Journal of Data Analysis and Information Processing, 2013, 1, 35-45
http://dx.doi.org/10.4236/jdaip.2013.13006 Published Online August 2013 (http://www.scirp.org/journal/jdaip)
Analyzing Students’ Cognitive Load to Prioritize English
Public Speaking Training
Yow-Jyy Joyce Lee
Department of Applied English, National Taichung University of Science and Technology, Taichung City, Taiwan
Received June 7, 2013; revised July 12, 2013; accepted August 6, 2013
Copyright © 2013 Yow-Jyy Joyce Lee. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
This paper applies the Hierarchy Grey Relational Analysis (HGRA) for data analysis obtained from the EFL students’
cognitive load in English public speaking. Thirty-one EFL students in a class of English Presentation Training partici-
pated in the experiment and the teacher familiarized them with nine criteria of abilities in public speaking training. The
participants were then asked to reflect on their own confidences in the same criteria. A framework employing HGRA
was developed to analyze the data. The results show that from the easiest to the most difficult, the cognitive confidence
in sequence of the participants is S(21 ) → S(27) → S(25) → S(16) → S(12), … , → S(2), and in sequence of criteria is
C(1) Posture → C(5) Preparing effective visual aids → C(2) Eye contact → C(3) Gestures → C(6) Explaining visual
aids → C(4) Voice variation → C(9) Closing the speech → C(7 ) Opening the speech → C(8) Organizing & outlining
the speech body. Based on the findings, in order to tailor to students’ cognitive load for best training results, the teacher
should start from easier, more concrete techniques such as motor skills and preparing effective visual aids, and finally
proceed to the abstract, logical organization of the main points. Additionally, the teacher can even offer differentiated
practices to those whose cognitive load level in speech skills are different.
Keywords: English Public Speaking; Cognitive Load; Hierarchy Grey Relational Analysis (HGRA); Instructional
Design
1. Introduction
In a required EFL public speaking course, most of the
students are novice public speakers at the time of enroll-
ment. Faced with a new subject to be learned, it is very
important that students feel successful. Zheng [1] sug-
gested that unpleasant learning experience could become
a traumatic experience under some circumstances and
might bring down a learner’s self-esteem and self-con-
fidence. MacIntyre [2] argued that anxiety derived from
negative experiences early in language learning experi-
ence. In short, feelings of success and low anxiety facili-
tate learning [3]. Cognitive load theory assumes human
cognitive resources to be limited. When a learning ac-
tivity requires more cognitive capacity than what a
learner is equipped with, it causes an overload on the
learner’s cognition, which eventually results in failure in
the learning activity [4]. What cognitive load theory
seeks, therefore, is the teaching materials and activities
appropriately designed to lower learners’ cognitive load,
by which learning effectiveness is enhanced. These stud-
ies support the view that beginners’ experience is critical
to their future learning success. What speech educator
should do, hence, is to alleviate students’ worries and
anxieties in the first place by carefully planning the cur-
riculum and the pedagogical materials to ensure a suc-
cessful learning experience bestowed on novice speaker
from the outset.
The project herein aims to sort out the skills which
students feel most confident in and generate least anxiety.
If the speech instructor approaches teaching students in
this direction, then students’ cognitive load is more likely
to be in line with their learning. In turn, students feel
more prepared to be challenged by further “difficult”
techniques in public speaking.
Hence, this study proposes a data analysis procedure
utilizing the Hierarchy Grey Relational Analysis (HGRA)
method, which combines the Analytic Hierarchy Process
(AHP) to derive the weight of nine speech attributes and
then inputs the weights to the Grey Relational Analysis
(GRA) method. It is anticipated that this study can de-
lineate the confidence patterns from the data of EFL stu-
dents’ perspectives so that teachers can develop more
effectual instructional design.
C
opyright © 2013 SciRes. JDAIP

