015-0135 · Web view015-0135 Critical Thinking and its Role in Effective Problem Solving Dr Pauline...

015-0135

Critical Thinking and its Role in Effective Problem Solving

Dr Pauline Found, Cardiff University, Lean Enterprise Research Centre, Cardiff, CF24 4AY, United Kingdom. Tel: +44(0)29 2064 7022. Email: [email protected]

Lyndon Hughes, ConvaTec Limited, First Avenue, Deeside Industrial Park, Deeside, Flintshire, CH5 2NU, United Kingdom. Tel: +44(0)1244 584017. Email: [email protected]

POMS 21st Annual Conference

Vancouver, Canada

May 7 to May 10, 2010

Abstract

Many organisations striving to become a Lean Enterprise have set their end goal as achieving

perfection. Whilst perceived as unattainable; this fifth and final principle of Lean ensures a

continual drive to improve the way the organisation does business. These organizations

understand that to achieve perfection they need to have a culture of identifying and resolving

problems so they do not re-occur. In the authors’ experience organizations faltering in their

Lean transformation often cite a failure to capitalise on the benefits of problem solving as one

of the primary reasons. It can be argued that there are three key elements to effective problem

solving, one of which is critical thinking and the others motivation and knowledge; this paper

explores the interface between these.

Introduction

In striving to become a Lean Enterprise many organizations pursue the fifth and final

principle of Lean which, according to Womack and Jones (2003) is perfection, thus ensuring

a continual drive to improve the way the organization does business. These organizations

understand that to achieve perfection they need to have a culture of actively seeking and

resolving problems so they do not re-occur, Wilmott (unpub) refers to this as applying the

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mailto:[email protected]

‘100 year fix’. The Toyota motor company is possibly the most well known for applying this

philosophy, however in their book, The Toyota Way Field Book, Liker and Meier (2006)

explain that “calling the process ‘problem solving’ may be a misnomer, since the process

goes well beyond the basics of solving problems”. They explain how the method

encompasses critical and logical thinking processes.

The ability to solve problems is seen as a key driver in a lean enterprise; in this context

attention is turned to critical thinking within a manufacturing environment. Thinking

critically is not a new phenomenon, 2500 years ago Socrates began to develop the principles

of critical thinking when he “established the importance of seeking evidence, closely

examining reasoning and assumptions, analyzing basic concepts, and tracing out

implications not only of what is said but of what is done as well. His method of questioning

is now known as ‘Socratic Questioning’ and is the best known critical thinking strategy. In

his mode of questioning, Socrates highlighted the need in thinking for clarity and logical

consistency” (Paul, 2009a). Critical thinking helps foster a healthy democracy, it is part of

what it means to be a developing person and without it our work places would remain

organized as they were 20 years ago (Brookfield, 1987). The need to develop critical

thinking skills within education has long been agreed, but it has also long been debated and

consensus has not yet been achieved on how to define or assess a student’s ability, which in

some instances has lead to inaction. This inaction results in the development of students who

are ill equipped for today’s complex, fast paced, information laden environment. Sampson et

al., (2007) discuss the findings of the 1991 Departments of Labour report ‘what work requires

of school’ in which they note that “critical thinking skills were reported to be a fundamental

requirement for competing in the contemporary global environment”. There would be a

resounding ‘yes’ from industry leaders to the question, “do first line supervisors in a

manufacturing environment also need these skills?” The likelihood is however that this

demographic of the organization have not had exposure to the concept of critical thinking nor

the encouragement to develop these skills. This is despite the fact that they have to contend

with an ever increasing pace of modern day manufacturing and an almost exponential

increase in the complexity, availability and quantity of information. As a result, the need for

front line manufacturing supervisors to posses the traits and skills of a critical thinker is

paramount if they are to continuously improve their environments. These individuals must be

capable of interpreting the reams of information before them and by applying purposeful and

reflective judgment determine the meaning and significance of what has been observed,

expressed or inferred. They must determine whether there is adequate justification to accept a

conclusion as true and decide on the appropriate course of action.

Whilst business leaders acknowledge the need for this ability, within a manufacturing

supervisory environment the term critical thinking is practically an unknown phrase. It is the

opinion of this researcher that critical thinking has been replaced with the term problem

solving; prior to this research a ‘straw pole’ within this researcher’s organisation drew many

blank faces when supervisors were asked their understanding of critical thinking. This lack of

recognition of terminology does not restrict itself to the shop floor; literature too fails to

adequately cover the concept within a manufacturing environment and focuses almost

exclusively on pedagogy.

Relationship to Existing Literature

In reviewing the literature on critical thinking one thing is apparent at a very early stage; this

is the lack of a single definitive definition of what critical thinking actually is. Each author,

philosopher, psychologist and professor has a slightly different opinion as to the meaning of

critical thinking, what is included within its scope and even whether it should be given the

title critical thinking. With this lack of agreement on the definition of critical thinking comes

an opportunity for confusion in what is a difficult and in-depth subject. This thought is

captured by Boychuk-Duchscher (1999) who believes that “existing literature is confusing,

in it’s description of the process and is ambiguous in drawing relationships between

critical thinking and the language used to illustrate the process…”

Despite academia failing to agree on a single definition of critical thinking and the skills

required to achieve this higher order thinking, there is sufficient agreement within both

business and educational leaders that there is a need to develop the abilities to apply sound

thinking in everyday lives. The benefits to industry and society as a result of critical thinking

are evident and ‘vital’ if we are to continue to develop both culturally and technologically.

This said, and despite the fact that the skills and attributes required to become a critical

thinking person are well documented, there is a definite gap in the development of these skills

within industry. There is little evidence that a critically thinking disposition is either

appreciated or recognized, and in some organisations is feared (Paul and Elder, 2002). This

lack of acknowledgement and industries desire to apply tools and techniques rather than

philosophies (lean implementation is a classic example), it appears that critical thinking has

been somewhat replaced by problem solving. As a result industry teach ‘toolbox’ skills such

as Ishikawa or Process Mapping to the masses with minimal effort required on coaching and

mentoring rather than nurturing critical thinking. This lack of mentoring has resulted in

organisations applying tools sporadically which often results in problems being solved in an

uncritical manner.

