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Student Perceptions of Cognitive Efficiency: Implications for
Instruction

Bobby Hoffman1

1) University of Central Florida, United States of America

Date of publication: June 24th, 2013

To cite this article: Hoffman, B. (2013). Student Perceptions of Cognitive
Efficiency: Implications for Instruction. International Journal of Educational
Psychology, 2(2), 109­143. doi: 10.4471/ijep.2013.22

To link this article: http://dx.doi.org/10.4471/ijep.2013.22

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IJEP - International Journal ofEducational Psychology Vol. 2 No. 2

June 2013 pp. 109-143

StudentPerceptions of

Cognitive Efficiency:

Implications for Instruction

This study used a phenomenological approach with content analysis to create a

model of how students perceive cognitive efficiency (CE), which is generally

described as increases in the rate, amount, or conceptual clarity of knowledge,

versus cognitive costs needed to attain knowledge. Graduate education students

completed a five-item open-ended survey to measure perceptions of CE and

what factors they believed enhanced or inhibited CE. Analysis of results

revealed that student perceptions of CE predominantly focused on malleable

aspects of self-regulated and reflective cognition, aligning with many

descriptions of expert teaching. Students described a diminished emphasis on

knowledge acquisition and information processing, in contrast to views

typically associated with CE in instructional and psychological research

(Hoffman & Schraw, 2010; van Gog & Paas, 2008). Practical teaching and

learning implications, including suggestions for instructional practice and

future research are presented.

Keywords: cognitive efficiency, student perceptions, instruction.

Bobby Hoffman

University ofCentral Florida

Abstract

2013 Hipatia Press

ISSN 2014-3591

DOI: 10.4471/ijep.2013.22



IJEP - International Journal ofEducational Psychology Vol. 2 No. 2

June 2013 pp. 109-143

Percepciones de las y los

Estudiantes sobre la Eficiencia

Cognitiva: Implicaciones para

la Instrucción

Este estudio utilizó un enfoque fenomenológico con análisis de contenido para

crear un modelo de cómo las y los estudiantes perciben la eficiencia cognitiva

(EC), que se describe de forma general como el incremento en la tasa, cantidad

o la claridad conceptual de conocimiento versus los costes cognitivos

necesarios para conseguir el conocimiento. Estudiantes graduados completaron

una encuesta semi-abierta de cinco ítems para medir percepciones de EC y qué

factores creían que aumentaban o inhibían la EC. El análisis de los resultados

reveló que la percepción de las y los estudiantes sobre la EC se focalizó

predominantemente en aspectos maleables de la cognición auto-regulada y

reflexiva, acorde con muchas descripciones de enseñanza experta. Las y los

estudiantes describieron un énfasis reducido en la adquisición del conocimiento

y el procesamiento de la información, en contraste con visiones típicamente

asociadas con EC en la investigación instruccional y psicológica (Hoffman &

Schraw, 2010; van Gog & Paas, 2008). También se presentan implicaciones

para la práctica de la enseñanza y el aprendizaje, incluyendo sugerencias para

la instrucción y para la futura investigación.

Palabras clave: eficiencia cognitiva, percepciones de las y los estudiantes,

instrucción.

Bobby Hoffman

University ofCentral Florida

Resumen

2013 Hipatia Press

ISSN 2014-3591

DOI: 10.4471/ijep.2013.22



Verplanken, 2006), is a growing topic of research in the domains of

neuroscience (Ansari & Derakshan, 2011; Bassett, Bullmore, Meyer-

Lindenberg, Apud, Weinberger, & Coppola, 2009; Doppelmayr,

Klimesch, Hödlmoser, Sauseng, & Gruber, 2005; Neubauer & Fink,

2009; Rypma et al., 2008), psychology (Cates, Burns, & Joesph, 2010;

Pyc & Rowson, 2007; Stilley et al., 2010), and instruction (Ayres & van

Gog, 2009; Kalyuga, 2006; Kirschner, Paas, & Kirschner, 2009;

Scharfenberg & Bogner, 2010). Although most conventional definitions

ofCE are domain specific, CE is generally described as increases in the

rate, amount, or conceptual clarity ofknowledge, versus cognitive costs

such as mental effort needed to attain knowledge. Currently, there is

little consensus regarding a conceptual model of efficient cognition or

agreement how to measure and evaluate efficiency outcomes (Hoffman,

2012; Hoffman & Schraw, 2010; van Gog & Paas, 2008; Whelan, 2007).

  Research in CE differs from most research on teaching and learning

in that it focuses on optimal performance under restricted conditions,

rather than on simple performance, while accounting for constraints

such as time, effort, working memory, neurological processing,

motivation, or variation in strategy use. Research in CE is important for

both theoretical and practical reasons. From a theoretical perspective,

cognitive and neurological views of learning emphasize that the

constraints in human information-processing architecture must be

considered to determine what constitutes optimal problem solving,

learning, and associated pedagogy (Kirschner, Sweller, & Clark, 2006;

Rypma et al., 2008; Stanovich, 2009). From a practical perspective,

understanding student beliefs and perceptions has been closely linked to

learning, motivation, and achievement (Pianta, Hamre, & Stuhlman,

2003), and more specifically CE is one of the primary considerations to

inform instructional design (Beckmann, 2010). The development of a

theoretical model that effectively articulates student perceptions of CE

will assist educators in designing learning materials, pedagogy, and

educational contexts that recognize student perceptions and meet the

evolving teaching challenges encountered in the classroom (Corno,

2008; López, 2007; Valli & Buese, 2007).

