Download - Cognitive Affective State (CAS) is an ecological component of executive function

Cognitive- Affective State (CAS) Rank

is a Measure of an Ecological Component

of Executive Function

Centre for Human Ecology Theory

Simon P Walker

2014

Abstract

Measurements using one model of Cognitive-Affective State Rank (CAS) in yr 10 students in the UK

have been shown to correlate with measures of general intelligence (g) and academic performance

(predicted GCSE grades).

This study looked at the claim that, within this model, CAS Rank should be considered as an

ecological component of Executive Function rather than a non-ecological, or intrinsic component. An

alternative non-ecological hypothesis was proposed which would predict that variance in CAS scores

across independent populations of learners would not be significantly different. This would

demonstrate that CAS was a non-ecological measure.

This prediction was tested using data of CAS Footprints assessments from four cohorts of yr

10 students in the UK, across four different, independent learning environments. The data was

collected between December 2012 and December 2013 at four UK secondary schools. 496 Yr 10

students took part and more than 2,500 subject lesson assessments were made.

The alternative non-ecological hypothesis was not supported. The variance in CAS scores

varied across the four schools. Two schools showed low variance and two much higher variances both

within five factors in this model of CAS and between them. The low variance schools showed a non-

significant correlation with the optimal CAS model which predicts that academically successful

students will show a pattern of CAS scores that match a theoretical optimal pattern. By contrast, the

higher variance schools showed a significant correlation with the optimal CAS model. I conclude that

in low variance schools a cultural suppression factor is restricting the range of CAS scores amongst

students, imposing a school wide set of scores which accounts for the low variance.

I conclude that this study supports the claim that CAS is an ecological component of

Executive Function rather than a non-ecological component. I suggest directions in which future

research may go toward testing metacognitive interventions and understanding the role of the

imagination in CAS.

Introduction: An ecological view of cognitive ability

Executive Function (CAS) and CAS Rank (CAS)

Intrinsic models of learning emphasise the intrinsic cognitive ability of the learner. Traditional IQ

models, for example, assume that a learner’s cognitive ability can be measured in abstract and the

resulting score will hold good for the learner whatever the context he is learning in. Some authors

question this assumption on cultural grounds (Berry 1993; Barber 2005),whilst many others have

Centre for Human Ecology Theory 2

increasingly sought to understand cognition through a construct that can take into account ecological

context and situational and novel problems.

The construct of executive function (EF) has been proposed to describe the relationship

between g and the cognitive ability required to deal with in situ problems when handling novel

situations (Banich 2009). EF is a generalised construct that sits over a range of elements that relate to

the effective handling of novel situations (Miyake et al. 2000). These include events that involve

planning or decision making; that involve error correction or troubleshooting; situations where

responses are not well-rehearsed or contain novel sequences of actions; dangerous or technically

difficult situations; situations that require the overcoming of a strong habitual response or resisting

temptation. Growing evidence supports the conceptualisation that EF is a neurologically overlapping

but distinct construct from g (Barbey et al. 2012; Brydges et al. 2012; Blair 2006; Bull, Scerif 2001;

Burgess et al. 2006; Carlson et al. 2002; Crinella, Yu 1999; Lehto et al. 2003; Friedman et al. 2006;

Gray et al. 2003; Blair 2006; Conway et al. 2003).

Miyake and Friedman’s theory of EF proposes that there are three aspects of executive

function: updating, inhibition, and shifting (Miyake and Friedman 2000), each of which relates to the

capacity to adapt one’s cognition to the task in hand. Updating is defined as the continuous

monitoring and quick addition or deletion of contents within one’s working memory. Inhibition is

one’s capacity to supersede responses that are prepotent in a given situation. Shifting is one’s

cognitive flexibility to switch between different tasks or mental states.

CAS- an ecological component of EF?