Literature is also split on the generalizability of critical thinking and whether or not subject

specific knowledge is required in order to apply critical thinking. Whilst acknowledging in

some instances critical thinking may occur if there is no former knowledge, within a

manufacturing environment some level of knowledge is an essential part in order to apply

sound critical thinking. For this subject specific knowledge to be of benefit organisations

must foster an environment that supports critical thinking and provide the right motivational

drivers for a person to learn and apply the skills of critical thinking. These skills can be

audited through the application of assessments such as the array of Insight Assessment

critical thinking assessments or Watson Glaser’s critical thinking appraisal test, although the

later should be escorted with a caveat as it does not assess a person disposition to use these

skills. From the literature reviewed there is a definite absence of the terminology of critical

thinking including the title itself and it is unclear if this has been purposefully or naturally

transitioned. It is however clear that within industry critical thinking has been replaced with

problem solving. From the literature reviewed this researcher believes there are three key

elements to application of problem solving these are Critical Thinking, Motivation and

Knowledge and therefore it is the objective of this paper to understand how these three

manifest themselves in first line supervisors within a medical devices manufacturer.

Based on the literature reviewed it is apparent that critical thinking at the coal face of

manufacturing has manifested itself into problem solving and from this it can be argued that

there are three key elements to effective problem solving, one of which is critical thinking the

other two are motivation and knowledge. Therefore it can be hypothesized that the following

model can be used to understand an individual’s ability to effectively problem solve by

empirically testing each of the individual components of the hypothesis within a medical

device manufacturing environment.

Critical Thinking

MotivationKnowledge

ProblemSolving

Without critical thinking skills sound thought cannot be applied

to effectively solve problems.

Without knowledge of the situation or subject it is not possible to effectively solve

problems

Unless a person is motivated by the process of solving problems they will not effectively do so.

Figure 1: Three Key Ingredients to Effective Problem Solving.

Research Methods

The purpose of this study was to empirically test first line supervision within a medical

device manufacturer to challenge the hypothesis that effective problem solving is a

combination of sound critical thinking, knowledge and the correct motivational traits (Fig 1).

This hypothesis was tested by measuring and comparing the abilities of first line

manufacturing supervisors to apply the concept of critical thinking and their dispositions

towards the appropriate business attitudes along with their motivational drivers and their

knowledge, both tacit and explicit.

The need to challenge the multiple facets of the hypothesis requires qualitative and

quantitative data to be collated and analyzed. This requirement therefore calls for a mixed

method choice (Saunders et al., 2007) and as this research is time constrained the time

horizon will be Cross Sectional in that it is a ‘snap shot’ of reality at a particular moment in

time rather than a longitudinal process taken over a longer time frame which is outside the

capability of this research period.

This study represents an embedded case study, focusing on the abilities of manufacturing

based supervisors within the researcher’s organization and therefore the sample selection is

100 percent of the population of supervisors. There are 12 supervisors, eight of which are

responsible for manufacturing activities and four engineering. The supervisors predominantly

work a 37 hour, 5 day working week rotating between shifts starting at 6am or 2pm each

week, there are 2 who are exceptions to this rule and work a permanent 37 hour night shift.

As a result if the identification of the need to conduct a mixed method process for data

collection the following instruments were used.

1. SHL Motivation Questionnaire (MQ.M5)

2. Critical Thinking Assessment

o Business Critical Thinking Skills Test (BCTST)

o Business Attitude Inventory (BAI)

3. Knowledge Assessment

o Tacit Knowledge Inventory for Managers (TKIM)

o Explicit Knowledge Assessment (Personal)

o Explicit Knowledge Assessment (Line Manager)

4. Problem Solving Assessment

Critical Thinking

MotivationKnowledge

ProblemSolving

Business Critical Thinking Skills Test & Business Attitude Inventory Test

TKIM Assessment & Explicit Knowledge Assessments

MQ.M5 AssessmentGridlock and Plates Assessments.

o Gridlock Activity

o Plates Activity

The above instruments will provide the data to challenge the hypothesis as shown in Figure 2..

Figure 1: An Illustration of the Assessment Instruments to Test the Hypothesis

Findings

1. Research Results and Analysis for Motivation Assessment (MQ.M5)To obtain data for this portion of the hypothesis the research participants completed the

motivation questionnaire, MQ.M5 which measures motivation against four groupings, these

are; energy and dynamism, synergy, intrinsic and extrinsic. Table 1 provides summaries of

the sten scores for each of these groups and their respective sub categories by participant.

Table1 – Sten Scores by Participant by Category

To understand the sample groups motivational drivers as a whole the average sten scores