C
ognitive efficiency (CE), also known interchangeably as

mental efficiency (Paas, Tuovinen, Tabbers, & Van Gerven,

2003; Stilley, Bender, Dunbar-Jacob, Sereika, & Ryan, 2010;

IJEP - International Journal ofEducational Psychology, (2)2 111



  Student perceptions of what constitutes efficient cognition have not

yet been empirically considered. In order for instruction to be relevant

and engaging it should align with students’ needs and understanding

about thinking and learning (McCaslin & Good, 1996; Perry, Turner, &

Meyer, 2008). In addition, the appraisal of student thinking is highly

relevant to foster abandonment of notions that may be misguided or

inaccurate (Linn & Eylon, 2008). Assessment of student thinking is

linked to promoting student conceptual knowledge (Fraivillig, Murphy,

& Fuson, 1999), is instrumental in advancing constructivist pedagogy

(Bereiter & Scardamlia, 1989), and ultimately creates opportunities for

learning (Flutter, 2006; Flutter & Rudduck, 2004; Gillen, Wright, &

Spink, 2011). Specific knowledge of student perceptions about CE will

provide valuable insight to support instruction that matches student

needs (Corno, 2008; Pianta et al., 2003).

  The current study sought to answer three specific research questions

using qualitative methods: how do learners describe cognitive

efficiency; how do learners believe that cognitive efficiency can be

enhanced; and what obstacles are described as inhibiting learners from

being cognitively efficient? A phenomenological approach was used as

existing literature has not documented student perceptions, or compared

these perceptions to existing exemplars of CE found in expert teaching

descriptions (Bereiter & Scardamalia, 1993; Berliner, 2001; Corno,

2008; Feldon, 2007; Hammerness, Darling-Hammond, Bransford,

Berliner, Cochran-Smith, McDonald, & Zeichner, 2005; Sternberg &

Horvath, 1995). The concordant views of students, teachers, and

researchers may be invaluable in proposing instructional strategies that

might promote efficient cognition in the classroom.

The Diverse Perspectives ofCE

Researchers in education, psychology, and neuroscience interpret CE as

either a physiological phenomenon contingent upon optimal

neurological functioning, or as competency in knowledge acquisition

when accounting for constraints on learning such as limited time or

accelerated effort. CE research is typically situated within the

framework of cognitive load theory, which assumes a limited capacity

working memory, and in absence of automatic information processing,

Bobby Hoffman - Perceptions ofCognitive Efficiency112



the need to dedicate more cognitive resources and effort when learning

intrinsically complex material (Kalyuga, 2007). During knowledge

acquisition, the relative effectiveness of instruction materials, the

modality of delivery or pedagogical style can influence how learners

regulate mental effort, and subsequently achieve CE.

  Quantitative changes in the rate, amount, or frequency of knowledge

acquisition can also determine CE (Hoffman & Schraw, 2009). Greater

CE is associated with quicker learning, or the acquisition of more

complex knowledge with a minimal investment of time or effort (Cates,

Burns, & Joseph, 2010). Learners needing more time or exerting greater

effort to achieve similar results in comparison to their own performance,

or to the performance of others, are described as cognitively less

efficient (van Gog & Paas, 2008).

  All views of CE emphasize the importance of working memory

capacity (WMC), which refers to “the limited-supply cognitive

resources that can be allocated flexibly depending on the demands ofthe

task” (Hambrick & Engle, 2003, p. 181). When learners automate

cognitive processing the limits of working memory are moderated and

CE improves. Distinct efficiency advantages are created as automation

requires fewer cognitive resources, reduces the need for attentional

focus, and allows for faster processing of information (Unsworth &

Engle, 2007). For example, in mathematics, learners that bypass time

consuming computational strategies can allocate capacity towards

activities such as rehearsing new material, engaging in analogical

mapping, or algorithmic approaches to problem solving. These activities

eventually strengthen networks for math knowledge and improve

overall competency in performance (Royer, Tronsky, Chan, Jackson, &

Marchant, 1999). Automaticity frees up cognitive capacity to think

about the problems to be solved, and to assist in learning additional

content.

  Most models of CE emphasize the mediating role of strategy use in

reaching learning goals. Even when WMC is taxed, or when

automaticity fails, learners can use strategies to enhance CE (Calvo,

Eysenck, Ramos, & Jimenez, 1994; Hoffman & Spatariu, 2008;

Swanson, Kehler, & Jerman, 2010; Walczyk & Griffith-Ross, 2006).

Strategy choice influences CE since strategies vary in the amount of

cognitive resources needed to execute the strategy, and some strategies,

IJEP - International Journal ofEducational Psychology, (2)2 113



such as direct fact retrieval, are less time-consuming and less effortful.

Conversely, some strategies are counterproductive to CE. When

learners evoke self-regulatory approaches to monitor and reflect upon

their progress towards learning goals additional task demands are

created, and thus capacity must be appropriated between primary and

secondary tasks (Feldon, 2007; van Gog, Kester, & Paas, 2011).

Overreliance on automaticity can also lead to deficits in CE due to

“arrested skill development” (Feldon, 2007, p. 131), resulting from a

decrease in conscious monitoring, or a premature automation of skills

prior to achieving expertise.

  The research cited reveals that CE is a contextualized and task

dependent cognitive process that is reliant on fast, controlled, yet

automatic processing of information combined with the judicious use of

strategies. Dual process models of cognition, using clear empirical

distinctions from neuroscience and cognitive psychology (Feldon, 2007;

Hoffman, 2012; Stanovich, 2004; 2009) mirror a similar multiplicative

view to explain optimal cognition. Two complimentary, yet different

modes of cognition are proposed, generally labeled as autonomous and

controlled (see Stanovich (2004; 2009) and Evans (2008) for analysis

and comparison). Autonomous processing, largely domain specific, is

implicit, reflexive, heuristic, and relatively non-demanding of cognitive

resources. Controlled processing is methodical, resource demanding,

conscious, and analytical. The two symbiotic components work in

tandem balancing physiological capability, learner motivations, and

environmental constraints, with the goal of completing task demands.