Y. J. LEE 36
The rest of this paper is organized as follows. The ra-
tionales for the proposed data analysis procedure are
briefly reviewed in the next section. The subsequent sec-
tions address the procedures of data analysis, the experi-
mental results, and the pragmatic implications.
2. Literature Review
2.1. The Hierarchy Grey Relational Analysis
(HGRA) Method
To investigate the EFL students’ cognitive learning con-
fidence in English public speaking, this study introduces
an integrated model—the Hierarchy Grey Relational
Analysis (HGRA), which combines the AHP and GRA
into a single evaluation model. In fact, this HGRA inte-
grated model has been used by other researchers to
evaluate the correlation between data sequences in dif-
ferent contexts [5-7].
2.1.1. The Analytical Hierarchy Process (AHP)
Method
AHP is frequently used to analyze the multi-attribute
decision systems. Generally, the AHP assumes a unidi-
rectional hierarchical relationship among decision lev-
els—the top element of the hierarchical structure is the
overall goal (s) for the decision model, and the hierarchy
devolves to more specific attributes until a level of man-
ageable decision criteria is met. Each element in the hi-
erarchy is assumed independent of one another; namely,
the decision criteria must be independent of all the others,
and the alternatives are independent of the decision crite-
ria and of each other. AHP employs a system of pairwise
comparisons to measure the weights or to ratio the scale
priorities of the elements by the matrix of linear algebra,
and finally to rank the priority of the alternatives in the
decision [8,9]. To avoid the dataset inconsistency when
making the pairwise comparisons, the AHP procedures
produce a consistency index (CI). If CI < 0.1, it means
the dataset is consistent and clean [9]. Applications of
the AHP technique can be found in different fields (e.g.,
business, industry, government, healthcare, education,
among others) for choosing the favorite alternative from
a given set of alternatives and for other purposes such as
ranking, prioritization, resource allocation, benchmarking,
quality management, and conflict resolution [10-12].
2.1.2. The Grey System Theory and the Grey
Relation al Analysis (GRA) Metho d
The grey system theory was first proposed by Deng [13].
Its primary purpose is to perform relational analysis and
model construction when dealing with discrete, uncer-
tain, multi-dimensional, and incomplete data. Among the
many analytical tools developed for grey system theory,
GRA is one of the most effective experimental processes.
The functions of GRA are to quantify the factors, to
quantitatively measure the distributed time series effects
and conflicting beliefs, and to examine the effective rela-
tive index by means of systematic application [14]. It
does not need a great amount of data, the results are
based on original data, and the calculations are simple
and easy to understand. The grey system theory has been
proven an alternative to traditional multivariate statistical
methods (e.g., factor analysis and regression analysis).
Thus, the GRA has become one of the best methods to
guide the selection process through the ordinal process
and has helped a decision maker solve the selection
problem, particularly when there are multiple and con-
tradictory objectives to be considered. Now GRA has
been applied in many fields such as product design [15,
16], market survey [17], education [18,19], material sci-
ence [20], and information management [21].
2.2. Cognitive Load Theory
Adopting the perspective of information processing
theory and as part of cognitive psychology, Sweller first
introduced cognitive load theory to be applied in educa-
tion in the 1980s. Under its assumption that human cog-
nitive capacity is limited, learning difficulty arises when
an overwhelming amount of information in need of being
processed exceeds the working capacity [22]. Cognitive
load theory was proposed to provide guidelines to lower
learners’ cognitive load and help pedagogy encourage
learner activities that optimize learning output.
Cognitive load theory aims at discovering the rela-
tionships between cognitive processing and instructional
design. Research found that cognitive load exerted great
influence in teaching and learning [23-25]. There also
has been research producing results as useful pedagogical
reference [24,25].
Cognitive load theory and the results of the study pro-
vide a direction for teachers to ponder on the design of
pedagogical materials, presentation of the materials, and
the interpretation of learning difficulties experienced by
students. Firstly, to lessen students’ cognitive load,
teaching materials should be prepared and introduced
from easy to difficult sequence to effectively assist stu-
dents with practicing the abilities, which ultimately in-
ternalized into automatic schema. Such arrangement
based on cognitive load theory will establish students’
confidence and skills. Secondly, the a priori knowledge,
which affects the status of cognitive load in students,
should be investigated at the onset of the course. Reme-
dial treatment should be provided if it is found inade-
quate. Thirdly, inappropriate pedagogical materials/skills
create cognitive overload. Hence, teachers should select
appropriate teaching methods and presentation of the
teaching materials according to the students’ level and

Y. J. LEE 37
the nature of the teaching materials. Fourth, the construc-
tion of schemas is highly related to cognitive load and is
beneficial to learning. Hence, instructors should provide
systematic practices to install automatic knowledge/skills
output or schema in students’ capacity.
Therefore, using the theoretical framework of cogni-
tive load, it is argued that the characteristics of the struc-
tures that constitute human cognitive architecture need to
be taken into account in the design of effective and effi-
cient instruction.
3. Data Analysis Procedure
Specifically, pairwise comparisons of difficulty level
between any two criteria are answered by all responding
students and the AHP procedure is then used to derive
AHP weights and test the dataset consistency. Moreover,
the AHP weights are used in the GRA procedure to
evaluate the degree of correlation for different data se-
quences for each participant and then among the partici-
pants and criteria. The proposed procedure is further ex-
plained below.
3.1. Identifying the Evaluation Criteria
Central to the problem definition is the identification of
evaluation criteria. This study first identifies the best-
selling EFL textbook in public speaking training in Tai-
wan, and then extracts nine criteria of abilities from it.
3.2. Collecting Data by Pairwise Comparison
Each participant makes paired comparisons of elements
on a common property or criterion on a ratio scale: 1/9,
1/7. 1/5, 1/3, 1, 3, 5, 7, and 9. The ratio scale priorities of
each participant forms a subset of a larger dataset col-
lected from all participants.
3.3. Computing AHP Weights and Testing Data
Consistency
The AHP weights of criteria for each participant are
computed by AHP procedure. AHP weights are first de-
rived from ratio scale priorities. Let Cj,m denote the crite-
rion m for participant j, the weights of criteria for all par-
ticipants [W]j can be expressed as