Walker’s Human Ecology model proposes seven factors that may be considered to influence the

dynamic cognitive- affective state (CAS) that the learner adopts in their varied learning environments

(Walker 2014). CAS, as proposed by Walker, is an ecological function of cognitive-affective

components of metacognition (2014). CAS represents the internal-external cognitive/affective

interaction between the learner and their environment. A learner’s CAS is a measure of their ability to

alter their ‘state’, in terms of these seven factors, when engaging across a range of learning

environments.

Walker proposes a model of seven factors which contribute to CAS and may be measured

using the Footprints technology. Factor analysis shows that CAS, as measured through the Footprints

technology against the seven factors, explains a proportion of variance of grade prediction that cannot

be explained by CAT scores (Walker 2014). Walker also shows that a model of optimal CAS

correlates with a measure of general intelligence, g, as measured by the CAT2 test.

A schema can describe the postulated relationship between CAS and g and the internal

learning processes of the learner (i.e. their cognitive-affective state) and the external learning

environment (i.e. the context in which the learner is engaging in the subject) (Figure One.).

Figure One. Walker’s postulated schema for the relationship between g, EF and CAS in guiding

the cognitive-affective engagement of the learner to their environment

EF g

Specific external

environment of learner

Sensory

data

Modified state of CAS in

response to sensory data


One way to test Walker’s ecological conceptualisation of CAS is to see whether CAS is independent

from the wider environment. If CAS is, in fact, a measure of the intrinsic cognitive-affective state of

the learner that is independent of the wider environment that the learner is engaged in, then CAS will

not be context dependent.

If, for example, one assessed CAS in randomized controlled cohorts of students studying in a

range of independent learning environments one would not expect significant difference in CAS

between those learning environments.

Environment A Environment B Environment C

Independent learning environments

Figure Two. If CAS was a non-ecological function of cognitive-affective condition then across

three independent learning environments (A, B and C) one would not expect to find significant

differences in the variances of CAS in cohorts of learners.

Method

Some authors have criticised current assessments used to measure executive function (EF). Burgess

argues that current EF tests are non-ecological in that they test for atomised characteristics in abstract

rather than in situ. Ecological tests would take account of the environmental context of the

behaviours, a recognition of the fact that EF is a construct which refers to agency in situ, the ability to

respond to novel situations. They would also accept that psychological dimensions such as affective

state, social role and personality should be incorporated into measures of EF.

The CAS assessment technology used in this study was developed on the basis of Human

Ecology Theory. It provides an ecological approach to testing some aspects of executive function

through mental simulation. The Footprints CAS assessment required candidates to complete an online

computer-based imagination exercise. The exercise involve a series of verbal instructions, listened to

through headphones, which invite the candidate to imagine a space in their own imagination. See

appendix for further explanation. The instructions enable the candidate to form the dimensions, shape,

features and activities of a space they imagine in their mind. Having created their space, the candidate

is then invited to score a set of multiple choice statements about their space. These answers give a

baseline score of the candidate’s actual imagined cognitive-affective state.

Having established the individual’s baseline scores for imagined cognitive-affective state, the

Footprints CAS assessment instrument then leads the candidate through three sets of further enquiries

about their space. Specifically, the candidate is invited to imagine, in turn, a particular learning

context taking place within their space; for example, their maths lesson, or their science lesson.

The chosen learning context is one which the candidate experiences in reality within school.

For example, if they are in maths set one in school, then in the Footprints CAS imagination exercise,

they imagine maths set one as the learning context within their space. The candidate is cued up by


verbal cues to imagine how their space might be changed by each of the learning context taking place

within their space and how their activity might change.

The candidate then scores a comparative set of statements to the first baseline statement

which identifies their scores in relation to imagined cognitive-affective state when participating in

each learning context in their imagined space. By this method, the Footprints CAS assessments obtain

four comparative sets of data about each candidate; their imagined cognitive-affective state as baseline

and then their imagined cognitive-affective state when participating in three specific learning contexts.

CAS is the measured adjustment of a student’s imagined cognitive-affective state when

participating across the three learning contexts.