CE results when the two systems coordinate to reaching learning

objectives with minimal time, low effort, and consistent accuracy.

How CEApplies to Teaching and Learning

Understanding the variation between the research findings described

above and student perceptions ofCE is highly relevant for at least three

applied reasons related to teaching and learning. First, pre-instructional

beliefs and lack of congruence between instructional objectives and

learner understanding can perpetuate construct misconceptions (Chinn

& Brewer, 1993) and impede construction ofknowledge (Greene, Muis,

& Pieschl, 2010; Hammer, 1996). Misalignment of student and teacher

Bobby Hoffman - Perceptions ofCognitive Efficiency114



perceptions has been linked to inferior learning climates (Gillen et al,

2011; Pianta et al., 2003) and academic risk factors such as impaired

student-teacher relationships (Fan et al., 2011). Potential consequences

of cognitive inefficiency due to learner/teacher misalignment include

ignoring critical content, misperceiving meanings and application of

new knowledge, and inferior construct representations in memory,

leading to poor recall (Vogel-Walcutt, Marino Carper, Bowers, &

Nicholson, 2010).

  Second, some learning contexts, typical to many higher education

classrooms, exacerbate the need for CE. Learners completing

standardized or classroom testing under time limits, or students needing

to rapidly learn material, are especially vulnerable to inefficient

cognition (Walczyk, & Griffith-Ross, 2006). Unlike simple learning

without time considerations, restricted conditions place additional

demands upon learners to achieve fast performance, and time

restrictions negate the value of using compensatory strategies that

typically mitigate CE during unrestricted tasks (Hoffman & Spatariu,

2008; Walczyk, Wei, Griffith-Ross, Goubert, Cooper, & Zha, 2007). In a

study of cognitive disruptions, similar to the type found in many

classrooms, Bailey and Konstan (2006) found up to 27% longer task

completion times and more errors on interrupted computational and

reading tasks then when compared to an uninterrupted control group.

The elimination of interference allowed for more focused attention and

superior performance suggesting that counterproductive contextual

variables can impede CE.

  From a traditional information processing perspective (Ericsson &

Kintsch, 2007), CE is a prerequisite for the use and refinement of

higher-order thinking skills. Many instructional situations require that

learners decipher relevant and key knowledge constructs from an

abundance of facts by actively filtering out extraneous and irrelevant

information. Ineffective filtering, or the dedication of time and effort to

ancillary aspects of a task, may result in cognitive overload, or a focus

only on non-salient task aspects (Kalyuga & Sweller, 2005). Learners

addressing irrelevant task aspects have been associated with non-

productive haphazard memory searches for solutions (Vogel-Walcutt et

al., 2010), or failure to eliminate non-essential steps in the learning

process (Kalyuga, 2006). The cognitively inefficient learner is

IJEP - International Journal ofEducational Psychology, (2)2 115



disadvantaged, with impoverished resources dedicated toward shallow

learning and unavailable to be used for reasoning, evaluative, and

metacognitive strategies often found related to deeper learning,

improved performance, and knowledge transfer (Corbalan, Kester, &

van Merriënboer, 2009).

  Third, several descriptions of expert teaching mention the need for

efficient cognitive processing as a necessary component to be

considered a teaching expert (Bereiter & Scardamalia, 1993; Berliner,

2001; Feldon, 2007; Hammerness et al., 2005; Hattie; 2003; Sternberg

& Horvath, 1995). Expert teaching denotes the culturally determined

qualities and practices that describe teachers deemed superior in

comparison to normative or defined standards of performance,

knowledge, or productivity (National Board of Professional Teacher

Standards, 2012). Teaching expertise is not an automatic function of

experience (Berliner, 2001), but instead involves the application of

broad domain knowledge and a repertoire of teaching strategies

(Fenstermacher & Richardson, 2000) that results in superior student

achievement.

  Models of teaching expertise vary broadly (see Hattie, 2003; Tsui,

2009 for reviews), but in regards to CE several themes transcend

theoretical models. “Adaptive experts” (Bransford, Derry, Berliner,

Hammerness, & Beckett, 2005, p. 48) rapidly retrieve information with

minimal attentional resources, practice higher-order thinking skills

routinely, judiciously and quickly direct cognitive resources and

attentional control (Sternberg, 1998), while concurrently monitoring,

evaluating, and adapting teaching strategies in response to classroom

activity (Artzt & Armour-Thomas, 1998). Other expert teaching

approaches suggest that superior working memory capacity, coupled

with automatized schemas and routines (Feldon, 2007; Hammerness et

al., 2005), and regulation and economization of mental resources,

coordinated with a strong emphasis on metacognitive awareness are

essential for teaching expertise (Bereiter & Scardamalia, 1997). Expert

teachers devote greater cognitive resources to activities that promote

learning, successfully manage the elimination of extraneous cognitive

load and are far less likely to be consumed by prescriptive routines

(Feldon, 2007). Table 1 summarizes empirically supported CE

Bobby Hoffman - Perceptions ofCognitive Efficiency116



exemplars represented in a variety ofexpert teaching descriptions.