were compared to that of the norm group. These are shown in Table 2

Sub Category S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12 Mean SD

E1 Level of Activity 8 6 3 6 5 6 7 10 6 6 10 6 6.6 2.0

E2 Achievement 7 7 5 7 4 4 4 10 6 7 8 7 6.3 1.8

E3 Competition 5 6 5 7 6 8 6 10 6 6 9 7 6.8 1.5

E4 Fear of Failure 7 5 2 4 8 7 7 8 8 5 10 9 6.7 2.3

E5 Power 8 6 7 4 3 5 7 10 6 6 9 6 6.4 2.0

E6 Immersion 4 10 3 5 7 8 9 2 7 10 7 7 6.6 2.6

E7 Commercial Outlook 5 7 6 8 4 4 6 7 7 7 9 6 6.3 1.5

S1 Affiliation 6 7 7 4 5 5 5 9 5 7 6 5 5.9 1.4

S2 Recognition 4 10 10 7 6 5 5 6 3 10 6 4 6.3 2.5

S3 Personal principles 6 4 6 9 4 6 3 6 3 4 4 6 5.1 1.7

S4 Ease and Security 4 4 10 5 3 4 3 8 6 4 6 2 4.9 2.3

S5 Personal Growth 9 7 5 8 4 6 5 9 6 7 9 7 6.8 1.7

I1 Interest 8 4 5 9 4 4 5 6 4 4 6 9 5.7 2.0

I2 Flexibility 4 3 2 4 4 3 6 2 8 3 7 8 4.5 2.2

I3 Autonomy 5 5 10 5 4 1 3 7 7 5 3 8 5.3 2.5

X1 Material Reward 4 4 8 8 3 3 2 8 5 4 6 6 5.1 2.1

X2 Progression 5 3 4 10 3 4 4 10 5 3 10 5 5.5 2.8

X3 Status 4 6 8 7 2 5 3 9 5 6 5 5 5.4 2.0

Intr

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Table 2 – Motivational Sten Scores by Participant

Motivation Optimum for Problem Solving.

To understand how the motivational scores of the sample group impact effective problem

solving, each category has been assessed by the researcher and it has been deemed

appropriate that for each of the sten scores, a score as high as possible would be appropriate

when identifying an effective problem solver. Whilst Baron et al, (2002) highlight that a high

sten score does not mean an individual is more motivated, the objective of this assessment is

to understand at what level the individuals motivational score is in comparison to that of an

Motivation Sub CategoryMean of Sample

Mean of Norm Group Difference SD

E1 Level of Activity 6.6 6.0 0.6 1.98

E2 Achievement 6.3 5.0 1.3 1.83

E3 Competition 6.8 6.0 0.8 1.54

E4 Fear of Failure 6.7 6.0 0.7 2.27

E5 Power 6.4 6.0 0.4 1.98

E6 Immersion 6.6 5.0 1.6 2.61

E7 Commercial Outlook 6.3 6.0 0.3 1.50

S1 Affiliation 5.9 6.0 -0.1 1.38

S2 Recognition 6.3 5.0 1.3 2.46

S3 Personal principles 5.1 6.0 -0.9 1.73

S4 Ease and Securtiy 4.9 5.0 -0.1 2.27

S5 Personal Growth 6.8 6.0 0.8 1.70

I1 Interest 5.7 5.0 0.7 1.97

I2 Flexibility 4.5 6.0 -1.5 2.20

I3 Autonomy 5.3 5.0 0.3 2.45

X1 Material Reward 5.1 5.0 0.1 2.11

X2 Progression 5.5 5.0 0.5 2.81

X3 Status 5.4 6.0 -0.6 1.98

Intr

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Dyn

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effective problem solver. For example, it would be unrealistic to believe that a person who is

demotivated when faced with failure would be an effective problem solver, much learning is

gained from failure and this demotivation will be a barrier to trying alternatives during the

problem solving process. Figure 3 below provides the overall scores of each of the

participants.

Figure 3 – Comparison of Sten Scores and Problem Solving (PS) Optimum

Through comparison of each individual participant’s scores to the maximum available sten

score the individuals with a potential motivational ‘fit’ to problem solving can be determined

this can be shown by reconfiguring the data into Pareto format as shown in Figure 4.

Motivation Category Sten Scores by Participant

0

20

40

60

80

100

120

140

160

Participant

Cum

ulat

ive

Sten

Sco

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Extrinsic 13 13 20 25 8 12 9 27 15 13 21 16

Intrinsic 17 12 17 18 12 8 14 15 19 12 16 25

Synergy 29 32 38 33 22 26 21 38 23 32 31 24

Energy and Dynamism 44 47 31 41 37 42 46 57 46 47 62 48

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12

Figure 4 –Motivational Sten Scores Against Maximum by Participant.

It can be seen that participants S8 and S11 have the highest overall sten score and are

therefore more likely to be motivated by the challenges that problem solving presents. This

however does not mean that participant S5 would not be able to solve problems, it implies

that this individual will show a lesser desire to actively seek and solve problems those

problems.

2. Critical Thinking Assessment

2.1 Research Results and Analysis for Business Critical Thinking Skills Test (BCTST)

The BCTST has been devised and provided by Insight Assessment (IA). IA are part of the

California Academic Press group and was borne from the Delphi report - Critical Thinking: A

statement of expert consensus for purposes of educational assessment and instruction

(Facione, 1990). IA’s focus is on the international advancement of critical thinking and their

pioneering work in the measurement of critical thinking skills and dispositions has developed

the BCTST assessment. The BCTST is a case-based reasoning skills assessment tool which is

specifically designed to evaluate the critical thinking skills of working professionals. The

BCTST provides an objective measure of critical thinking skills applied to business and

Motivation Sten Score - Pareto by Participant

0

20

40

60

80

100

120

140

160

180

200

S8 S11 S4 S12 S3 S2 S10 S1 S9 S7 S6 S5

Participant

Ove

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Sten

Sco

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Sten Score

Max Sten Score

workplace, professional and workplace reasoning contexts. It uses mini-cases and vignettes

drawn from common business and workplace contexts. The online timed (50 minutes)

assessment comprises of 35 multiple choice test items which range in difficulty and

complexity. Questions are presented in business contexts with all specialized information

needed to respond correctly provided within the question. With business-relevant topics and,

in some cases, with data presented in images and diagrams, the BCTST provides items that

broadly represent reasoning ability required to be a skilful critical thinker in the business

professions.

BCTST requires the test taker to accurately interpret specific questions or to draw the correct

inferences from a set of assumptions with the more complex questions requiring an iteration

of these two cognitive skills.

The results from the test are presented in two ways, there is an overall critical thinking score,

this first score has been shown to be a predictor of a person’s success within a work

environment (Facione et al., 2008). The second score is individual rankings against the

critical thinking skills of analysis, evaluation, inference and deductive reasoning. All twelve

of the research participants completed this assessment.