,1 12 131
1
,2 1223 2
11
,3 13 23
11 1
,
j,1 j
123
,2 j,3,
1
1
,1,2,,
1
jm
jm
j
j
jm mmm j
jm
Cww w
Cwww
C Www j
Cwww
CCC C


















In addition, through the AHP procedure the data in all
comparative sequences are verified by the Consistency
Index (CI) to be clean. When CI ≤ 0.1, the consistence of
the pairwise comparison matrix is acceptable.
The CI can be computed as max
1
m
CI m


 (2)
In this study, the CI verification will be repeated 31
times on each participant.
3.4. Establishing the Raw Data of Grey Relation
To set up the GRA matrix, the reference vector x0 and the
comparative vector xj are required [26].










00 000
1,2, ,, ,
1, 2,3,,
x
xx xkxm
km



 (3)
 


  


11 1 1
22 22
1,2,,, ,
1,2, ,, ,
1,2,,, ,
1, 2,3,,
i
i
nn nnn
xx xxkxm
xx xxkxm
xx xxkxm
in

















(4)
If x0 is the reference vector and the rest of vectors
serve as comparative vectors, it is called localization
GRA (LGRA). If any xj can serve as a reference vector, it
is called globalization GRA (GGRA). In this study,
LGRA is adopted because LGRA is usually used for
ranking purposes while GGRA is used for weighting
purposes.
3.5. Normalization of the Raw Data of Grey
Relation
This step seeks to normalize the raw data, denoted below,
for GRA procedures.
 


** ***
1,2, ,, ,
1, 2,,
jj jjj
x
x xxkxm
jn




(5)
The raw data must satisfy three conditions, i.e. non-
dimension, scaling and polarization, before the compara-
bility of the series is established. There are three methods
to generate and standardize the data, and they are: lar-
ger-the-better, smaller-the-better, and nominal-the-better.
In this paper, large-the-better denoted below is applied,





 
min
max min
1, 2,,
ii
i
ii
i i
xk xk
xk
i
x
kx
jn



k (6)
n
(1)
where


max i
i
x
k means the maximum number in j and


min i
i
x
k means the minimum number in j.

Y. J. LEE 38
3.6. Using AHP Weights as Raw Data to
Calculate Individual Grey Relation Grade
This study adopts Nagai’s GRA formula [14] for this step.
Let Γ denote a localization grey relational grade (LGRG)
value, and the reference vector is X0 and the comparative
vector is Xi,
 

max 0
0
max min
,i
ioi
xkxk  
 (7)
where

1
00 0
1
n
ii i
k
xk





  



where max

re-
presents the maximum value of 0i
, and min
repre-
sents the minimum value of 0i
.
Γ0i is compared in the decision-making process. The
larger Γ0i value, the more important the factor is. This
rule serves as the ranking principle of the system. When
Γ0i approaches 1, it indicates that X0 and Xi are highly
related to each other; when Γ0i approaches 0, it indicates
that X0 and Xi are not related to each other.
The step will be repeated 31 times on all participants’
subset, yielding 31 sets of LGRG values for the next
step.
3.7. Grey Relational Ordinal for Participants
and Criteria
Using the individual LGRG values from the above step,
Nagai’s GRA formula [14] is adopted again twice for
this step to compute both students’ and criteria’s LGRG
values. In the first execution, LGRG values of the par-
ticipating students are aggregated, with the students
serving as the Y axis and the criteria serving as the X
axis in the GRA matrix. In the second execution, the
LGRG values of the criteria are aggregated, with the cri-
teria serving as the Y axis and students serving as the X
axis in the GRA matrix.
3.8. Constructing a Consolidated Matrix
The two sets of LGRG values, one from the participants
and one from the criteria, from the above step are ranked
into a consolidated matrix.
4. Experiment
4.1. Research Design
A project was designed wherein nine evaluation criteria
were identified to decipher the participants’ cognitive
confidence in each construct. The nine criteria were
adopted directly from the best-selling textbook Speaking
of Speech (New/e) [27] in Taiwan according to data ob-
tained by a survey with the top-five major EFL textbook
dealers in the year of 2012. A total of nine criteria, coded
as C(1)-C(9), together with their corresponding training
elements were identified in this textbook. The nine crite-
ria of abilities in public speaking include C(1) Posture,
C(2) Eye contact, C(3) Gestures, C(4) Voice inflection,
C(5) Preparing effective visual aids, C(6) Explaining
visual aids, C(7) Opening the speech, C(8) Organizing
and outlining the speech body, and C(9) Closing the
speech (Table 1).
Table 1. The targeted abilities in Speaking of Speech.
Constructs in the public
speaking training Training elements
C(1)Posture
 Maintain a good posture
 Stand tall
 Position the whole body
C(2)Eye contact  Look the audience in the eye
C(3)Gestures
 Use gestures to emphasize
important points & support the
verbal message
C(4)Voice inflection
 Tone and character of voice
 Use stress to emphasize key
words
 Breathe correctly
 Adjust volume
 Adjust pace/rate
 Practice articulation
 Pauses effectively
 Stretch key words
 Vary intonation/pitch
 Avoid filler words
C(5) Preparing effective
visual aids
 Understand different types of
visuals
 Learn different methods for
displaying visuals
 Coordinate body language
with visuals
 Use proper equipments
 Select explaining phrases for
visuals’ maximum output
C(6) Explaining visual
aids
 Explain visuals for their
maximum output
C(7)Opening the speech
 Use openers techniques
 Engage the audience from the
start
 Provide a preview
 Establish a compassion with
the topic
C(8)
Organizing &
outlining the speech
body
 Choose a topic
 Analyze the audience
 Construct a thesis statement
 Learn the structure of an
outline
 Organize main points
 Organize subpoints
 Provide evidence of the
message
 Use transitions/signposts
 Connect the visuals into the
message
C(9)Closing the speech
 Provide a summary for the
audience to remember
 Share personal experiences
 Call for action
 End as you started