A
u
to
m
a
ti
c
it
y
/
W
o
r
k
in
g

M
e
m
o
r
y

F
il
te
r
in
g
o
f
e
x
tr
a
n
e
o
u
s

c
o
g
n
it
iv
e
lo
a
d

R
e
fl
e
c
ti
v
e
c
o
g
n
it
io
n
/

S
p
e
e
d
/d
e
p
th
o
f

k
n
o
w
le
d
g
e
a
c
q
u
is
it
io
n

S
p
e
e
d
o
f
p
r
o
c
e
ss
in
g

A
d
a
p
ti
v
e
S
tr
a
te
g
y
U
se

Bereiter &

Scardamalia, 1993
X X X

Berliner, 2001 X X X X

Feldon, 2007 X X X X

Hammerness, Darling-

Hammond et al., 2005
X X X X X

Hattie, 2003 X X X X X

Sternberg & Horvath,

1995
X X X X X

Schulman, 1987 X X

R
e
g
u
la
ti
o
n
o
f
m
e
n
ta
l

e
ff
o
r
t

X

Table 1

CE exemplars included in expert teaching descriptions

The Present Study

The present study sought to aggregate perceptions of students

understanding of CE. Although domain-specific descriptions of CE are

well-articulated in education, psychology, and neurological research, no

study to date has investigated student perceptions of what is considered

optimal cognition. Graduate education students completed a five-item

IJEP - International Journal ofEducational Psychology, (2)2 117



opened-ended survey developed by the author to measure perceptions of

CE and what factors they believed enhanced or inhibited CE.

  Phenomenological qualitative methods using content and comparative

analysis were employed (Miles & Huberman, 1994). This method

ideally fit the purpose of the study due to the intent to determine if

student’s perceptions of CE differed from research descriptions and in

absence of any previous qualitative analysis of the CE construct. Since

research-based findings describe CE as a multidimensional construct,

qualitative approaches were ideal to disentangle the perceptions of

students, as qualitative designs can reveal how constituent parts interact

to define the construct. Findings should provide new evidence that will

enable instructors to better align instructional materials and methods

with student expectations, and provide a further understanding of the

nature of how learner beliefs may be linked to instruction promoting

CE.

Method

Participants

Study participants were from a large southeastern U.S. public university

(N = 47, F = 33, M = 14) and were a convenience sample of 80%

education majors taking a graduate level course in learning and

instruction. The majority of the participants were in-service teachers or

individuals completing education courses for alternative route teaching

certification. The participant demographic data indicated 78.7% were

Caucasian; 10.6% Hispanic; 4.2% African-American; 4.2% Asian; and

2.1% did not indicate an ethnicity. The average participant age was 31.4

and the mean grade point average ofparticipants was 3.26. Participation

was encouraged by offering students extra-class credit resulting in 100%

student participation from two different class sections taught by the

same teacher. The sample of graduate education students was selected

based upon anticipated future work in teaching and instruction and

because of the emphasis on efficiency in some models of expert

teaching (Berliner, 2001; Bereiter & Scardamalia, 1993; Darling-

Hammond & Bransford, 2005; Feldon, 2007; Sternberg & Horvath,

1995).

Bobby Hoffman - Perceptions ofCognitive Efficiency118



Procedures

Data was gathered by administering an in-class survey that consisted of

five open-ended questions designed to determine the student’s

perceptions of CE, and factors perceived as influencing the facilitation

or inhibition ofCE (See Table 2).

Table 2

Survey questions

1. What is cognitive efficiency?

2. How do you know when you are cognitively efficient, how can you tell?

3. What factors decrease your ability to be cognitively efficient?

4. What factors increase your ability to be cognitively efficient?

5. Do you believe cognitive efficiency is a general trait, or a trait that changes

according to the subject matter you study or the task you do?

  _______General  _____ Changeable  ______ Both

  The survey was administered prior to any class discussion of

cognition or motivation during the term ofthe course to avoid responses

being biased by any specific cognitive theory. Any participant indicating

advanced knowledge of cognitive or motivational processes was

excluded from the study. Advanced knowledge was determined by self-

selection by the participants or exclusion by the researcher, if the

participants had taken any previous courses in cognitive, motivational,

or educational psychology at the graduate level. No participants

required removal from the study. The survey questions were developed

by the author based upon emerging research themes in cognitive load

(van Gog & Paas, 2008; Paas, Tuovinen, Tabbers, & Van Gerven, 2003)

and cognitive efficiency theory (Hoffman, 2012, Hoffman & Schraw,

2010, Stilley et al., 2010; Verplanken, 2006) that attempt to measure and

define constructs related to information processing. Participants were

informed that the intent of the research was to learn about how students

IJEP - International Journal ofEducational Psychology, (2)2 119



defined cognitive efficiency under the premise that the research results

could provide instructors with additional knowledge to enhance the

efficiency ofinstruction.

Method ofInquiry andAnalysis

  Design

The current inquiry used a phenomenological lens to examine student’s

perceptions of CE. A phenomenological approach was chosen to offer

researchers and practitioners a descriptive, reflective, and interpretive

analysis of individual perceptions (Richards & Morse, 2013) that were

previously unknown. Phenomenological premises (Giorgi, 1997)

emphasize the researcher’s goal of discovering the psychological

substance of a phenomenon, not a “universal or philosophical essence”

(p. 100). Data using the phenomenological approach allows the

researcher to construct knowledge and understand the nature of the

individual inquiry, with the current intent to analyze and compare

previously unreported student perceptions of CE with those found in

published research.

  Data analysis method

Content analysis in three phases (Creswell, 2008; Miles & Huberman,

1994) was employed by the author to generate one or more codes from

each survey response in order to summarize the data and create general

categories from the full data set. During the first phase of content

analysis, data repetitions and linguistic connections were used to

generate 383 individual in-vivo codes (labels phrased in the exact words

of participants) or lean codes (labels phrased in the words of the

researcher). A summary is provided in Table 3. Descriptive code

generation was used to determine individualized accounts ofCE and the

factors related to the facilitation and inhibition of efficient cognition.

For example, when answering the question “what does it mean to be

cognitively efficient?” a participant indicated “to be able to think

coherently and rapidly without missing significant information”. This

statement generated the in-vivo codes of “coherence” and “speed”, and

Bobby Hoffman - Perceptions ofCognitive Efficiency120



the lean code of“thoroughness”.