Four cohorts of a total of 496 yr 10 students from four different and independent schools

undertook the Footprints CAS assessments as a measure of CAS. Students in this study undertook

CAS assessments within the context of an ICT session. They imagined three learning contexts: Maths,

Chemistry and English.

Data Model

The data model used in the study is composed of seven factors or elements involved in a model of

cognition proposed by Walker 2009.

This model of CAS proposes seven factors which are components of a student’s situated cognition.

The seven factors of data collected for each student are:

1. Trust of my self- how much I trust my own ideas, qualities and opinions in this lesson

2. Trust of other’s- how much I trust other’s ideas, qualities and opinions in this lesson

3. Pace- how much pace, risk and change do I like in this lesson

4. Disclosure- how willing am I share to share thoughts, ideas, opinions and questions in this

lesson

5. Perspective- whether I see things from a detached or personal perspective in this lesson

6. Processing- whether I focus on making connections or following step by step in this lesson

7. Planning- whether I focus on the learning outcome or am open ended in this lesson

CAS Rank is used as a measure of Optimal CAS

Factors 1, 2, 5, 6 and 7 compose Walker’s CAS Rank. CAS Rank is used as a measure of optimal

CAS. It ranks a student’s CAS scores for these five factors when engaged in Maths, Science and

English by comparing the student scores to the optimal scores in Walker’s ideal imagined cognitive

operation model (Walker 2013).

A high student CAS Rank represents a high optimal CAS (an accurate adjustment of cognitive-

affective state to the learning environment); a low student CAS represents a low optimal CAS (an

inaccurate adjustment of cognitive-affective state to the learning environment).


Results

Analysis 1 Analysing the variance between CAS factors and within CAS factors.

The variance in CAS scores in Maths, Science and English for factors 1,25,6 and 7 across yr 10

cohorts was measured in all schools (Figure Three.).

Factors

School 1. Trust of own 2. Trust of others’ 5. Perspective 6. Processing 7. Planning

B 2.381710076 2.381710076 1.831910477 2.381710076 2.381710076

E 2.269772088 2.269772088 2.118256575 2.269772088 2.269772088

M 2.434070803 3.36460499 2.924944595 1.945600599 2.767648307

H 2.68798987 3.820562646 2.082040292 2.664719901 2.461684021

Figure Three. showing the variances in schools B, E, M and H for CAS factors: Perspective,

Processing, Planning, Trust of own ideas, Trust of others’ ideas

In schools B and E the variance showed remarkable consistency between the five factors (0.0292). In

schools M and H (combined as a group M,H) the variance differed between the five factors (0.3144).

In comparing the variance of the two groups using a one-tailed t-test, CAS (10.769) is greater than

CAS Critical one-tail (3.179) therefore the variance between the populations of B,E and H,M is

different.

CAS-Test Two-Sample for Variances

Group

M,H

Group

B,E

Mean 2.715387 2.25561

Variance 0.314452 0.029199

Observations 10 10

df 9 9

CAS 10.76912

P(CAS<=f)

one-tail 0.000783

CAS Critical

one-tail 3.178893

A two-sample T test assuming unequal variances was then performed to test the assumption that there

is no significant difference between the variances of CAS factors in group M,H and group B,E. The

assumption proved false (t -2.480> t critical two-tail 2.200), indicating that there is significant

difference between the variances of groups M,H and B,E. A comparison between the means of the

variances of the five factors in schools B, E compared schools M, H shows that B, E (mean = 2.255)

is significantly lower than M, H (mean = 2.715).


t-Test: Two-Sample Assuming Unequal Variances

Variable 1 Variable 2

Mean 2.25561 2.715387

Variance 0.029199 0.314452

Observations 10 10

Hypothesized Mean

Difference 0

df 11

t Stat -2.48021

P(T<=t) one-tail 0.015281

t Critical one-tail 1.795885

P(T<=t) two-tail 0.030562

t Critical two-tail 2.200985

This data indicates that schools B and E showed significant differences in the variance of CAS factor

scores to schools M and H. The variances in individual CAS factor (CAS) scores in schools B and E

were significantly lower than the variances of the same scores in schools M and H.