Y. J. LEE 39
4.2. Participants
An English Presentation Training class of 31 students,
coded as S(1)-S(31), took part in this experiment. All of
them were senior EFL majors from a university in central
Taiwan. Throughout the semester, the participants re-
ceived constant instructions on how to deliver English
speech. The instructor familiarized them with the above
nine criteria. At the end of the semester they were asked
to reflect on their own public speech confidence in the
nine criteria.
4.3. Data Consistency and HGRA for Individual
Participants
A student skills survey measured the learning of the tar-
geted nine abilities from the participant’s perspective.
Each participant was requested to make a pairwise com-
parison on a 9-point scale: 9, 7, 5, 3, 1, 1/3, 1/5, 1/7, 1/9
about the difficulty level between any two criteria, thus
forming a sub-dataset from each participant. The AHP
procedure was then employed to determine the AHP
weights and the CI value of each sub-dataset. Taking
student No. 1 as an example, Table 2 detailed the pair-
wise comparisons and nine criteria’s AHP weights. The
CI value was less than 0.1, suggesting the whole dataset
for this participant was consistent. All of the CI values
from the 31 participants were under 0.1, confirming the
consistency of the data.
Next, with the help of the MATLAB software, GRA
method was performed 31 times on each participant’s set
of AHP weights. Consequently, it yielded 31 subsets of
LGRG values on the nine criteria.
4.4. The LGRG Values and Rankings
After verifying the data consistency for all participants,
the LGRG values of each participant subset, obtained from
the above step, were aggregated to run the GRA in MAT-
LAB in order to evaluate the 31 participants’ cognitive
confidence in English public speaking. In this GRA pro-
cedure, “student” served as the vertical axis and “crite-
ria” served as the horizontal axis. Table 3 showed the
results. Participant No. 21 (LGRA = 0) had the most
confidence in English public speaking, followed by par-
ticipant No. 27, and so on. No. 2 (LGRA = 1) had the
most difficulty.
Next, in order to evaluate the nine criteria affecting
public speaking confidence, GRA procedure was applied
again on the LGRG values from all participant subsets.
This time with “criteria” served as the vertical axis and
“stu- dent” served as the horizontal axis. Table 4 showed
the results. The easiest criterion perceived by the 31 EFL
participants was C(1) Posture (LGRA = 0), followed by
C(5) Preparing effective visual aids, and so on. The most
difficult criterion was C(8) Organizing & outlining the
speech body (LGRA = 1).
4.5. Consolidated Ranking Table
Finally, we consolidated the information from Tables 3
and 4 into a matrix, detailed in Table 5. In so doing, one
can concurrently view both students’ and criteria’ se-
quences from the easiest to the most difficult in one ma-
trix. The results have displayed on the vertical axis from
top to bottom that from the easiest to the most difficult in
sequence of the 31 participants were: S(21) → S(27) →
S(25) → S(16) → S(12), and so on. In sequence of the
nine criteria were: C(1) Posture → C(5) Preparing effec-
tive visual aids → C(2) Eye contact → C(3) Gestures →
C(6) Explaining visual aids → C(4) Voice variation →
C(9) Closing the speech → C(7) Opening the speech →
C(8) Organizing & outlining the speech body. The most
difficult item was C(8) located on the right of the hori-
zontal axis, while the easiest item was C(1) located on
the left of the horizontal axis.
Table 2. The AHP pairwise comparisons and nine c riteria AHP weights for participant No. 1.
Participant 1 C(1) C(2) C(3) C(4) C(5) C(6) C(7) C(8) C(9) AHP weight
C(1) 1 1/3 1/3 1/5 3 1 5 3 3 0.0948
C(2) 3 1 1/3 1/5 3 3 3 3 3 0.1292
C(3) 3 3 1 1 3 3 5 3 3 0.2087
C(4) 5 5 1 1 5 5 7 7 5 0.3162
C(5) 1/3 1/3 1/3 1/5 1 1 3 1 1 0.0550
C(6) 1 1/3 1/3 1/5 1 1 3 5 1 0.0743
C(7) 1/5 1/3 1/5 1/7 1/3 1/3 1 1/3 1 0.0290
C(8) 1/3 1/3 1/3 1/7 1 1/5 3 1 1 0.0443
C(9) 1/3 1/3 1/3 1/5 1 1 1 1 1 0.0486
CI = 0.0960 < 0.1