  In the second phase of analysis, cluster coding was used to

consolidate the phase one data to create 14 condensed categories,

positioning each category at the center of the participant thought

process, and relating to similar codes from phase one (Creswell, 2007).

The phase two coding was completed individually by two trained

graduate assistants resulting in 92% coding agreement. The initial

categories were developed as a result of shared discussions between the

coders. Initial discrepancies and ambiguous codes were resolved

through discussion with the author until 100% coding agreement was

reached. For example, phase two analyses included the consolidation of

terms “fewest steps”, “precision”, and “accomplish the task effectively”

into the category “organization”.

Table 3

Frequency ofcondensedcategories by theme

Condensed categories

0

Cognitive/Affective Environmental Total

Time to complete task

Physiological

0 40 40

Organization 0 10 44 54

Distraction 0 32 32 64

Resources 10 0 1 11

Timely completion oftask 0 0 10 10

Concentration 0 17 0 17

Interest 0 15 0 15

Awareness 0 8 0 8

Ability 0 18 0 18

Health 44 0 0 44

Accomplish task 30 0 0 30

Decision Making 5 7 0 12

Performance 42 0 0 42

Stress 5 13 0 18

Total 136 120 127 383

IJEP - International Journal ofEducational Psychology, (2)2 121



  The third coding phase led to the identification of three main

categories. Physiological influences included individual differences,

health, or measurable conscious actions related to one’s physical

condition, but unrelated to cognition, that a participant described as

related to efficiency. Cognitive or affective determinants represented

what the participant was thinking or feeling when completing a task and

being cognitively efficient. Cognitive and affective exemplars of CE

were combined due to the interdependence of the constructs as

described in the neuropsychology (Ray & Zald, 2012) and education

literature (D'Mello & Graesser, 2011; Efklides, 2011; Pekrun, Elliot, &

Maier, 2009). The environmental category emerged from codes that

described the influence of factors external to the person attempting to

complete a task, but were not related to the internal physiological state

of the respondent. These themes and condensed categories served as the

basis for the analysis and subsequent development ofa model indicating

what strategies contributed to enhancing CE (see Figure 1).

  Next, an adaptive prototype design framework (Sternberg & Horvath,

1995) was used to create a table comparing student perceptions ofCE to

research descriptions, including instructional implications for each CE

exemplar (see Table 4). Prototype models, originally conceived by

Rosch (1973) were designed to eliminate the “fuzziness” of discrepant

categorical exemplars. The prototype view contrasts similarities and

differences among exemplars to evaluate the confluence of evidence on

a particular topic.

Analysis and Results

The process of analysis was initiated by using the expertise of the

researcher as a foundation of domain knowledge to describe results,

assess intention, and ascribe meaning (Richards & Morse, 2013), while

accurately transforming the essence of participant perceptions of CE.

Intentionality (van Manen, 1990) was a planned analysis strategy,

whereby the researcher sought to reflect on experienced phenomena,

which included comparisons to descriptions of CE in neurological,

psychological, and educational literature. The analysis process was

repeated individually for each question described below.

Bobby Hoffman - Perceptions ofCognitive Efficiency122



What is Cognitive Efficiency?

Responses to the main research question, “What is cognitive

efficiency?” generated 84 unique codes. Participants most frequently

associated CE with completing a task quickly by utilizing time

effectively (33.3%), with minimal resources (14.2%), and in an

organized (21.4%) and reflective manner (13.0%), while minimizing

intrusive thoughts (10.7%) and limiting environmental distractions

(5.9%). The confluence of responses led to the conclusion that students

perceived CE as the conscious ability to monitor cognitive operations

while completing a task as quickly and as accurately as possible.

  Responses coded as attributing CE to physiological attributes (22.6%)

focused on the deliberate and conscious regulation of mental resources,

not specifically task related, or the physiological readiness to complete a

task. Mental resources included “targeted attention”, “avoidance of day

dreaming” and the “regulation of effort”, but excluded cognitive

strategies such as planning, setting learning goals, or executing

strategies used to complete a task. Physiological readiness included

ample sleep, energy, and nutrition minimally necessary to attempt and

complete a task.

  Cognitive and affective determinants of CE (32.1%) were based on

descriptions of what the person was thinking and feeling while

completing a task under the perception ofefficiency. Cognitive factors

included concentration, interest, and ability, whereas affective factors

targeted reducing anxiety, avoiding stress, and fostering adaptive task

motivation. Substantial variability existed in the type of cognition

described by participants. Some participants emphasized an information

processing view of CE (Ericsson & Kintsch, 2007) for example, stating

CE is “To do something with the least number of steps and in the

shortest amount of time while still doing it effectively”. However,

another participant indicated CE was “the ability to think logically and

rationally” suggesting a reflective approach to evaluating efficient

cognition. Others contended that CE was not possible without

“decisiveness”, “higher-order thinking skills”, “creativity”, or

“confidence”.

  Codes related to environmental factors (45.2%) emphasized the

importance of controlling one’s context and conditions of thinking to

IJEP - International Journal ofEducational Psychology, (2)2 123



achieve and maintain CE. Participants clearly indicated that the greatest

environmental threats to CE were a result ofdistractions (16.6%) due to

self-imposed stress such as lack of sleep (15.4%) or food deprivation

(11.4%), or factors such as “noise”, “movement”, or “chaos”. One

participant indicated, when there are “too many things going on at a

time, the environment is not conducive to the task.” Another stated “the

need to be aware and monitor what works for me”. The comments

suggested that participants felt willing and capable to self-regulate their

learning and thinking environments to foster CE.

  Comparative analysis (Miles & Huberman, 1994) revealed a number

ofdistinct contrasts in the perceptions ofCE. A majority ofparticipants

(34) focused on the process of thought, while others (13) indicated their

CE was based upon the quality of task outcomes. There was little

variability in individual answers concerning the antecedents of CE.