The BCTST assessment produces six individual scores that demonstrate a person’s ability in

each of the five critical thinking skills as discussed by Facione (2007) plus a total critical

thinking score. Through comparison of these individual scores against the 2008 scale norms

for the overall sample of test takers using the BCTST (Facione et al., 2008) and the

descriptions of the category as provided by Facione et al, a judgement can be made on the

critical thinking skill levels of each test taker.

Inductive Reasoning Abilities.

The results obtained for the inductive reasoning portion of the assessment have been

summarised in Figure 5. The mean score was 9.75 with minimum and maximum scores of 5

and 13 respectively and a standard deviation of 2.99.

Figure 5 – Graphical Statistical Summary of Results for BCTST Inductive Reasoning Scores

Using the norm group for inductive reasoning and applying the results from the sample the

following percentile curve can be created from this it can be seen that the inductive ability of

the supervisors range from the 1st to the 45th percentile of the norm group. A person who

scores highly on this scale can be said to be skilled in the interpretation and evaluation of

inductive forms of reason.

Deductive Reasoning Abilities.

The deductive reasoning scores summarised in Figure 6 below provide a mean score of 5.42

with minimum and maximum scores of 1 and 13 respectively and a standard deviation of

3.12.

1815129630

Median

Mean

1211109876

1st Quartile 6.2500Median 11.00003rd Quartile 12.0000Maximum 13.0000

7.8511 11.6489

6.2631 12.0000

2.1171 5.0743

A-Squared 0.72P-Value 0.043Mean 9.7500StDev 2.9886Variance 8.9318Skewness -0.51953Kurtosis -1.53079N 12Minimum 5.0000

Anderson-Darling Normality Test

95% Confidence Interval for Mean

95% Confidence I nterval for Median

95% Confidence I nterval for StDev95% Confidence I ntervals

Summary for Induction (out of 20)

Figure 6 – Histogram of Results for BCTST Deductive Reasoning Scores

Using the norm group for deductive reasoning and applying the results from the sample group

from this it can be seen that the ability of the supervisors to apply deductive reasoning to test

the validity of claims and arguments ranges from the 2nd to the 90th percentile of the norm

group. A person who scores high in this norm group can be expected to show confidence in

their ability to logically determine conclusions based upon arguments where the premises are

known to be or must be taken as true.

Analysis Abilities.

The analysis scores summarised in Figure 7 below provide a mean score of 5.83 with

minimum and maximum scores of 3 and 9 respectively and a standard deviation of 2.25.

14121086420

Median

Mean

876543


3.4358 7.3975

3.0000 7.7369

2.2085 5.2934

A-Squared 0.22P-Value 0.798Mean 5.4167StDev 3.1176Variance 9.7197Skewness 0.484158Kurtosis -0.608819N 12Minimum 1.0000


95% Confidence I nterval for Mean

95% Confidence Interval for Median

95% Confidence Interval for StDev95% Confidence Intervals

Summary for Deduction (out of 15)

Figure 7 – Histogram of Results for BCTST Deductive Reasoning Scores.

The analysis scale measures the skills of the individual to comprehend and express the

meaning and significance of a wide variety of situations, experiences, data, events,

judgements, beliefs, rules or criteria.

Inference Abilities.

The analysis scores summarised in Figure 8 below provide a mean score of 5.42 with


9876543

Median

Mean

87654


4.4040 7.2626

4.0000 8.0000

1.5936 3.8195






Summary for Analysis (Out of 10)

14121086420

Median

Mean

76543


3.9714 6.8619

3.2631 6.7369

1.6114 3.8622

A-Squared 0.58P-Value 0.106Mean 5.4167StDev 2.2747Variance 5.1742Skewness 0.936009Kurtosis 0.198698N 12Minimum 3.0000





Summary for Inference (Out of 15)

Figure 8– Histogram of Results for BCTST Inference Scores

Using the norm group percentiles for inference, which is the ability to identify and consider

relevant information to draw reasonable conclusions and hypotheses the results show that the

supervisory sample group ranged from the 10th to the 87th percentile of the norm group.

Evaluation Abilities.

The evaluation scores summarised in Figure 9 below provide a mean score of 3.92 with


Figure 9 – Histogram of Results for BCTST Evaluation Scores

Using the norm group for evaluation and applying the results from the sample the following

percentile curve can be created, this indicates that the supervisors ability to evaluate

information to aid their decision making process ranges from the 1st to the 65th percentile of

the norm group.

14121086420

Median

Mean

5.04.54.03.53.02.52.0


2.8865 4.9468

2.2631 5.0000

1.1486 2.7529

A-Squared 0.38P-Value 0.342Mean 3.9167StDev 1.6214Variance 2.6288Skewness -0.454667Kurtosis -0.798299N 12Minimum 1.0000





Summary for Evaluation (Out of 15)

BCTST Total Scores.

The total score is the best measure of critical thinking skills and is ideal to compare

individuals and identify those that think at a higher level. The summary in Figure 10 below

shows a mean score of 15.17 with minimum and maximum scores of 8 and 23 respectively

and a standard deviation of 5.31.

Figure 10 – Histogram of Results for BCTST Total Scores

3024181260

Median

Mean

201816141210


11.796 18.538

9.263 20.211

3.759 9.009





95% Confidence Interval for StDev95% Confidence I ntervals

Summary for Total (out of 35)

Facione et al, provide recommended groupings of score ranges in which they suggest provide

an indication of ‘like minded’ individuals. This is shown in Figure 11.

Figure 11 – Distribution of Total BCTST Results as Recommended by Facione et al.

Using the national scale norm as a comparison, Figure 12 shows the percentile results ranging

from the 1st to the 75th percentile.