Analysis 2. Analysing Footprints CAS Rank correlations with academic performance in

the four different schools

A second set of analyses then sought to establish whether these differences in the variances of CAS

scores could be attributed to a suppression of expected variance in CAS by the environments of B and

E. The hypothesis of CAS is that optimal CAS correlates with higher academic performance (Walker

2014) as measured by CAS Rank.

If CAS is a non-ecological function of cognition, then this hypothesis will be demonstrated

independent of environment. In other words, the hypothesis of optimal CAS will be supported by the

evidence of CAS Rank–academic performance correlation in each of the four schools. School will not

be a factor influencing the optimal CAS hypothesis.

However, if CAS is an ecological function of cognitive-affective state, then optimal CAS may

not be demonstrated independent of environment. Environment may have a role in influencing CAS.

Multiple analyses of student CAS scores compared to existing measures of academic

performance in each of the four schools were then conducted. This was to establish how CAS

correlated with high and low academic performance in each of the four independent learning

environments.

Analysis of Variance between CAS rank vs Grade rank at school H

A one-way ANOVA was used to test for the relationship between CAS Rank and Grade Rank in

school H. Grade Rank is a categorical independent variable and CAS Ranking score is a categorical

dependent variable; there are more than two levels of the independent variable, thus the appropriate

analysis is the one way analysis of variance. The relationship between CAS Rank and Grade Rank differed significantly F (1, 34) =

6.679, p = 0.0142


df SS MS F Sig F

Regression 1 34.64751 34.64751 6.689373 0.014154

Residual 34 176.1025 5.179485

Total 35 210.75

Coefficients

Standard

Error t Stat P-value Lower 95%

Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 12.29009 1.420329 8.652993 4.13E-10 9.403639 15.17655 9.403639 15.17655

13 -0.37156 0.143658 -2.58638 0.014154 -0.6635 -0.07961 -0.6635 -0.07961

A regression analysis was performed to confirm the relationship between CAS Rank and Grade Rank,

CAS (1,96) =6.689, significance CAS = 0.01412. The slope is significantly non-zero, indicating that

there is probably a relationship between CAS Rank and Grade Rank.

Analysis of Variance between CAS Rank and Set at school M

A one-way ANOVA was used to test for the relationship between CAS Rank and Set in school M.

Set is a categorical independent variable and CAS Ranking score is a categorical dependent variable;

there are more than two levels of the independent variable, thus the appropriate analysis is the one

way analysis of variance.

The relationship between CAS Rank and Grade Rank differed significantly CAS (1, 56) =

8.145, p = 0.0061

df SS MS F Sig F

Regression 1 9.810609 9.810609 8.144521 0.006113

Residual 54 65.04653 1.204565

Total 55 74.85714

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 0.706286 0.596767 1.183519 0.241787 -0.49016 1.902732 -0.49016 1.902732

3 0.733003 0.256846 2.853861 0.006113 0.218058 1.247949 0.218058 1.247949

Analysis of Variance between CAS Rank and Set at schools B and D

A one-way ANOVA was used to test for the relationship between CAS Rank and Set in schools B and

E. Set categorical independent variable and CAS Ranking score is a categorical dependent variable;

there are more than two levels of the independent variable, thus the appropriate analysis is the one

way analysis of variance.

The relationship between CAS Rank and Grade Rank differed significantly F (1, 153) =

1.958, p = 0.1655 The relationship between CAS Rank and Set did not differ significantly indicating

there is probably not a relationship between CAS Rank and Set at schools B and E.

df SS MS F Sign F

Regression 1 7.21829 7.21829 1.958628 0.165526

Residual 153 294.8305 3.685381

Total 154 302.0488

Coefficients

Standard


Upper

95%

Lower

95.0%

Upper

95.0%

Intercept 12.72212 0.413831 30.74233 4.56E-46 11.89857 13.54567 11.89857 13.54567

X Variable 0.143109 0.102257 1.39951 0.165526 -0.06039 0.346607 -0.06039 0.346607


Discussion

Ecological and non-ecological function

Analysis 1.