Y. J. LEE 40
Table 3. The 31 participants’ LGRG values and rankings.
Participants C(1) C(2) C(3) C(4) C(5) C(6) C(7) C(8) C(9)
LBa 0.2232 0.3704 0.3761 0.3789 0.3413 0.4326 0.4014 0.3839 0.3463
LGRG
(value) Ranking
S(1) 0.0948 0.1292 0.2087 0.3162 0.0550 0.0743 0.0290 0.0443 0.0486 0.6485 19
S(2) 0.0395 0.0445 0.0395 0.0395 0.0863 0.1205 0.2100 0.2100 0.2100 1 31
S(3) 0.0352 0.0399 0.0485 0.0813 0.0674 0.1068 0.0790 0.3696 0.1725 0.6770 20
S(4) 0.0416 0.1094 0.0416 0.2528 0.0416 0.0416 0.2528 0.1094 0.1094 0.9260 29
S(5) 0.0273 0.0273 0.0273 0.0273 0.0704 0.0704 0.4005 0.1961 0.1536 0.4998 8
S(6) 0.0434 0.0285 0.0259 0.2252 0.0293 0.0555 0.1177 0.3568 0.1177 0.5780 14
S(7) 0.0312 0.3531 0.1304 0.0337 0.0207 0.0388 0.0741 0.2188 0.0993 0.5666 13
S(8) 0.0704 0.0474 0.1853 0.2751 0.0412 0.0521 0.1640 0.1015 0.0628 0.9190 28
S(9) 0.0447 0.1192 0.0635 0.0593 0.0411 0.0368 0.1222 0.3283 0.1849 0.7361 22
S(10) 0.0719 0.0315 0.0936 0.0324 0.0480 0.4326 0.0261 0.2307 0.0333 0.4410 7
S(11) 0.0743 0.0381 0.0298 0.1356 0.0354 0.0487 0.2477 0.3162 0.0743 0.6865 21
S(12) 0.1551 0.3353 0.1980 0.0354 0.0314 0.0452 0.0857 0.0466 0.0672 0.3864 5
S(13) 0.0732 0.3001 0.0934 0.0320 0.0397 0.0989 0.1192 0.0507 0.1927 0.7723 23
S(14) 0.0320 0.0933 0.1411 0.0361 0.0825 0.3544 0.0361 0.1925 0.0320 0.9032 27
S(15) 0.0174 0.0174 0.0320 0.1377 0.2706 0.2706 0.0458 0.1377 0.0706 0.8240 25
S(16) 0.1299 0.0537 0.1299 0.1299 0.0537 0.0239 0.4014 0.0537 0.0239 0.3760 4
S(17) 0.0576 0.0254 0.0298 0.0468 0.0565 0.1474 0.2899 0.1936 0.1530 0.9270 30
S(18) 0.0885 0.0414 0.0885 0.1277 0.0324 0.0513 0.0233 0.2006 0.3463 0.4130 6
S(19) 0.0606 0.0273 0.0899 0.0348 0.0329 0.0393 0.3652 0.1361 0.2138 0.5314 11
S(20) 0.0371 0.0371 0.0371 0.0371 0.0865 0.0915 0.1768 0.3200 0.1768 0.7901 24
S(21) 0.2232 0.3704 0.1189 0.1189 0.0272 0.0272 0.0272 0.0598 0.0272 0 1
S(22) 0.0184 0.0184 0.0355 0.1264 0.0624 0.0355 0.1338 0.3653 0.2042 0.5414 12
S(23) 0.0184 0.2587 0.0352 0.0309 0.2290 0.1953 0.1100 0.0687 0.0538 0.8833 26
S(24) 0.0834 0.0345 0.0401 0.0345 0.0345 0.0834 0.3653 0.1820 0.1426 0.6167 15
S(25) 0.1061 0.3463 0.2335 0.1421 0.0519 0.0304 0.0524 0.0203 0.0170 0.3423 3
S(26) 0.0282 0.0282 0.0282 0.0662 0.0459 0.1070 0.1606 0.3839 0.1517 0.6218 16
S(27) 0.0819 0.0372 0.0420 0.3789 0.0216 0.0216 0.0725 0.2305 0.1139 0.3359 2
S(28) 0.0241 0.0684 0.0241 0.0241 0.3413 0.1871 0.0916 0.1871 0.0521 0.6291 18
S(29) 0.0292 0.0672 0.3761 0.2266 0.0769 0.1364 0.0292 0.0292 0.0292 0.5247 9
S(30) 0.0640 0.0640 0.0474 0.1333 0.0329 0.0329 0.3781 0.2301 0.0173 0.5278 10
S(31) 0.0386 0.0710 0.0802 0.0386 0.3353 0.0386 0.1766 0.1307 0.0906 0.6258 17
a. LB = Larger-the-better.
5. Discussion
The results are discussed in this section, with pragmatic
implications of design principles for learning tasks, se-
quences of learning tasks in fixed programs, and ways to
create adaptive or personalized programs are addressed
as well.
The sequence of criteria from the easiest to the most
difficult is S(21) → S(27) → S(25) → S(16) → S(12),
and so on. In sequence of the nine criteria are: C(1) Pos-
ture → C(5) Preparing effective visual aids → C(2) Eye
contact → C(3) Gestures → C(6) Explaining visual
aids → C(4) Voice variation → C(9) Closing the speech
→ C(7) Opening the speech → C(8) Organizing & out-
lining the speech body. The results show that C(8) Orga-
nizing the speech, C(7) opening speech and C(9) closing
the speech elicit the most cognitive load in the partici-
pants, implying that ‘the overall structure of what to be
heard’ matters more than anything else because public
speaking also deals with the content they “plan” to talk.
In particular, C(8) (LGRG = 1) takes a much greater leap
than C(7) (LGRG = 0.6516), indicating C(8) alone causes
heavy cognitive load when the participants learn to ac-
quire this skill. Another great LGRG leap takes place