% Distribution of Total BCTST Scores

0%

0%

25%

8%

25%

17%

25%

0%

0%

0%

0-2

3-6

7-9

10-13

14-16

17-20

21-23

24-27

28-30

31-34

Scor

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artic

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% of Test Takers 0%0%25%8%25%17%25%0%0%0%

0-23-67-910-1314-1617-2021-2324-2728-3031-34

Figure 12– Percentile Curve for BCTST Total Scores

Table 3 below shows the percentage scores for each of the participants with the overall

percentage based on a potential score of 110.

Table 3 – BCTST Assessment Score by Participants

Percentile Curve for Total Score Using 2008 BCTST National Scale Norm Sample Group

0

10

20

30

40

50

60

70

80

90

100

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34

Total Score

Perc

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0

1

2

3

4

5

6

7

8

9

10

Num

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per

Pe

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Poin

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Norm Group Percentile

Sample Percentiles

Number of Samples at Percentile Point

Participant ID

Induction (out of 20)

Deduction (out of 15)

Analysis (Out of 10)

Inference (Out of 15)

Evaluation (Out of 15)

Total (out of 35)

S1 60% 27% 50% 33% 40% 46%

S2 30% 20% 40% 20% 13% 26%

S3 55% 47% 70% 40% 33% 51%

S4 60% 67% 80% 60% 33% 63%

S5 65% 13% 50% 27% 40% 43%

S6 25% 33% 40% 20% 20% 29%

S7 30% 20% 30% 33% 7% 26%

S8 45% 33% 50% 33% 27% 40%

S9 35% 7% 30% 20% 13% 23%

S10 65% 53% 90% 47% 33% 60%

S11 60% 73% 90% 67% 27% 66%

S12 55% 40% 80% 33% 27% 49%Average of

group 49% 36% 58% 36% 26% 43%

Considering the statement by Facione et al (2008), that total critical thinking scores can be

predictors of success within the workplace, it can be assumed that in the assessment of

whether the sample group have the ability to effectively problem solve, supervisors S11, S4

and S10 should have a distinct advantage over the other candidates assuming the research

hypothesis is valid. This will be discussed later in this paper.

2.2 Research Results and Analysis for Business Attitude Inventory Assessment (BAI)

The Business Attitude Inventory is part of the suite of tests offered by IA and focuses on the

attitudes and dispositions toward using thinking. This tool focuses on an array of attitudes

and values that influence a person's capacity to learn and to effectively apply critical thinking

skills. Critical thinking disposition and skills go hand in hand: the "'willing and able"

(Facione, 2009) of human reasoning. As with the BCTST, BAI is an online assessment,

however the questions are not timed nor are they multiple-choice. The responses for this

assessment are scored against the desired response for the question posed.

The BAI assessment produces a score for an individual’s critical thinking style plus eight

other attributes required for key employees. These additional eight are dependability,

commitment, honesty, desire to work, willingness to learn, flexibility, sociability and

tolerance. According to Facione et al, (2008) there are three types of trait for each of the BAI

attitudes. Positive traits demonstrate that individuals have a desirable tendency towards the

particular attitude. Negative traits mean hostility is shown towards the attitude and

ambivalence suggests an inconsistency in their expression towards the attitude. Table 4

(below) shows the scores for each participant against the individual attributes and also the

average of the total scores. Scores between 30 and 40 are deemed as positive, between 21 and

29 are ambivalent and between 10 and 20 are hostile towards the attribute.

Table 4: Individual BAI Assessment Results

Of the 12 participants, none exhibited negativity towards any of the business attitudes or the

critical thinking style however ambivalence was the overriding trait with only willingness to

learn demonstrating a strong positive attitude from the group with 92%. Whilst this data is of

concern there is an opportunity that must be capitalised upon as they are all open to learning,

and to quote Thomas R. Dewar, “minds are like parachutes, they only work when they are

open”.

3. Research Results and Analysis for Knowledge Assessment

3.1 Tacit Knowledge Assessment Results.

There are two forms of knowledge that are focussed upon in the field of knowledge

management, tacit and explicit (Sanchez, 2004). Table 5 below demonstrates the differences

between these two forms of knowledge and how they are managed.

S1 30.91 29.09 30.91 23.64 34.00 27.50 34.55 30.00 31.67 30.25

S2 30.00 28.18 27.27 25.45 36.00 25.83 30.00 26.36 30.00 28.79

S3 29.09 30.00 31.82 24.55 32.00 29.17 27.27 30.91 30.83 29.52

S4 30.00 25.45 26.36 31.82 32.00 27.50 30.91 24.55 25.00 28.18

S5 29.09 24.55 27.27 26.36 29.00 24.17 30.00 26.36 26.67 27.05

S6 28.18 32.73 24.55 28.18 33.00 26.67 30.00 28.18 28.33 28.87

S7 28.18 32.73 26.36 29.09 34.00 29.17 31.82 33.64 30.83 30.65

S8 36.36 30.00 30.91 30.00 35.00 31.67 33.64 30.91 28.33 31.87

S9 30.91 28.18 25.45 32.73 32.00 24.17 28.18 23.64 25.00 27.81

S10 29.09 28.18 29.09 26.36 33.00 29.17 30.00 30.00 32.50 29.71

S11 30.91 30.91 30.91 26.36 35.00 30.00 33.64 29.09 32.50 31.04

S12 29.09 26.36 26.36 25.45 34.00 27.50 28.18 31.82 30.00 28.75Average Score 30.15 28.86 28.11 27.50 33.25 27.71 30.68 28.79 29.31

Tacit Knowledge Explicit KnowledgeKnowledge is personal in nature and very difficult to extract from people.Knowledge must be transferred by moving people within or between organisations.Learning time must be encouraged by bringing the right people together under the right circumstances.

Knowledge can be articulated and codified to create explicit knowledge assets.Knowledge can be disseminated (using information technologies) in the form of document drawings, best practices etc.Learning can be designed to remedy knowledge deficiencies through structured, managed, scientific processes.

