub Areas

Faculty, Staff & Student membership

Special Interest Group (SIG)

Research and Development

Teaching and Learning

Industry Institute Interaction

Excellence in Engineering Education

47

2.7 METHODOLOGY

A pilot study is mandatory for the development of reliable, valid and

practical instruments. Also, such instrument and their findings can be effectively

used for construction and betterment of quality management models. This can be

achieved in a systematic way to measure the stakeholders perceptions from

organizations (engineering colleges). Questionnaire survey has been widely used

as an efficient tool for capturing ideas of individuals/institutions on the subject

under research. In order to validate the four critical success factors, a survey

instrument consisting of thirty nine (39) factors for engineering education

questionnaires has been constructed.

This instrument has been developed on the basis of an exhaustive

review of literature (conceptual, observation) and also among experts in India.

The instrument has been refined many times based on the inputs and comments

received from the experts. The instrument has been developed to receive the

insights and aspects of quality management with respect to quality Education.

The pilot study was conducted to explore the critical success factors

and clarify the quality dimensions used by various stakeholders to evaluate

technical education. Quantitative methods were employed to develop a

measuring instrument on the engineering education quality dimensions to assess

the validity and reliability. The primary focus of this exercise is to develop and

test the measuring instrument.

The study was conducted through the engineering institutions in

Southern districts of Tamilnadu, India shown in Table 2.5

48

Table 2.5 Details of Faculty Members Participated in the Survey of

Impediments Southern Districts of Tamil Nadu

2.7.1 Survey Instrument

The purpose of field test survey instrument was designed and

developed by the need to capture the real life processes that might be affected the

engineering education

Step 1: An Inventory was accomplished on each of the proposed educational

quality dimensions to ensure the conceived ideas.

Step 2: A pool of 10-20 survey items was developed for each of the eight

common dimensions. The items were reviewed by individual with

questionnaire construction experience and the expert in the field of

technical education.

Step 3: The instrument was pre-tested with the various stakeholders to

measure completion times, determine the area of confusion, and

access affective responses to the survey. Many modifications were

done based on the pre-testing.

District Number of Respondents

Dindigul 33

Madurai 37

Sivagangai 36

Theni 32

Tirunelveli 35

Tuticorin 36

Virudhunagar 31

Total 240

49

2.7.2 Data Collection

This thesis presents to study identify the impediments to the quality

improvement of engineering education. The study is framed so as to collect

primary data from the stakeholders of engineering education and use in the

second chapter. There are many stakeholders of engineering education, namely

students, teachers, parents, industries that employ the graduates and

managements of colleges. While perceptions of all the stakeholders are

important, all of them other than the teachers have limited interactions with the

system and can access only some of the quality characteristics of the programme.

The teachers have a role to play with regard to Teaching and Learning Fifteen

(15) critical factors, Research and Development Ten (10) Factors and Industry

Institute Interaction Fourteen (14) Factors. Hence, the teachers of engineering

education are selected as the respondents for the survey.

The interest here is to find out the impediments currently existing in

the engineering education sector and to prioritize the initiatives needed for the

improvement of quality of education. This Quality of Education can be assessed

using a set of thirty nine (39) factors shown in Table 2.6. The study involves the

collection of subjective opinions and multilevel decisionmaking.

Respondents were randomly selected from faculty members from

various Engineering Colleges like Autonomous, Government, Government

Aided, Self financing Colleges and either pursuing PG or Ph.D or attending

refresher courses. They have the knowledge and experience to prioritize the

factors with regard to the quality of engineering education. It can be assumed that

this sample selection provided openness, randomness as well as quality

awareness in the response. Two hundred and forty members representing

opinions from various parts of southern district of Tamil Nadu were personally

50

interviewed for data collection. The details of faculty members participated in the

survey are displayed in Table 2.5.

2.7.2.1 Local Priorities and Consistency of Comparisons

The stake holders were requested to rank the factor groups with respect

to their importance in improving the performance of the engineering education.