The results support the hypothesis that CAS is an ecological function rather than a non-ecological

function. The results from analysis 1. show that, in schools B and E the within-factor variance was

lower within each of the five CAS factors of this model (mean within factor variance= 2.255)

compared to within-factor variance for students of schools M and H (mean = 2.715). In other words,

students in schools M and H were adjusting their cognitive-affective state more between Maths,

Science and English lessons than students in schools B and E.

We also found that the variance across the five factors in schools B and E was remarkably

consistent (0.0292). Each of the five factors of this model of CAS exhibited a very similar range of

scores to the other four suggesting that some degree of supra-factor controlling influence limiting the

factor score range. This contrasted sharply with the range of intra-factor scores for schools M and H

(0.341) which showed a greater variability between the factors with some exhibiting a narrow range

of variance (1.94) and others a wide range of variance (3.82).

Analysis 2.

The results from analysis 2. showed that optimal CAS (as measured by CAS Rank) correlated with

academic performance in two of the four schools (M and H) but not in schools B and E. The

hypothesis that optimal CAS (as measured by CAS Rank) is a non-ecological function of cognition is

not supported by this result as CAS scores are influenced by the ecological context of school.

Analysis 1 & 2.

When we relate analysis 1 to analysis 2 we can infer that the environment of schools B and E is acting

as a constraining influence over CAS, restricting the variability of CAS both between lessons and

between factors. Anecdotally, we can report that the environment in schools B and E was strict and

controlling, behaviour of students both in and between lessons appeared to be more directed by the

teachers. Overall, schools B and E evidenced more dominating and directive cultures than schools M

and H which, by comparison tolerated a greater degree of student freedom and diversity of

behaviours.

This study then offers an ecological explanation for the non-significant correlation between

academic performance and CAS Rank in schools B and E, in contrast to the significant correlation

between Set/Grade Prediction and CAS Rank in schools H and M. Individual students would not be

expected to exhibit a correlation between Set and optimal CAS in a school in which CAS was

principally restricted by overall school factors.

CAS and Executive Function

Evidence from a previous study indicates that this model of CAS and CAT appear to be measuring

overlapping but different factors that are involved in the prediction of academic achievement. The

CAT test, as a measure of g, is a non-ecological measure that accounts for a significant proportion of

variance in predicted GCSE grades but not all. Walker claims that a proportion of the variance in

predicted grade which could not be accounted for by CAT can be accounted for by CAS Rank (CAS).

Walker has previously claimed evidence that less academically successful students fail

because their cognitive-affective attunement (sub-optimal CAS) is either inaccurate or inflexible.


Inflexible students fail because they do not adjust their cognitive-affective state of CAS at all in

different learning contexts. These students appear to lack contextual awareness and the cognitive

flexibility. Other students, do adjust CAS, but their attunement is inaccurate. In other words, they

adopt a sub-optimal state of CAS for the learning contexts.

Walker proposes that CAS sits within the umbrella construct of executive function (EF) and is

involved in the adjustment of the cognitive-affective state of the learner toward their environment.

EF is a generalised construct that sits over a range of elements that relate to the effective

handling of novel situations. These include events that involve planning or decision making; that

involve error correction or troubleshooting; situations where responses are not well-rehearsed or

contain novel sequences of actions; dangerous or technically difficult situations; situations that require

the overcoming of a strong habitual response or resisting temptation.

One way of conceptualising CAS and EF is to see CAS as a subset of EF which is responsible

for guiding the cognitive-affective engagement of the learner to their environment. CAS is responsible

for the action of the learner; it directs the learner’s agency and embodied response toward the

environment (Figure Five.). The Miyake and Friedman (2000) model of executive function of

updating, inhibition and shifting provides one framework for understanding that conceiving of the

relationship between g, EF, CAS and the environment.