Table 5.: Source: Adapted from (Sanchez, 2004)

To measure these differing forms of knowledge within the sample group, two knowledge

assessments were administered.

Tacit Knowledge

HEC School of Management, Paris, conducted a tacit management project to benchmark the

management and selling skills characteristics of high mobility managers, engineers, and

salespeople (Segalla et al., 2009), they believe that if managers have a better level of tacit

knowledge about how to get things done then their companies will prosper. An outcome of

this research project is the availability of an on-line tacit knowledge assessment tool to allow

individuals to benchmark themselves against the sample used in the research. The Tacit

Knowledge for Managers’ (TKIM) assessment has been adapted from the original version

created by Sternberg and Wagner at Yale University.

The TKIM Project enables respondents to be compared to an expert panel of over 70

European C-level executives selected by Boyden, a leading executive search consultancy, and

over 2,000 managers and business students from around the world.

All twelve of the research participants completed this assessment.

Table 6 – Summary of TKIM scores

For this particular measure the total TKIM score is the indicator of a persons tacit knowledge,

Figure 13 provides a Pareto of the total TKIM scores by participant, the Pareto is grouped in

ascending order as the lower the total TKIM score the higher the level of tacit knowledge.

Figure 13 – Pareto of TKIM Total Scores for Each Participant

Participant Managing

Yourself ScoreManaging

Others ScoreManaging Tasks Score

Total TKIM Score

S1 27 40 31 98S2 45 52 60 157S3 28 35 26 89S4 33 52 29 114S5 34 31 29 94S6 46 47 48 141S7 55 57 72 184S8 39 66 67 172S9 48 51 40 139S10 39 39 47 125S11 62 68 71 201S12 50 41 38 129

Minimum 27.0 31.0 26.0 89.0Mean 42.2 48.3 46.5 136.9

Median 42.0 49.0 43.5 134.0Maximum 62.0 68.0 72.0 201.0

SD 10.8 11.7 17.2 36.1n 12.0 12.0 12.0 12.0

Total TKIM Pareto

0

50

100

150

200

250

S3 S5 S1 S4 S10 S12 S9 S6 S2 S8 S7 S11

Participant

Tota

l TK

IM S

core

Total TKIM ScoreAverage TKIM Score

The lower the TKIM score the higher the level of tacit knowledge.

The results for the TKIM assessment show a wide ranging level of tacit knowledge; this has

resulted in a standard deviation (SD) for the total score of 36.1. Participants S3, S5 and S1

show the strongest performance whilst candidates S7 and S11 showed very little tacit

knowledge ability.

Explicit Knowledge Assessment Results.

Understanding the required skills and knowledge within an organisation is an essential part of

knowledge management and a key factor to challenging the hypothesis of this research,

observing knowledge however is not possible (Hunt, 2003). Hunt explains the need for a

form of test in order to assess a person’s knowledge, and to this the sample group were asked

to complete a knowledge self assessment questionnaire. The questionnaire was developed

from discussions with the sample groups, their line leaders as well as department heads. Hunt

also explains that measurement is difficult as often people are unaware of what they should

know and therefore cannot state their true knowledge. Likewise they may well have been

misinformed, this results in their belief of what the ‘knowledge’ is being incorrect In an

attempt to overcome this each participant’s line manager were also asked to complete the

questionnaire to enable a comparison between perceptions of knowledge levels.

All twelve of the research participants completed this assessment.

A summary of how the individuals scored their own individual levels of explicit knowledge

and also how their line managers scored them against the same criteria is shown in Figure 14

Table 7 below summarises the overall individual and line manager’s responses for each

category. Presenting this data further into each individual participant and their line manager’s

responses, Figure 41 shows how there is a definite shift in the scoring. In every instance the

individual’s perceived personal knowledge was greater than their line manager’s perception

of that person’s knowledge.

Table 7 – Explicit Knowledge Category Summary

Figure 14– Explicit Knowledge Results – Individual and Line Manager Comparison

Category Scores Min ScoreAverage

ScoreMax

Score Min ScoreAverage

ScoreMax

Score

Role Specific 6.2 8.3 10.0 5.0 6.6 9.0

Site EHS 7.3 8.9 10.0 4.8 7.4 10.0

Product Knowledge 6.0 8.0 9.3 4.3 6.8 9.0

Quality, Regulatory 6.9 8.2 10.0 4.5 6.1 7.1

Accounting 1.0 4.3 9.0 1.0 4.3 7.0

Project management 6.3 7.9 10.0 3.7 6.7 10.0

Use of data 4.3 7.6 10.0 3.3 6.8 10.0

Information technology 4.0 8.1 10.0 3.7 6.6 8.3

Customer Service 6.7 8.3 10.0 5.5 6.4 7.7

Leadership and Management 7.6 8.5 10.0 4.1 5.9 7.0

Overal Score 1.0 7.8 10.0 1.0 6.4 10.0

Individual Scores Line Manager Scores

Explicit Knowledge Score Comparison - Individual and Line Manager Assessment

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

S1 S2 S3 S4 S5 S6 S7 S8 S9 S10 S11 S12

Participant

Expl

icit

Kno

wle

dge

Scor

e

Individual AssessmentLine Manager Assessment

Despite the difference in scores there is a high level of correlation (0.7) between those of the

individual and those of the line manager. Therefore for the purpose of this research and to

compare explicit knowledge levels of each individual it can be assumed that the mean of the

two scores can be used as the explicit knowledge value. Therefore Table 8 presents these

scores for each participant, it is important to note that participant S3 is relatively new to the

role and therefore their perceived knowledge and the knowledge of their line manager are

lower than maybe expected.

Table 8 – Explicit Knowledge Comparison and Final Adjusted Scores.

A summary of the explicit knowledge levels is shown in Figure 15. The mean explicit

knowledge score is 70.78 and the standard deviation of the sample group is 7.5.