Then they were asked to compare the relative importance of these factor groups

with each other and to mark them on the given 1-9 scales. The same procedure is

repeated for the factors coming under each factor group by comparing their

relative importance in improving the factor group of the programme. Of the

respondent, 40% were male and 61% were female. The majority were aged 31-55

years (45%), followed by 20-30 years (31%). The respondents in the engineering

education were heterogeneous in their personal characteristics e.g. age group,

gender and marital status, educational level, income, employment status. The

response rate is 60% of those who received the questionnaire and approximately

40% of those who did not return measuring instrument. These response rates are

achieved due to periodic follow-ups over telephone and personal visits. The

critical influencing areas and critical success factors of engineering education

quality measure are given in Table 2.6.

Respondents were requested to note two points while filling up the

questionnaire.

1. All the responses should be in the light of the situation prevailing in

their department in their institute.

2. Factors which need immediate attention and improvement in their

department should be given higher score.

51

Sample Questionnaire is attached in the Appendix 2. The ranking of

alternatives and the individual attention of the stakeholder to each of the

responses assured consistency of response.

Table 2.6 Identified Critical Factors and Sub Factors for Quality

Excellence in Engineering Education

Sl.

No.

Critical influencing

areas Critical success Factors

1 Teaching Process Subjects Handling (SIG based)

Case study Based Teaching

Inter Disciplinary approach

Guest lecture / Seminar

Class Room Management

Projects Guidance

Educational Tour

Motivation to the students

2 Learning Process Problem based learning

Group, co-operative or collaborative learning

Hands on/ experimental Learning and practice

Interactive lectures

Learning Style (Visual, audio etc)

Diversity in learners

Learner centric approach

52

Table 2.6 continued

3 Academic Research Research Topics SIG Based

Resource and knowledge sharing

Interaction with similar groups

Attending and organizing conferences.

4 Sponsored Research Visits to Institute / Industry

Open House

Proposals

Presentations

Professional Society membership

Alumni Interaction

5 Training and Placement Career guidance

In plant Training / Industrial Visits

Management Skills

Software certification and computer

proficiency

Industrial / mini project

Communication

Special lecture / courses

Intra departmental participation

6 Staff Industry /Institute

Interaction

Visits to Institute / Industry

Open House

Proposals

Presentations

Professional Society membership

Alumni Interaction

53

2.7.2.2 Scale Refinement and Validation

A critical aspect in management theory is the development of authentic

measures to obtain valid and reliable instrument of the measurable constructs and

their relationships to another. So, initial research should find out the intricate

dimensions of management, check that they are measured reliably and validly

and subsequent determination of influence on organization performance.

Reliability and validity should be measured to standardize the measurement

scales otherwise it will misguide the researcher about the actual measure, what

they are supposing to measure. The extensive review of literature and

identification of critical success factors of the construct have lead to the theory

and concepts about a particular management concept.

The constructed questionnaire has been validated and tested for

reliability. A fundamental requisite for checking the validity is through

correlation coefficients of measure. Correlations are especially useful for

answering the question i.e. what is the survey instruments predictive or

concurrent validity (Taylor and Morris 1985). Keeping in view, in order to test

the validity of the statement of questionnaire; the coefficient correlation was

computed. The values ranging from 0.87 to 0.99 (moderate to very positive

correlation) for each pair of dimensions, signify that the engineering education

quality dimensions are distinct even when measurement error is taken into

consideration. Discriminant validity was carried out and the result shows that the

dimensions are correlated. Values of correlation coefficient beyond 0.8 indicate

a very strong relationship and below 0.4 generally represent a weak relationship

(Taylor and Morris 1985). These findings provide the evidence for the

discriminant validity of dimensions in the constructed engineering education

quality measurement scale.

54

2.7.2.3 Reliability Analysis

Many measures are available for reliability and it can be evaluated in

order to establish the reliability of a measuring instrument. The various reliability

methods are: test-retest method, split-half and internal consistency method. The

internal consistency method is most frequently used for simple administration

and effective in onsite studies (Suresh Chander et al 2002). Also, this internal

consistency method is considered to be the most general form of reliability

estimation. The degree of inter-correlations among the items will form a

scale and confirms the reliability as internal consistency; internal consistency

of a construct is the homogeneity of the items of a concerned scale

(Nunnally 1978). Internal consistency is estimated using reliability coefficient

called cronbach alpha (Cronbach 1951).