Participants implied that either internal processes (e.g., attention, deep

concentration) determined CE (55.3%), or that external attributes such

as controlling distractions were wholly responsible for their CE

(29.7%). Only nine participants (19.7%) indicated that CE involved the

regulation of both internal and external factors. Finally, participants

were asked to evaluate the domain specificity of CE. Only two

participants (4.2%) believed CE was exclusively a domain general trait,

whereas most participants (53.1%) indicated CE was domain specific, or

contingent on a specific task (40.4%).

  Surprisingly, few participants alluded to the importance of

background knowledge, or effortful cognitive processing as contributory

to CE in contrast to widely accepted views of information processing

(Hoffman & Schraw, 2010; van Gog & Paas, 2008) and neurological

perspectives of CE (Rypma et al., 2008). Frequently participants

stressed the influential role of self-regulatory strategies such as

planning, monitoring, and reflective thought in achieving CE, a view

consistent with many social-cognitive (Zimmerman, 2001) and dual-

process theories of cognition (Evans, 2008; Smith & DeCoster, 2000;

Stanovich, 2004).

  Although student perceptions were partially incongruent with

information processing and neurological perspectives of CE, many

parallels between student perceptions and expert teaching models were

observed. Resemblance across perspectives centered on the need for

Bobby Hoffman - Perceptions ofCognitive Efficiency124



rapid schematic organization of knowledge, the elimination of thought

irrelevant to learning, and strategy adaption. Table 4 lists typical

exemplars of CE aligned with a representative sample of student

responses in conjunction with descriptions found in various teaching

models.

Table 4

CE research exemplars, student perceptions, teaching descriptions, and

instructional inferences

CE Exemplar Sample Student

Perceptions

Sample Teaching

Description

Instructional inference

Regulation

ofmental

effort

“To use time wisely

and work smart.”

“In your mind you

are able to get

organized and

focused all at once

to accomplish a

goal.”

Executive control

including planning,

monitoring, and

evaluating. The

reinvestment of

cognitive resources

(Sternberg &

Horvath, 1995).

View students as active

participants in the

construction ofknowledge;

sequence learning

objectives logically;

openly discuss potential

difficulties learners may

encounter during the

learning process (Artzt &

Armour Thomas, 1998).

Automaticity

or enhanced

working

memory

capacity

“Performing

multiple tasks

simultaneously, to

find or create a path

ofleast resistance.”

“When you don’t

have to reread

instructions,

coming to

conclusions

without a huge

investment of

effort.”

“Operations that

once took thought

and planning come

to be done with

little or no effort”

(Bereiter &

Scardamalia, 1993,

p. 119).

Present brieflessons that

do not overload learners.

Embed repetition into

lessons that promote

automaticity ofprocedures.

Consider just in time

lessons, activation of

existing mental models,

and supportive scaffolding

for non-repetitive

knowledge (van

Merriënboer, Kirschner, &

Kester ,2003).

IJEP - International Journal ofEducational Psychology, (2)2 125



Filtering of

extraneous

cognitive

load

“Mentally efficient

means that you

have mental order.

You don’t waste

time daydreaming.”

“To cut out a lot of

white noise, other

thoughts, other

words.”

“Unnecessary

structural or semantic

content that occupies

space in working

memory”….Teachers

“develop more

elaborate schemas to

process information

efficiently and their

actions require less

mental effort.”

(Feldon, 2007, p.

126)

Remove anxiety producing

learning cues that might

activate stress in high-

anxious individuals,

introduce preparatory

periods that help learners

adjust to restricted

conditions. Provide

learners with

compensatory strategies to

overcome anxiety (Ansari

& Derasham, 2011).

Use of

reflective

cognition

“You do not know

ifyou are mentally

efficient because if

you realize,

metacognitively

that your mind has

wandered offtask

and you are no

longer efficient.”

“To have a rational

thought process

when completing

tasks.”

Reflection and

conscious

deliberation (Tsui,

2009).

“The teacher’s

skillfulness in

monitoring…in-

flight decision-

making in dynamic

environments

(Berliner, 2001).

Given available resources,

provide explicit instruction

on how to monitor for

efficient cognition with a

focus on the evaluation of

the thought process.

(Helsdingen, van Gog, &

van Merriënboer ,2011).

Expert teachers monitor

the learning process,

learning outcomes and

their own intrinsic interest,

while seeking self-

evaluation ofteaching

techniques (Kreber,

Castleden, Erfani, &

Wright, 2007).

Bobby Hoffman - Perceptions ofCognitive Efficiency126



Speed ofor

depth of

knowledge

acquisition

“Mental efficiency

is achieved when

the individual uses

the minimal amount

oftime required to

complete a thinking

task.”

“Asolid education

teaches you how to

think.”

Expert teachers "can

spontaneously relate

what is

happening….can

quickly recognizes

sequences ofevents

occurring in the

classroom which in

some way affect the

learning and teaching

ofa topic.” (Hattie,

2003, p. 5)

The depth of

pedagogical content

knowledge

(Schulman, 1987).

Emphasize that efficient

thinking and learning

involves understanding of

both the process and

outcome ofknowledge

acquisition (Artzt &

Armour Thomas ,1998).

Create a classroom with

structure and predictability

including the use of

scripted routines that

promote learner

preparation (Konrad, Helf,

& Joseph, 2011).

Speed of

information

processing

“To be able to think

coherently and

rapidly”

“Not missing

significant and

important

information, but

achieving a desired

outcome quickly

and thoroughly.”

“People who are high

on efficiency can

rapidly retrieve and

accurately apply

appropriate

knowledge and skills

to solve a problem or

understand an

explanation”

(Bransford et al.,

1995, p. 49).