Participant ID Individual ScoreLine Managers

ScoresFinal Explicit

Score

S1 88.2 72.3 80.3

S2 67.6 56.6 62.1

S3 66.3 51.3 58.8

S4 72.9 71.0 72.0

S5 74.3 58.8 66.6

S6 74.5 65.1 69.8

S7 80.9 70.6 75.8

S8 90.9 74.6 82.8

S9 77.9 70.6 74.3

S10 88.8 65.5 77.2

S11 75.9 57.0 66.5

S12 76.9 50.2 63.6

Figure 15 – Summary of Adjusted Explicit Knowledge Scores.

Overall Knowledge Assessment Results

To establish the overall knowledge ranking of each participant the two scores were combined.

Figure 16 – Pareto of Overall Knowledge Scores.

858075706560

Median

Mean

77.575.072.570.067.565.0


66.008 75.551

64.313 76.782

5.320 12.751





95% Confidence Interval for StDev95% Confidence I ntervals

Summary for Final Explicit Score

Overall Knowledge Score by Participant

0.0

50.0

100.0

150.0

200.0

250.0

S1 S5 S3 S4 S10 S9 S12 S6 S8 S2 S7 S11

Paticipant

Know

ledg

e Sc

ore

Lower scores are desirable for this assessment

4. Research Results and Analysis for Problem Solving Assessment

To establish the problem solving ability, measurement was conducted with the application of

two practical activities. The resulting scores provide a ranking in terms of problem solving

ability

The first of the problem solving activities is known as Gridlock, this challenge is usually used

in a team context in which the team is observed in their application of thought, knowledge,

collaboration and leadership whilst they create the image presented on the instruction sheet.

This activity has been adapted for the purpose of this research in that it is an individual

activity and through timing and observation the researcher can assess how well the

individuals apply themselves to the issue.

The second activity, developed as part of the apprentice and technical operator recruitment

process at the case study organization, known as Plates, assesses how individuals interpret

instruction and conduct themselves to complete a task within a given timescale.

4.1 Gridlock Activity Results

All 12 sample members attempted the activity, of which only two completed it within the

time allocation of 40 minutes. Within the group of the ten who failed to complete only one

did not utilise their full time allocation and ‘gave up’ relatively quickly once they had

reached a point of ‘impasse’ despite having 22 minutes remaining.

During the observations, two distinct categories of person became apparent in the ‘did not

finish’ group. There were those who were unable to progress the activity (6 participants) and

were multiple planks from completion and those who managed to assemble the planks (4

participants) with the exception of the final plank which was unable to be fitted, they then

utilised their remaining time to attempt to resolve this error. The error is as a result of the

orientation of plank 13 when initially assembled. Of the two individuals that completed the

activity they too witnessed this final plank issue but managed to find a resolution relatively

quickly.

Of those who completed the activity the average completion time was 19 minutes. And of

those who failed to complete the activity but had reached a point where this was to just the

last plank the average time was 21 minutes.

The performance of each of the individuals is shown in Table 9, a Pareto of the total scores

are shown in Figure 17.

Table 9 – Gridlock Activity Performance Scores and timings by Participant

Participant No.

Time Taken

(Minutes)Completed

Task

Assembled Except One

Piece.Time to Just

1 PieceAssessment

Mark

Bonus for Getting to Final Piece

Penalty for Not using

Time Allowed

Bonus for Completing (1 point per min under

time) ScoreS1 40 N Y 36 25 5 30S2 40 N 9 9S3 40 N 27 5 32S4 16 Y 14 23 5 24 52S5 40 N 14 14S6 40 N 16 16S7 40 N 24 24S8 28 - DNF N Y 14 22 5 -3 24S9 40 N 21 21S10 22 Y 14 26 5 18 49S11 40 N Y 18 23 5 28S12 40 N Y 18 22 5 27

Minimum 16 9 9.0Mean 36.2 21 27.2

Maximum 40 27 52.0

Figure 17– Gridlock Activity Performance Score by Participant (Pareto)

In considering Figure 17, participants S4 and S10 stand out as high performing in this activity

and this is rightly so as they were the only two individuals to complete the activity. However,

if the breakdown of the scores is analysed further, this shows that the successful completion

has a large influence on the scores. Figure 18 shows the overall data but with the added

detail of the score allocations, this Pareto has been sorted on the assessment and bonus for

getting to the final piece. Here it can be seen that participants S4 and S10 are no longer

leading the field and the impact of the ability to resolve the issue of the incorrect final piece

becomes much more apparent. S4 and S10 however both resolved the issue of the final piece

in which the others failed and therefore the overall scores, including the finish time bonus

should remain the measure.

Pareto of Total Score for Gridlock Activity

0

10

20

30

40

50

60

S4 S10 S3 S1 S11 S12 S7 S8 S9 S6 S5 S2

Participant

Poin

ts A

war

ded

Score

Figure 18 – Gridlock Activity Performance Score breakdown.

4.2 Plates Activity Results

Of the 12 participants to attempt this activity three failed to complete it, S1 took the full 20

minute allocation, he had assembled plate 2 incorrectly and could not resolve this.

Participants S6 and S2 stopped their test at 16 and 18 minutes respectively as a result of being

unable to follow the instructions or understand the task. Of those who completed the activity

the average completion time was 14 minutes. The performance of each of the individuals is

shown in Table 10, a Pareto of the total scores are shown in Figure 19.