Figure Four. A postulated schema for the relationship between g, EF and CAS in guiding the

cognitive-affective engagement of the learner to their environment

The function of CAS, then, can be conceived as an internal-external cognitive-affective tuning system

by which the individual adjusts, shifts, regulates and directs attention and behaviour toward their

environment.

Environment A Environment B Environment C

Independent learning environments

Figure Five. CAS shown as an ecological function of cognitive-affective state by which the

learner potentiates engagement in different learning environments

EF g

Specific external

environment of learner

Sensory

data




Shifts in CAS represent changes in cognitive-affective state as the learner engages in different

learning environments. (Figure Two.) Sensory data from the external environment is detected by the

learner. This is processed, I propose via g, the brain’s capacity to process verbal, non-verbal,

numerical and spatial data. G, which itself is a construct of non-ecological intelligence, must interact

with the ecological function of cognitive-affective state in order for the learner to respond to the

learning environment. In response to the data from g, the state of CAS may alter in order for the

learner to adjust to the cognitive-affective requirement of the learning environment which has been

detected.

The role of the imagination in EF and CAS

The Footprints CAS assessment measures CAS using a simulated ecological technology. The

technology works by utilising the capacity of the candidate to imagine them self performing the

learning activities of their class. Walker (2013) has discussed how this imagination mechanism may

work to provide a representation of the state a student actually adopts in their lessons. He suggests

that student response is informed by how students remember themselves performing and how they

conceive of themselves performing if such a scenario were real.

One question this study poses is whether the imaginative function utilised by the Footprints

CAS assessment to measure CAS represents CAS in the CAS assessment, or whether CAS functions

through the imagination. In other words, does CAS govern the cognitive-affective engagement of the

learner to the environment through the imagination, or is the imagination (as captured by the CAS

assessment) simply able to represent the operational activity of CAS in the real world?

If the imagination is representing the operational activity of CAS in the real world as an act of

memory then it is more likely that the imagination is not a route by which future actions are directed

and coordinated. However, multiple studies suggest that the region of the brain activated by imagining

an activity is the same as that when remembering having performing that activity (Schacter, Addis et

al. 2007).

One model of EF proposes that cognition requires the ability to anticipate and organise mental

operations in order to fulfil a sequence of mental activities effectively (Stein 1994). Schacter, Addis et

al. (2007) argue that the brain projects forward a method of self-operation prior to then enacting that

projected sequence. In this way they propose, the brain’s capacity to imagine serves as a guide or

route map directing action. A faulty route map will lead to faulty action and, in this case, unsuccessful

learning. Walker (2013) suggested, if this were the case then imagined cognitive self-operation may

have a direct influence on successful or unsuccessful mental operation in actual tasks. At this time,

however, we cannot determine whether CAS functions through the imagination or whether the

imagination is simply able to represent the functions of CAS.

Activating and deactivating environments

Walker shows evidence that (2014 b.) that school environment can have an activating or deactivating

impact on one measure of CAS in learners. He identifies three zones of environmental experience in a

GCSE year group, determined by predicted grade. Students with predicted grades of D showed an

activated CAS scores in relation to openness to new learning, conscientiousness and eagerness to

please. By contrast students with either one predicted grade higher (C) or lower grades (E.F and G)

showed deactivated CAS scores in relation specific factors to do with openness to new learning,

compliance and conscientiousness. A C grade is an important pass/fail threshold for GCSE. Walker

suggests that this is due to the activating or deactivating impact of the environment in which the

perception of the student that they can or cannot achieve a goal influences their CAS. Students who


feel they are unable to reach their goal, or who feel they have already achieved their goal exhibit a

deactivated CAS.

Smith and Jostman et al. (2008) provide interesting evidence that the perception of power a

person holds in a situation increases aspects of their executive function. By contrast, a perception of a

lack of power inhbits or decreases the EF capacity of individuals in a group. Students to whom grade

improvement feels beyond their reach may be an example of a cohort who experience a lack of power.