To promote quick

individual understanding,

during intrinsically

complex learning, focus on

practical application of

knowledge, instead of

theoretical mastery

(Scharfenberg & Bogner,

2010).

Adaptive

strategy use

“Using the fewest

steps possible to

reach a decision or

understanding”

“Know when to

change gears and

what does or

doesn’t work for

you”

An adaptive teacher

…has a propensity to

check students’

thinking and

understanding on a

continuous basis in a

variety ofways and

has a hesitant attitude

about using any one

approach with every

student” (Corno,

2008, p. 171).

Employ contextualized

thinking by demonstrating

responsiveness to changing

circumstances and student

thinking through

impromptu decision

making during, not after

instruction (Berliner,

2001).

IJEP - International Journal ofEducational Psychology, (2)2 127



How Do You Know WhenYouAre Cognitively Efficient?

Students reported that they monitored CE by reflecting on their progress

towards meeting learning goals. Completing the task at hand (22.6%),

with the fewest possible distractions (20.8%), in the quickest amount of

time (22.2%) were reported as the most common actualizations of CE.

Focused attention of mental resources was frequently described as

necessary to achieve CE (25.9%). Students remarked, being “focused in

the clearest possible manner”, having “thoughts flow without

interruption”, and being “able to think without getting distracted” as

representative ofbeing cognitively efficient.

  Mental resources were described in cognitive, affective, and

physiological terms included “working smart”, “feeling confident”, and

having “a clear head”. Specific cognitive determinants included having

both interest and experience in the subject matter. Some participants

claimed that they knew they were being cognitively efficient when they

understood the information, “when you understand something, you can

communicate”. Another participant indicated a problem-solving focus

stating “when I am able to see all sides ofthe situation and work toward

a solution I am cognitively efficient”. Others equated CE with physical

well being and the regulation ofstress. One student indicated “I can tell

when I am cognitively efficient because I am not stressed out and

worried that I am forgetting things, I feel calm when I am cognitively

efficient”.

What Factors DecreaseYourAbility to Be Cognitively Efficient?

The reported impediments to achieve CE were largely based upon

physiological factors, such as sleep and food deprivation (19.4%), stress

(13.9%) or illness (12.9%). Environmental constraints including noise,

and cognitively disruptive aspects of learning were cited as detrimental

to CE by 13.9% ofparticipants. A variety ofchangeable factors such as

the ability to control distractions and lack ofmotivation were additional

reasons that inhibited efficient cognition. Lack of task focus and

maladaptive motivation were also cited as inhibitory to CE, as one

individual stated, “use it or lose it” when referring to the need to

dedicate resources to a task when trying to be efficient. Only 6.45% of

Bobby Hoffman - Perceptions ofCognitive Efficiency128



respondents indicated lack of ability or intelligence as interfering with

their ability to achieve CE, suggesting that most learners in the current

sample held an incremental and controllable view ofefficient cognition.

What Factors IncreaseYourAbility to Be Cognitively Efficient?

Four primary strategies evolved from the 84 codes developed to

describe how CE may be improved: modeling optimal health (16.6%),

limiting distractions (16.6%), gaining more experience through practice

or increasing knowledge (15.4%), and organizing thoughts and

resources (10.7%). Little emphasis was placed on motivational criteria

typically associated with task success such as goals, task challenge, or

effort (Csikszentmihalyi, 1997; Pintrich, Marx, & Boyle, 1993);

however six students indicated interest was a necessary component to

increase CE.

  Students allocated the regulation of CE into two broad categories:

behavioral (48.8%) and mental control (38.2%). Behavioral control

means specific actions that individuals take related to the physical task

environment or surroundings, such as “organizing the work setting”, or

removing “external interference”. Whereas mental control means

monitoring or orchestrating changes in cognitive processes including,

“deep thinking”, “centeredness”, or “having a clear mind”. Figure 1

provides a graphic representation by theme of what strategies students

considered when attempting to improve CE.

IJEP - International Journal ofEducational Psychology, (2)2 129



Discussion

The current study sought to understand student perceptions of efficient

cognition. Several of the views espoused by students differed in

emphasis from research-based perspectives of efficient cognition

(Hoffman & Schraw, 2010, van Gog & Paas, 2008) and efficiency in

descriptions of expert teaching (Berliner, 2001; Bereiter & Scardamalia,

1993; Darling-Hammond & Bransford, 2005; Feldon, 2007; Sternberg

& Horvath, 1995). First, beyond the need for attentional control,

Bobby Hoffman - Perceptions ofCognitive Efficiency130

Figure 1. Model ofstrategies used to increase CE



students significantly understated the role of working memory and

processing resources as instrumental in CE. Second, students associated

success in cognitive tasks as largely dependent upon physiological

readiness and stamina. Last, students placed substantial importance on

the role of experience, not qualitative changes in learning as a

determinant of CE. Given the influence of learner conceptions on

selective attention, deeper processing, and more accurate retrieval

(Pintrich et al., 1993) the incongruence between research findings and

student perceptions may have notable ramifications for learning and

teaching.

  The descriptions of CE suggested that students have their own clear

conceptions of what constitutes optimal cognition. As such, students

described how they assessed and evaluated discrepancies between states

of routine performance and visualized states of optimal cognition. The

self-evaluation and contextual remedies described closely parallel

representations of self-regulated learning strategies designed to promote

academic achievement (Pintrich, 2000; Zimmerman, 2001). Models of

self-regulation employ specific metacognitive strategies whereby

learners consciously and actively regulate cognitive resources,

motivation, and behavior in an effort to enhance progress towards

reaching learning goals. In the context of CE these self-regulatory

strategies involve maximizing resources to quickly and accurately attain

error-free performance. The model depicted in Figure 1, developed from

aggregation of responses, suggests that student perceptions of how to

enhance CE and research-based descriptions of self-regulation may be

closely aligned, ifnot indistinguishable.