Breakdown of Points Awarded to Participants - Gridlock Activity

-10

-5

0

5

10

15

20

25

30

35

40

45

50

55

S3 S10 S1 S4 S11 S12 S8 S7 S9 S6 S5 S2

Participant

Num

ber o

f Poi

nts

Bonus for Completing (1 point per min under time)Penalty for Not using Time AllowedBonus for Getting to Final PieceAssessment Mark

Table 10 – Plates Activity Performance Scores and Timings by Participant

Figure 19 – Plates Activity Performance Score by Participant (Pareto)

Participant No.Time Taken (Minutes)

Completed Task

Assessment Mark

Bonus for Completing (1 point per min under

time) ScoreS1 20 -DNF N 24 24S2 18 - DNF N 15 15S3 17 Y 19 3 22S4 10 Y 28 10 38S5 18 Y 21 2 23S6 16 - DNF N 10 10S7 9 Y 35 11 46S8 11 Y 31 9 40S9 15 Y 26 5 31

S10 15 Y 25 5 30S11 18 Y 22 2 24S12 14 Y 20 6 26

Minimum to Complete 9 10 2.0 10.0

Mean to Complete 14.1 23.0 5.9 27.4

Maximum to Complete 18 35 11.0 46.0

Pareto of Total Score for Plates Activity

0

10

20

30

40

50

60

70

S7 S8 S4 S9 S10 S12 S11 S3 S5 S1 S2 S6

Participant

Poin

ts A

war

ded

Score

Gridlock and Plates Activity Combined Scores

To understand the overall ability of the participants to effectively solve problems the two

scores have been combined. Figure 20 below provides a Pareto summary of each participant’s

problem solving ability. From this it can be seen that S4, S10 and S7 are the strongest overall

participants whilst S2 and S6 performed poorly in both activities. The overall correlation of

performance in the two activities is 0.43, this value falls into the region of a medium

correlation and therefore it is not conclusive that ability in the plates activity guarantees

success in the gridlock activity. It does however provide a predictor for those who may be

stronger in testing the hypothesis.

Figure 20 – Combined Problem Solving Score by Participant

Pareto of Combined Scores for Plates and Gridlock Activities by Participant

0

10

20

30

40

50

60

70

80

90

100

S4 S10 S7 S8 S1 S3 S9 S12 S11 S5 S6 S2

Participant

Com

bine

d Pr

oble

m S

olvi

ng S

core

Gridlock ScorePlates Score

Conclusions

The problem solving assessment results can be used along with the motivational assessment,

knowledge assessment and critical thinking assessment to establish a correlation. To enable

the comparison, the participant’s scores for each individual assessment have been ranked in

order of their position compared to the rest of the sample (Table 11). The overall assessment

and problem solving rankings are represented graphically in Figure 21.

Table 11 – Rankings for Each Assessment by Participant

ParticipantMotivation

RankingKnowledge

Ranking

Critcal Thinking Ranking

Overall Assessment

Ranking

Problem Solving Ranking

S1 8 1 5 3 5

S2 6 10 11 9 11

S3 5 3 4 2 5

S4 3 4 3 1 1

S5 12 2 8 8 9

S6 11 8 10 11 10

S7 10 11 9 12 3

S8 1 9 6 6 4

S9 9 6 12 10 7

S10 7 5 2 4 2

S11 2 12 1 5 8

S12 4 7 7 7 7

Figure 21 – Comparison of Participant Assessment Rankings

These rankings provide a platform to establish a correlation between the overall performance

in the assessments and that of the problem solving tasks.

Utilising the Pearson correlation calculation function within Minitab the correlation between

a participants overall ranking in the sample group for their motivation, knowledge and critical

thinking assessments and their overall ranking for the problems solving activities is:

Correlation (r) = 0.52

To understand whether this is significant, reference to the table of critical values for

Pearson’s r is required utilising the following criteria:

Test Type: One Tailed Test

Level of Significance (): 0.05

Comparison of Overall Assessment Rankings and Problem Solving Rankings

0 2 4 6 8 10 12 14

S1

S2

S3

S4

S5

S6

S7

S8

S9

S10

S11

S12

Parti

cipa

nt

Ranking

Overall Assessment Ranking Problem Solving Ranking

Degrees of Freedom (df): N-2, (N = 12) = 12-2 = 10

Therefore the critical value = 0.497

With the correlation value of 0.52, which is deemed a medium level of correlation in the

broad environment, and comparison to the critical value of 0.497 it can be seen that the

correlation between the overall ranking for the combined assessments and that of problem

solving is marginally significant.

At this point it is now appropriate to compare the individual assessments and the correlation

to the problems solving results. Table 12 below shows the correlation matrix of the

knowledge, motivation and critical thinking assessments to that of the problem solving

assessments.

Table 12 – Correlation Matrix of Individual Assessments to Problem Solving

Focusing purely on correlation and significance with problem solving and using the same

criteria as above, the correlation between critical thinking and problem solving is the only

combination of individual tasks that returns an r value (0.55) greater than that of the critical

value. However opening the comparisons wider, motivation and critical thinking also yield a

marginally significant correlation of 0.57.

Motivation Ranking

Knowledge Ranking

Critcal Thinking Ranking

Problem Solving

Overall Assessment

Ranking

Motivation Ranking 1.00

Knowledge Ranking -0.28 1.00

Critcal Thinking Ranking 0.57 0.17 1.00

Problem Solving 0.31 0.24 0.55 1.00 0.52

Knowledge20% Weighting

Motivation30% Weighting

Critical Thinking50% Weighting

ProblemSolving

By ranking each of the participants following their performance in the three individual

assessments and also in the problem solving activities, correlations were evaluated to

establish if the hypothesis discussed is valid. Through correlation of the overall assessment

rankings and the problem solving ranking a correlation value of 0.52 can be deemed as

significant when applying a confidence factor of 95%. It can therefore be concluded that the

combination of motivation, knowledge and critical thinking has a direct significance on the

ability of an individual to effectively solve problems.

Further individual correlations demonstrate that the strongest contributor to this correlation is

that of critical thinking, followed by motivation and finally knowledge. This would support

the generalizability of critical thinking and suggests these skills are generalizable. Taking this

into consideration the weighting of the model would shift and may best be represented by a

weighted Venn diagram such as:

Table 22 – The weighted model to Problem Solving

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