Y. J. LEE 41
Table 4. The 9 criteria’ LGRG values and rankings.
Criteria S(1) S(2) S(3) S(4) S(5) S(6) S(7) S(8) S(9) S(10) S(11)
LBa 0.3162 0.2100 0.3696 0.2528 0.4005 0.3568 0.3531 0.2751 0.3283 0.4326 0.3162
C(1) 0.0948 0.0395 0.0352 0.0416 0.0273 0.0434 0.0312 0.0704 0.0447 0.0719 0.0743
C(2) 0.1292 0.0445 0.0399 0.1094 0.0273 0.0285 0.3531 0.0474 0.1192 0.0315 0.0381
C(3) 0.2087 0.0395 0.0485 0.0416 0.0273 0.0259 0.1304 0.1853 0.0635 0.0936 0.0298
C(4) 0.3162 0.0395 0.0813 0.2528 0.0273 0.2252 0.0337 0.2751 0.0593 0.0324 0.1356
C(5) 0.0550 0.0863 0.0674 0.0416 0.0704 0.0293 0.0207 0.0412 0.0411 0.0480 0.0354
C(6) 0.0743 0.1205 0.1068 0.0416 0.0704 0.0555 0.0388 0.0521 0.0368 0.4326 0.0487
C(7) 0.0290 0.2100 0.0790 0.2528 0.4005 0.1177 0.0741 0.1640 0.1222 0.0261 0.2477
C(8) 0.0443 0.2100 0.03696 0.1094 0.1961 0.3568 0.2188 0.1015 0.03283 0.2307 0.3162
C(9) 0.0486 0.2100 0.1725 0.1094 0.1536 0.1177 0.0993 0.0628 0.1849 0.0333 0.0743
Criteria S(12) S(13) S(14) S(15) S(16) S(17) S(18) S(19) S(20) S(21) S(22)
LBa 0.3353 0.3001 0.3544 0.2706 0.4014 0.2899 0.3463 0.3652 0.3200 0.3704 0.3653
C(1) 0.1551 0.0732 0.0320 0.0174 0.1299 0.0576 0.0885 0.0606 0.0371 0.2232 0.0184
C(2) 0.3353 0.3001 0.0933 0.0174 0.0537 0.0254 0.0414 0.0273 0.0371 0.3704 0.0184
C(3) 0.1980 1.0934 0.1411 0.0320 0.1299 0.0298 0.0885 0.0899 0.0371 0.1189 0.0355
C(4) 0.0354 0.0320 0.0361 0.1377 0.1299 0.0468 0.1277 0.0348 0.0371 0.1189 0.1264
C(5) 0.0314 0.0397 0.0825 0.2706 0.0537 0.0565 0.0324 0.0329 0.0865 0.0272 0.0624
C(6) 0.0452 0.0989 0.3544 0.2706 0.0239 0.1474 0.0513 0.0393 0.0915 0.0272 0.0355
C(7) 0.0857 0.1192 0.0361 0.0458 0.4014 0.2899 0.0233 0.3652 0.1768 0.0272 0.1338
C(8) 0.0466 0.0507 0.1925 0.1377 0.0537 0.1936 0.2006 0.1361 0.3200 0.0598 0.3653
C(9) 0.0672 0.1927 0.0320 0.0706 0.0239 0.1530 0.3463 0.2138 0.1768 0.0272 0.2042
Students S(23) S(24) S(25) S(26) S(27) S(28) S(29) S(30) S(31)
LGRG
(value) Ranking
LBa 0.2587 0.3653 0.3463 0.3839 0.3789 0.3413 0.3761 0.3781 0.3353
C(1) 0.0184 0.0834 0.1061 0.0282 0.0819 0.0241 0.0292 0.0640 0.0386 0 1
C(2) 0.2587 0.0345 0.3463 0.0282 0.0372 0.0684 0.0672 0.0640 0.0710 0.1734 3
C(3) 0.0352 0.0401 0.2335 0.0282 0.0420 0.0241 0.3761 0.0474 0.0802 0.2062 4
C(4) 0.0309 0.0345 0.1421 0.0662 0.3789 0.0241 0.2266 0.1333 0.0386 0.3173 6
C(5) 0.2290 0.0345 0.0519 0.0459 0.0216 0.3413 0.0769 0.0329 0.3353 0.0154 2
C(6) 0.1953 0.0834 0.0304 0.1070 0.0216 0.1871 0.1364 0.0329 0.0386 0.2225 5
C(7) 0.1100 0.3653 0.0524 0.1606 0.0725 0.0916 0.0292 0.3781 0.1766 0.6516 8
C(8) 0.0687 0.1820 0.0203 0.3839 0.2305 0.1871 0.0292 0.2301 0.1307 1 9
C(9) 0.0538 0.1426 0.0170 0.1517 0.1139 0.0521 0.0292 0.0173 0.0906 0.3810 7
a. LB = Larger-the-better.
from C(7) (LGRG = 0.6516) to C(9) (LGRG = 0.3810).
These two great gaps make C(7) and C(8) stand out of the
rest of skills. This finding reveals that speech instructors
should take more time in designing the pedagogy when
teaching students how to start a speech and how to present
a speech logically. In addition, more practices are required
in these two skills so as to decrease their cognitive load.
On the other hand, the responding students feel they
have more grasps on C(1) posture, C(5) preparing effec-
tive visual aids, and C(2) eye contact. C(1) generates the
least cognitive load among the tested criteria. Preparing
effective visual aids ranks at the second place, implying
that the creation of visual aids is a relatively an easy task
to them, perhaps due to the fact that they are a generation
immersed in the technology era. Eye contact, ranked 3rd,
has a similar nature to posture—it is related to how to act
on stage. In particular, C(1) and C(5), as the two easiest
tasks, have very close LGRG values, i.e. 0 and close to 0
respectively, and their LGRG values lead the rest of cri-
teria’s LGRG values by a large margin. This finding in-
dicates that, as C(1) and C(5) elicit the least amount of
cognitive load, speech instructors should start teaching