This may then become a reinforcing depressive lever on their EF and CAS.

Improving CAS through explicit metacognitive signposts

This study raises the interesting possibility that it may be possible to improve a learner’s CAS through

explicit metacognitive signposts. If CAS is an ecological function of EF, then by establishing clear,

explicit metacognitive signposts for the five CAS factors in the learning environment, one might

predict that learners would be better able to adjust CAS to the environmental learning task through the

feedback loop (Figure 6.). Some authors have proposed intrinsic self-regulatory mechanisms by which

students can improve their academic function (Zimmerman 1996); the CAS model suggests that a

productive route is an environmental strategy. Such an approach would focus on the use of

metacognitive maps and direction in the classroom to provide clear markers for when and how to

adjust CAS state depending on the learning task at hand.

Such a strategy has the potential to support learners who do not currently have a

metacognitive internal awareness to make such choices. Theories of task shifting provide evidence

that changes of mental state impose a cognitive cost (Mayr, Keele 2001; Monsell 2003). Pessoa

asserts that this costs should be understood as an emotional cost (2009). If shifting cognitive state

involves emotional work, then reducing anxiety and uncertainty by increasing pedagogic clarity and

guidance about when and how to use the optimal CAS, should reduce the cognitive load upon the

learner. Walker (Walker 2014 c.) cites evidence that emotional independence / autonomy in thinking

correlates with higher academic performance, suggesting that such a strategy may be effective.

Figure Six. A possible strategy for improving CAS using explicit metacognitive signposts to

direct the learner to engage optimally in the learning environment.

EF g



Sensory

data

Sensory

data

CAS factor 1 signpost





Modified state of CAS in response to metacognitive sensory data

Metacognitive signposts

Learning environment


Further work

This study utilises data collected from a small sample group for analysis 2. Further studies should

look to replicate these findings. Outstanding questions include what is the structural relationship

between CAS and EF? Does imagination reveal or direct CAS? What effect do explicit metacognitive

signposts for the five CAS factors have on CAS and academic performance?

Conclusions

CAS Rank (CAS) as measured by the Footprints assessment in yr 10 students in the UK has been

shown to correlate with measures of general intelligence (g) and academic performance (predicted

GCSE grades).

This study looked at the claim that CAS Rank (CAS) should be considered as an ecological

component of Executive Function rather than a non-ecological, or intrinsic component. An alternative

non-ecological hypothesis was proposed which would predict that variance in CAS scores across

independent populations of learners would not be significantly different. This would demonstrate that

CAS was a non-ecological measure.

This prediction was tested using data of Footprints CAS assessments from four cohorts of yr

10 students in the UK, across four different, independent learning environments. The data was

collected between December 2012 and December 2013 at four UK secondary schools. 496 Yr 10

students took part and more than 2,500 subject lesson assessments were made.

The alternative non-ecological hypothesis was not supported. The variance in CAS scores

varied across the four schools. Two schools showed low variance and two much higher variances both

within the five CAS factors of the Footprints model and between them. The low variance schools

showed a non-significant correlation with the optimal CAS model which predicts that academically

successful students will show a pattern of CAS scores that match a theoretical optimal pattern. By

contrast, the higher variance schools showed a significant correlation with the optimal CAS model. I

conclude that in low variance schools a cultural suppression factor is restricting the range of CAS

scores amongst students, imposing a school wide set of scores which accounts for the low variance.

I conclude that this study supports the claim that CAS is an ecological component of

Executive Function rather than a non-ecological component. I suggest directions in which future

research may go toward testing metacognitive interventions and understanding the role of the

imagination in CAS.

Disclosure

The author acknowledges a conflict of interest through a commercial relationship with the

manufacturers of the CAS Assessment.


Appendix

The CAS Technology: Measuring imagined cognitive self-operation

The CAS assessment is a derivation of a projective test called the Personal Ecology Profile (Walker

2009). The psychological process involves triggering the imagination of the candidate to create a

‘space’ which they want to call their own through a series of neutral cues. The clean language of the

assessment is important to allow the candidate to project their own, independent meaning and shape

onto the cues.