Cronbachs alpha measures how well a set of items measures a single

unidimensional latent construct. Unidimensional refers to the existence of a

single construct underlying a set of measures. When data have a

multidimensional structure, Cronbachs alpha will usually be low. It is not a

statistical test but it shows the consistency or coefficient of reliability. The

reliability for engineering education quality measurement scale was found out by

computing the internal consistency reliability of the items included in each of the

dimensions. An alpha value of 0.7 or above is considered to be the criterion for

demonstrating internal consistency of established scales (Nunnally 1978). The

Cronbachs alpha values for all the ten scales are shown in Table 2.7. The result

shows that the values ranged from 0.68 to 0.82 are reliable scales. All the values

are around the obligatory requirements, thereby testifying that all the ten scales

are internally consistent and has acceptable reliability values in their original

form.

55

Table 2.7 Cronbach Alpha for EEQ Scale Dimensions

Engineering Education

Quality dimensions

Teaching Learning AR SR Training

and Placement

Staff III

Cronbach 0.81 0.68 0.82 0.78 0.76 0.78

Correlation co-efficient

Teaching 1

Learning 0.98 1

Academic Research

0.94 0.98 1

Sponsored Research

0.94 0.98 0.97 1

Training andPlacement

0.91 0.97 0.99 0.97 1

Staff III 0.87 0.95 0.97 0.96 0.99 1

2.7.2.4 Validity Analysis

Researchers confirm the methodology to describe the validity often

depends upon the nature of problem and judgments of researcher (Kothari 2004).

An elaborate list of validity types that are typically mentioned in books and the

research papers are: Face validity, Content validity, Criterion-related validity

(predictive and concurrent) and construct validity.

2.7.2.5 Face Validity

Face validity is the concurrence from the experts in that domain,

measure is valid. The purpose of measure is visualized on the items of construct

then the measure is considered to have face validity. Surprisingly, one who looks

56

at the measure and appears good adaptation of the construct. It seems like

spontaneous confirmation of construct validity, but researchers must rely on

subject experts on their judgments in their research. As the items representing the

five engineering education, quality factors have been identified from the

literature, their selection is justified, thereby confirming the face validity of the

instrument. The face validity has been established by an extensive review by

Juries. Juries are the experts in the concerned domain of technical education.

Four experts have been contacted in the respective domains to confirm the

content construction of survey instrument.

2.7.2.6 Content Validity

Content validity of a survey instrument is the extent to which a

measuring instrument provides adequate change and coverage of topic under

study. This is another type of validity which is logical, subjective and intuitive

rather than statistical. This is determined by a group of experts who shall judge

how well the measuring instrument meets the standards, but there is no numerical

way to express it (Kothari 2004). The instrument has been developed based on

comprehensive analysis of the conceptual. Moreover, content validity has been

established and ensured by a thorough review and opinion of Juries.

2.7.2.7 Criterion Related Validity

The fundamental idea of criterion-related validity is to check the

performance of the measure against some criterion (Flynn et al 1994) showed

that criterion-related validity is a measure of how well scales representing the

various quality management practices are correlated to measure the performance

criteria of quality. Conventionally, criterion related validity is evaluated from the

values of correlations of the factors with one or more measures of quality. In this

57

pilot study research, the criterion related validity is established by correlating the

values with two factors considered for the engineering education quality

measurement i.e. teaching and learning. Teaching is the crucial variable in any

engineering institution. The correlations are given in Table 2.7. It is observed that

all the scores have significant and positive correlations with every dimension.

Thus criterion- related validities. The survey instrument thus standardized can be

used to measure the levels of engineering education quality being practiced in

engineering institutions.

All the correlations have been found to be statistically significant. The

correlation value is high in the learning factor. It is because in engineering

institution learning is more important and hence higher correlation value

(Palani Natha Raja 2007).

2.8 PILOT STUDY

Thiagarajar College of Engineering (TCE), Madurai, was identified as

the institution for study. This college is an autonomous institution, affiliated to

Anna University, Chennai. TCE is funded by Central & State Governments and

is approved by All India Council for Technical Education, New Delhi, accredited

by National Board of Accreditation. TCE offers Seven Undergraduate

Programmes, Thirteen Postgraduate Programmes and Doctoral Programmes in

Engineering, Science and architecture.

2.8.1 Quality Policy

As a first step the quality policy for the college was framed to

encompass the identified factors for achieving quality excellence in technical

education. Quality policy of Thiagarajar College of Engineering: TCE is

committed to create quality professionals to meet the emerging industrial and

58

social needs through, innovative teaching, applied research, Industrial interaction,

placing faith in human values, aiming at continual improvement in all activities.