Y. J. LEE 42
Table 5. The resulted grey-based student-construct (S-C) matrix.
C(1) C(5) C(2) C(3) C(6) C(4) C(9) C(7) C(8)
S-C 0 0.0154 0.1734 0.2062 0.2225 0.3173 0.3810 0.6516 1
LGRG
(value)
S(21) 0.2232 0.0272 0.3704 0.1189 0.02720.1189 0.0272 0.0272 0.0598 0
S(27) 0.0819 0.0216 0.0372 0.0420 0.02160.3789 0.1139 0.0725 0.2305 0.3359
S(25) 0.1061 0.0519 0.3463 0.2335 0.03040.1421 0.0170 0.0524 0.0203 0.3423
S(16) 0.1299 0.0537 0.0537 0.1299 0.02390.1299 0.0239 0.4014 0.0537 0.3760
S(12) 0.1551 0.0314 0.3353 0.1980 0.04520.0354 0.0672 0.0857 0.0466 0.3864
S(18) 0.0885 0.0324 0.0414 0.0885 0.05130.1277 0.3463 0.0233 0.2006 0.4130
S(10) 0.0719 0.0480 0.0315 0.0936 0.43260.0324 0.0333 0.0261 0.2307 0.4410
S(5) 0.0273 0.0704 0.0273 0.0273 0.07040.0273 0.1536 0. 4005 0.1961 0.4998
S(29) 0.0292 0.0769 0.0672 0.3761 0.13640.2266 0.0292 0.0292 0.0292 0.5247
S(30) 0.0640 0.0329 0.0640 0.0474 0.03290.1333 0.0173 0.3781 0.2301 0.5278
S(19) 0.0606 0.0329 0.0273 0.0899 0.03930.0348 0.2138 0.3652 0.1361 0.5314
S(22) 0.0184 0.0624 0.0184 0.0355 0.03550.1264 0.2042 0.1338 0.3653 0.5414
S(7) 0.0312 0.0207 0.3531 0.1304 0.03880.0337 0.0993 0.0741 0.2188 0.5666
S(6) 0.0434 0.0293 0.0285 0.0259 0.05550.2252 0.1177 0.1177 0.3568 0.5780
S(24) 0.0834 0.0345 0.0345 0.0401 0.08340.0345 0.1426 0.3653 0.1820 0.6167
S(26) 0.0282 0.0459 0.0282 0.0282 0.10700.0662 0.1517 0.1606 0.3839 0.6218
S(31) 0.0386 0.3353 0.0710 0.0802 0.03860.0386 0.0906 0.1766 0.1307 0.6258
S(28) 0.0241 0.3413 0.0684 0.0241 0.18710.0241 0.0521 0.0916 0.1871 0.6291
S(1) 0.0948 0.0550 0.1292 0.2087 0.07430.3162 0.0486 0.0290 0.0443 0.6485
S(3) 0.0352 0.0674 0.0399 0.0485 0.10680.0813 0.1725 0.0790 0.3696 0.6770
S(11) 0.0743 0.0354 0.0381 0.0298 0.04870.1356 0.0743 0.2477 0.3162 0.6865
S(9) 0.0447 0.0411 0.1192 0.0635 0.03680.0593 0.1849 0.1222 0.3283 0.7361
S(13) 0.0732 0.0397 0.3001 0.0934 0.09890.0320 0.1927 0.1192 0.0507 0.7723
S(20) 0.0371 0.0865 0.0371 0.0371 0.09150.0371 0. 1768 0.1768 0.3200 0.7901
S(15) 0.0174 0.2706 0.0174 0.0320 0.27060.1377 0.0706 0.0458 0.1377 0.8240
S(23) 0.0184 0.2290 0.2587 0.0352 0.19530.0309 0.0538 0.1100 0.0687 0.8833
S(14) 0.0320 0.0825 0.0933 0.1411 0.35440.0361 0.0320 0.0361 0.1925 0.9032
S(8) 0.0704 0.0412 0.0474 0.1853 0.05210.2751 0.0628 0.1640 0.1015 0.9190
S(4) 0.0416 0.0416 0.1094 0.0416 0.04160.2528 0.1094 0.2528 0.1094 0.9260
S(17) 0.0576 0.0565 0.0254 0.0298 0.14740.0468 0.1530 0.2899 0.1936 0.9270
S(2) 0.0395 0.0863 0.0445 0.0395 0.12050.0395 0.2100 0.2100 0.2100 1
the two speech skills, from how to maintain a good pos-
ture and how to prepare visual aids, for students to
quickly establish confidence in the beginning of the
course.
The sequence of criteria from the easiest to the most
difficult (Posture → Preparing effective visual aids →
Eye contact → Gestures → Explaining visual aids →
Voice variation → Closing the speech → Opening the
speech → Organizing & outlining the speech body)
seems to indicate that motor skills are widely regarded as
easy techniques to master by the participants, while the
structure of the speech content poses more challenges to
the participants. This finding coincides with Piaget’s
outline [28-30] of the course of human intellectual de-
velopment from concrete experience or direct perception
to abstraction or formal thinking. Hence, it can never be
overemphasized that the teacher should constantly
strengthen the practices on aspects such as posture, ges-
ture, voice variation in the classroom to weaken the stu-
dents’ worries and anxieties in public speaking. In this
way, students would feel better prepared when they
tackle more challenging criteria (e.g., organization of the
speech).
The amount of time required to profess a skill may be
another plausible explanation of the sequence where
motor skills tend to be regarded as easy techniques to