  The most frequently contemplated strategies to improve the efficiency

of cognition were internal controllable factors such as focused attention

on task goals, or blocking out aversive environmental stimuli. Students’

advocacy of these types of control strategies suggests a minimized

awareness that cognitive capacity, and thus CE, can be mediated by the

use of information processing strategies. Students may not believe, or

may not be aware, of their ability to modify the transactional aspects of

cognition. Two plausible explanations may account for the diminished

emphasis by students, unconscious automatization of resources, or lack

of motivation to use certain strategies. Both social-cognitive and dual

process theories suggest that some types of cognitive associations such

IJEP - International Journal ofEducational Psychology, (2)2 131



as explicit rule-based processing associated with problem solving and

complex learning takes longer and are more effortful and thus may be

subject to learner motivation (Karoly, 1993; Smith & DeCoster, 2000;

Stanovich, 2004). In addition, many laboratory accounts of self-

regulatory behavior contend that some self-regulated learning strategies

are a depletable, yet renewable resource, and learners may fail to

activate strategies despite capability (Bannert & Mengelkamp, 2008), or

personal agency (Pintrich & Zusho, 2002).

  Only one-fifth of students stated that CE could be improved by both

internal and external regulatory approaches, suggesting that student

perceptions of CE may align with polarized views of motivational

processes during learning, such as dichotomous entity or incremental

views of intelligence, or related performance and mastery goal

orientations (Dweck, 1986). Most students viewed CE as a contextually

driven, domain-specific phenomenon and thus may believe task success

is influenced by effort allocation, or ability, but not both. Partitioning

intellectual efficiency into two classes may also account for the heavy

reliance by some students upon physiological readiness as a CE

prerequisite. In absence of the belief that CE is controllable by internal

regulation, students may overly rely upon manipulation oftheir physical

environment as the best method to enhance CE. Interpretations of this

nature are critical to teaching effectiveness as learner beliefs have been

empirically linked to receptivity of conceptual revision (Pintrich et al.,

1993; Mason, 2007), strategy choice (Zimmerman, 1989), and student

motivation (Dweck & Leggett, 1988). These findings are especially

relevant for educational contexts with restricted conditions such as

standardized testing. Students with misaligned perceptions of CE may

needlessly forgo helpful strategic interventions and inadvertently hinder

test performance.

  Despite the apparent incongruity of student perceptions with

information processing research several commonalities exist with expert

teaching descriptions (see Table 4). The similarities focus on quickly

regulating effort during knowledge acquisition, automating procedural

knowledge, and eliminating extraneous cognitive load while using a

variety of adaptive learning strategies. Although no models of expert

teaching focus exclusively on CE, several models consider promoting

learner efficiency as a necessary prerequisite to achieve developmental

Bobby Hoffman - Perceptions ofCognitive Efficiency132



trajectories for teaching expertise (Bereiter & Scardamalia, 1993;

Feldon, 2007; Sternberg & Horvath, 1995). The investigation of

corollaries across teachers, students, and researchers serves as the basis

for the prototype view (Sternberg & Horvath, 1995) used to create Table

4, which served as a foundation to suggest instructional inferences that

inform CE.

Recommendations for Practice

Isolated knowledge of student’s perceptions of CE may be considered

inert in absence of instructional implications that foster the development

of CE in the classroom. Table 4 displays the nexus of student

perceptions and a cross section of evidence from expert teaching

descriptions to suggest that several logical inferences may be proposed

to cultivate efficient thinking, learning, and problem solving among

students.

  First, learners need to know that CE is a multidimensional construct

that is influenced by knowledge acquisition, enhanced processing

ability, judicious effort, and adaptive strategy use. Instructors providing

greater awareness that CE can be simultaneously regulated by both

internal and external strategies may assist students in making gains in

both the amount and quality of knowledge they must master.

Approaches that emphasize both the algorithmic nature of information

processing and the analytic reflective aspects of learning closely mirror

dual-processing descriptions of cognition (Evans, 2008; Smith &

DeCoster, 2000; Stanovich, 2004) and may be well suited to

deconstructing CE.

  Second, adaption of strategies that foster CE are highly relevant in

light of ongoing changes in teaching standards that emphasize the need

for learners with better critical thinking and problem-solving ability as a

means to address authentic learning challenges within and outside the

classroom. Third, researchers and instructors should consider the

importance placed on self-regulation by learners and investigate how

reflective cognition and metacognitive awareness influence CE. Student

perceptions suggested that CE and self-regulated learning were closely

aligned implying that accurate and well-calibrated metacognitive

activity may be a materially similar construct as CE. Although the

IJEP - International Journal ofEducational Psychology, (2)2 133



sample used in the current study were graduate students who perhaps

may have had knowledge of self-regulated learning (although not yet

covered in their current course of study), and it is unknown if these

views ofCE are a basis for generalization to other populations.

  Empirical studies controlling for multicollinearity of variables are

needed to determine the extent ofvariance in CE explained by judicious

strategy use of all kinds across different domains and populations. The

coalescence ofneurological evidence garnered from brain-based studies

that identify locality of information processing and behavioral

assessments such as think-aloud protocols should provide additional

evidence as to how learners may manipulate and control their cognition

as a means to enhance or attain CE.

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Bobby Hoffman is Associate Professor in the School ofTeaching,

Learning, and Leadership at the University ofCentral Florida, United

States ofAmerica.

ContactAddress: Correspondence concerning this article should be

addressed to Dr. Bobby Hoffman, University ofCentral Florida,

School ofTeaching, Learning, & Leadership, College ofEducation,

P.O. Box 161250, Orlando, FL 32816-1250. Email:

bobby.hoffman@ucf.edu

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