2.8.2 Theme Areas and Special Interest Groups (SIGs) at TCE

The theme areas and Special Interest Groups (SIGs) for different

departments in Thiagarajar College of Engineering were identified based on the

vision of the college, technology trends, expertise available and infra structures

are available. They are given in Table 2.8.

Table 2.8 Theme Areas and SIGs at TCE

Department Theme area Special Interest Group (SIG)

Civil Engineering

Eco Friendly

Structures Steel structures

Concrete structures

Soils and roads

Environmental Engineering

Water resources

Solid waste management

Computer Science

Engineering and

Information

Technology

Distributed

Computing and

Database

Management

System

Open source software

Database management

Network security

Multi-core architecture

Multimedia

Software engineering

59

Table 2.8 continued

2.8.3 Implementation

In order to carry out a pilot study, the Electronics and Communication

department at TCE was chosen as the model. The theme area for this department

is Wireless Technologies. The sub areas in which special Interest Groups were

formed are : Radio Frequency Systems (RF), Digital Signal Processing (DSP),

Very Large Scale Integration (VLSI), Image Processing (IP), Networking (NW),

and Embedded Systems (ES).

Mechanical Engineering Automation Vision systems

Supply chain management

Quality engineering

Thermal engineering

CAD/CAM

Machine design

Electrical and Electronics

Engineering

Power

Systems and

Energy

Power systems

Power electronics and devices

Digital control

Soft computing

Instrumentation

Energy studies

Electronics and

Communication

Engineering

Wireless

Technologies

Radio Frequency Systems

Digital Signal Processing

VLSI System

Image Processing

Networking

Embedded Systems

60

A Special Interest Group will have faculty members who have done or

pursuing their Ph.D in that area, UG and PG Students who have decided to do

their projects in that area and fresh men who are keen to work in the area. An

open house programme is conducted for the freshmen to the department,

permitting them to visit all the laboratories and providing them opportunities to

listen to faculty and students belonging to each SIG. There is no rigid frame work

in the sense that students who would like to switch to different areas in the

middle of these course are allowed to do so. The special Interest Group

ultimately provides an opportunity to go beyond curriculum and leave

asynchronously. SIGs of the ECE department on critical influencing areas like

Teaching, Learning, Academic Research, Sponsored Research, Training and

Placement and Industry Institute Interaction. From the Table 2.9, it is observed

that different SIGs have different versatility with respect to the influencing areas.

Each area has a well established laboratory with the state of the art

equipment. While recruiting the staff members, their core area and their area of

specialization are identified and they start work in their area. The staff members

choose their own area with respect to their specialization and interest.

Table 2.9 Various SIGs of the Department of ECE

Critical influencing

areas

Electronics and Communication Engineering

SIG1 SIG2 SIG 3 SIG4 SIG5 SIG6

RF DSP VLSI IP NW ES

Teaching 42 80 73 80 64 82

Learning 43 78 69 80 62 80

Academic Research 60 88 78 62 63 52

Sponsored Research 82 82 48 68 41 58

Training and Placement 42 52 83 58 72 82

Staff III 82 72 40 52 40 78

* in Percentile

61

2.8.4 Influence of SIG in ECE Department on Teaching and Learning

Process

The Radio Frequency System is one of the SIGs in the department of

ECE. Here this is taken up to study the influence of SIGs on the teaching

Learning process in the ECE department and hence on a typical engineering

education system. The influence of SIG on Radio frequency (RF) system on

critical success factors pertaining to Teaching and learning process in ECE

department is given in Table 2.10 and 2.11 respectively.

Table 2.10 Influence of SIG on Critical Success Factors of Teaching Process

Critical success factors of Teaching

and Learning Process

Electronics and Communication Engineering Department

Teaching SIG - Radio Frequency (RF)

Subjects Handling

(SIG based)

Faculty belonging to RF group will handle subjects that

belong to RF or its sub areas such as Electro-magnetic

fields, Microwave circuits, Antennas and propagation,

RF systems and Wireless Antennas.

Case study Based

Teaching

RF phase shifters/Mixers were taught based on real

time specifications

Inter Disciplinary

approach

Any new Development in antenna is to be in suitably

changing the material used for fabrication. So material

science teachers were requested to educate students on

material characterization.