Further verbal cues develop the imagined focus of the candidate on their previously created space,

their imagined self-perception and self-operations

Further verbal cues then develop and explore the candidates’ imagined self-perception and operation

with the learning context present. A series of 28 statements then appear and are scored by the

candidate. These relate to seven factors stated in the data model.


Data Model

This study used Walker’s conceptual model of Human Ecology Theory (Walker 2009) to define the

cognitive self-operation. In the Footprints assessment four items score each factor. Each item is

scored on a six point Likert scale as above. This results in twenty eight items measuring cognitive

self-operation within a single learning environment.

The multiple learning contexts assessed therefore multiplies the number of times each item is scored.

A sample of three of the items is given below.

- Do you need to know what is going to happen in YOUR SPACE when the keyword is with

you?

- Does it help your learning in keyword when you can relate it to your own life?

- You need to make something in YOUR SPACE. Do you get lots of ideas popping into your

head as you go along?


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Centre for Human Ecology Theory, UK

www.humanecology.webeden.co.uk

The Centre for Human Ecology Theory was launched in 2013 and aims to develop insight into human

behaviour using Walker's Human Ecology Theory as its major tool through its research projects. The

Centre aims to bring together a community of practitioners from around the world committed to

developing understanding of human behaviour and how to engender more humane, sustainable living

through application of these ideas.

Walker's Human Ecology Theory was developed over a decade, from 2000-2010, by the author

through his work initially carried out whilst doing postgraduate studies at Oxford University in the

UK. Encompassing areas of human behaviour from personality theory, through to leadership,

organisational dynamics, teaching and learning, coaching and market cycles, Walker's Human

Ecology Theory claims to be a comprehensive human systems paradigm.

Resume of the researcher: Simon P Walker

Simon Walker taught at Wycliffe Hall, Oxford University between 2002-2009. He worked as a

consultant to the corporate world from 2002 and founded in 2004 The Leadership Community, an

alumni of graduates from his Undefended Leader course that grew to around five hundred over the

next five years.

In 2011 he announced a refocus on the area of education and schools, with a commitment to develop a

curriculum for social, emotional and cognitive development. Walker co-authored with Jo Walker, also

his wife, the CAS schools programme, a version of the Human Ecology Approach for children. He

became a Coach in Residence at Monkton Combe School in 2012.

Walker is the author of several ideas about human behaviour including a distinctive theory which he

calls 'Human Ecology Theory', described in a monograph 'A Brief Introduction To The Theory of

Human Ecology.'

CASrom his Human Ecology Theory Walker has developed a number of other ideas in the areas of

leadership, learning and coaching. He published the idea of 'undefended leadership' in a trilogy of

books launched at the Oxford Literary CASestival. His ideas have had an influence on writers in the

area of Christian leadership (MODEM) school leadership (Seldon) and power in leadership (Preece).

Walker has also set out a basis for being 'undefended' upon Christian spirituality which he calls the

Undefended Life and has taught the principles of Undefended Life in Africa, Norway, India and

Australia. This has stimulated numerous responses from other commentators in the church.

Over the years, Walker has developed and commercialised several proprietary psychological

technologies and instruments to analyse and develop people using a Human Ecology Approach. These

including the Personal Ecology Profile, Leadership Signatures, Footprints Assessments and Coaching

Signatures. He has collaborated with Meredith Belbin on several projects.

Prior to his wider adult education career, Walker was ordained as an Anglican vicar in 1997 and

served his curacy in Abingdon, Oxfordshire. He has bachelor degrees in Biology and Theology from

Oxford, an MPhil in Applied Theology from Oxford. He has just submitted his DProf by Public

Works at Winchester University in 2014, a review of his contribution to the adult education between

1997-2014. He is an accredited member of The Association of Executive Coaching and Supervision.

References at http://humanecology.webeden.co.uk/#/who-is-simon-p-Walker/4575814295