Guest lecture /

Seminar

One of the pioneering microwave teacher in India

Dr. Bharathi Bhat was invited to deliver a series of

lecture and she also was part of teach the teacher

programme

62

Table 2.10 Continue

Class Room

Management

Few of the concepts were handled in the RF lab in

research centre and live demos on vector network

analyzer was given

Projects Guidance Projects received from Motorola on Propagation

Measurement in villages were carried out with the help

of students

Educational Tour

(IPT / IV)

Students were taken to 3 RF industries in Bangalore

and local base station antenna manufacturer.

Motivation to the

students

Alumni of RF group were made to talk to the present

students

Table 2.11 Influence of SIG on Critical Success Factors of Learning Process

Factors Electronics and Communication Engineering

Learning SIG - Radio Frequency (RF)

Problem based learning EM theory was taught through problem solving

approach

Group, co-operative or

collaborative learning

Poster preparation on the different topics on wireless

Antennas

Hands on/ experimental

Learning and practice

Microwave circuits theory followed by lab

experiments

Invited lectures Lectures by Dr. Koul, IITD and Dr. Raghavan, NIT

Trichy

Learning Style

(Visual, audio etc)

Caricatures on EM theory so as to motivate

Diversity in learners Level of learners were identified and helped

Learner centric

approach

At the end of microwave course, students would be

familiar with smith chart based circuit design

63

2.8.5 Influence of SIG on Research and Development

The influence of SIG on RF, on academic research and sponsored

research in Electronics and Communication Engineering Department were

studied, given in the Table 2.12 and Table 2.13 respectively.

Table 2.12 Influence of SIG on Critical Success Factors on Research and

Development


Academic Research SIG - Radio Frequency (RF)

Research Topics SIG

Based

each members of RF group are assigned research

topics in Antennas, Mixer, Coupler, Filter and

switch

Resources and knowledge

sharing

Knowledge on soft computing, Numerical

methods and test and measurement are shared

among faculty

Interaction with similar

groups

Interaction with RF group in IIT Delhi and NIT

Trichy

Attending and organizing

conferences.Faculty sponsorship for RF conferences

2.8.6 Influence of SIG on Critical Success Factors of Industry Institute

Interaction

The influence of SIG on RF, on Industry Institute Interaction by

Students and Staff in Electronics and Communication Engineering Department

were studied given in the Table 2.14 and 2.15 respectively.

64

Table 2.13 Sponsored Research Factors


Sponsored Research SIG - Radio Frequency (RF)


Each faculty visits to industries like BEL,

Bangalore, DRDL Hyderabad.

Open House

Open house for industries gave a break through to

interact with TVS inter-connect

Proposals

Periodic proposals on Antennas and RF passives

to Department of Defense and Space

Presentations

Research works are periodically presented to

companies like Agilent, Ansoft and Empire

Professional Society

membership

Few faculty members are of IEEE microwave

theory and Techniques society

Alumni Interaction Alumni of RF group working in Indian space

research organization and HCL periodically

respond to us

Table 2.14 Industry Institute Interaction (Students) Factors

Factors Electronics and Communication

Engineering

Training and Placement SIG - Radio Frequency (RF)

Career guidance

RF group students are guided on

possible avenues for placements and

higher studies

In plant Training / Industrial Visits

SIG students placed for training in

Siemens, Astara and Agilent.

Management Skills

SIG students get the feel of

management projects

65

Table 2.14 continue

Table 2.15 Industry Institute Interaction (Staff) Factors


Industry Institute

Interaction (Staff)

SIG - Radio Frequency (RF)


Each faculty visit to industries like BEL,

Bangalore, DRDL Hyderabad.

Open House

Open house for industries gave a break through to

interact with TVS inter-connect

Professional Society

membership

Few faculty members of IEEE microwave theory

and Techniques society

Alumni Interaction

Alumni of RF group working in Indian space

research organization and HCL periodically

respond to us

Software certification and

computer proficiency

SIG members learn RF softwares like ADS,

Ansoft, Empire 3D

Industrial / mini project

Mini projects on RF passives and Antennas on

wireless bands

Communication

SIG seminars during group meeting

Special lecture / courses

(attended) Students attend expert lectures

Intra departmental

participation

Interface with physics department on material

characterization

66

The impact of SIG on RF, on Teaching Learning process was inferred

through the innovative teaching, design capability, product development and

research orientation is shown in Table 2.16.

Table 2.16 Impact of SIG on RF on Teaching and Learning Process

Year

Innovative

Teaching *

Design

Capability *

Product

Development

*

Research

Orientation

*

1999 3 1 0 2

2000 3 2 0 1

2001 4 3 1 1

2002 4 4 1 1

2003 6 6 2 3

2004 8 8 4 4

2005 12 12 5 6

2006 15 14 6 9

2007 18 15 7 12

2008 18 16 8 15

* in Percentile

2.8.7 Impacts

2.8.7.1 Innovative Teaching

The curriculum and syllabi are the platform on which innovative

teaching can evolve. Being an autonomous institution academic freedom was

possible. Such a platform facilitates innovative teaching through Novel

experiments, mini projects supporting theory classes, tutorials and assignments

orienting a student towards creative design and seminars and discussion forum to

defend their work.

67

2.8.7.2 Design Capability

Normally average and above average students are keen to learn

analytically. Their capacity to design will stand in good stead through out their

career. Design based learning of relevant subjects provides a great career.

2.8.7.3 Product Development

Among students, there will be a section that may not be academically

brilliant but have a bent of mind to implement. Product development based

learning will provide both academically as well as not so good students, but with

motivation to implement products, an avenue to innovate and grow.

2.8.7.4 Research Orientation

Among students, there will be always a creamy layer who requires

challenging opportunities. The research orientation helps those set of students to

grow faster.

Thus, as an outcome of all these factors, it has been found that there is

a gradual development in innovative Teaching, Design capacity, Product

Development and research Orientation which is given in percentile during the

year 1999 to 2008.

Table 2.17 Academic Results for ECE Department

Year 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08

ECE 82.93 84.16 86.33 88.37 90.29 92.82 94.66

As an example, the Pass percentage of ECE department during the year

2001-2008 is very good as shown in Table 2.17.

69

The impact of SIG on RF, on Research and Development was inferred

through publications, Ph.D, Collaborative research, projects, internship,

IPR/Pattern, product development and consortium projects and MoUs.

Thus, as an outcome of all these factors, it has been found that there is

a gradual development in Publication Books, Journals and Conference, Ph.D

Degree, Collaborative research, Projects independent, intern ship, IPR/ Patent,

Product, Consortium projects and MoU which is given in percentile from the

year 1999-2008, is shown in Table 2.18.

Table 2.19 Doctorates in the Department of ECE (1998-2008)

As an example the no. of Doctorates of ECE department from the year

2001-2008 is shown in Table 2.19.

The impact of SIG on RF, on Industry Institute Interaction was

inferred through Placement / Higher studies / Competitive examinations/

Entrepreneur, Student internship, Consultancy and Testing, Collaborative

Research, IPR, PDF/ Fellowships, Lab / Equipment / Infrastructure, IPT/ IV,

Internship (Staff) and it is shown the Table 2.20.

Year 1998-2001 2002-2005 2006-2008

Ph.D completed 3 5 8

Pursuing Ph.D 12 30 38

71

From all these factors, it has been found that there is a gradual

development in Placement / Higher studies / Competitive examinations/

Entrepreneurship, Student internship, Consultancy and Testing, Collaborative

Research, IPR, Post Doctoral/ Fellowships, Lab / Equipment / Infrastructure,

Industrial visit, Internship (Staff), which is given in percentile from the year

1999-2008 in Figure 2.5.

Figure 2.5 Department wise Placement (2001-2008)

2.9 SUMMARY

This chapter began with the discussion of Quality Frameworks and

their feature particularly for engineering education and also it discusses the issues

and challenges in each frame work. A novel Special Interest Group (SIG) model

has been proposed to overcome these issues and to achieve Excellence in

Engineering Education. The Three Sub-modules of the proposed model,

questionnaire development, validated instrument and their working have been

briefly discussed with a pilot study. A case study was conducted in the

department of ECE. It is found that there is considerable improvement in all the

three areas such as Teaching and Learning process, Research and Development

and Industry Institute Interaction. The SIG model can justifiably be extended for

various departments which constitutes the focus on next few chapters.

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