Y. J. LEE 43
master while the structure of the speech content is not.
Unlike the motor skills, which can be acquired or fixed in
a short period of time, to be professed in composing a
well-structured speech is an ability which takes long-
term planning and long time to cultivate. Hence, the re-
sponding students do not feel that their organizational
ability in devising a speech content can be improved
quickly in one single speech course alone. The partici-
pants’ heavy cognitive load in the beginning, body and
ending of a speech composition is more a reflection of
their overall English ability than a reflection of their pub-
lic speaking ability. Knowing how to compose a speech
with a sound structure may actually be one kind of a pri-
ori knowledge, which gravely affects the status of cogni-
tive load in students’ public speaking. As the findings
reflect C(8) Organizing and outlining the speech body
and C(7) Opening the speech to be inadequate in the par-
ticipants’ a priori skills, remedial treatment should be
provided to strengthen them.
More importantly, this result offers a new considera-
tion of rearranging the sequence of taught materials in an
English public speech class. Many speech textbooks cur-
rently available on the market starts from a general in-
troductory section, then deals with the verbal and struc-
tural aspects of the speech, and then teaches delivering
skills (including using visual aids), and finally ends with
a section delineating different types of speech purposes.
The result of this study challenges the prevalent sequence
of contents in most textbooks.
From the most difficult to the easiest, Table 5 dis-
plays the cognitive load in sequence of the thirty-one
students as S(21) → S(27) → S(25) → S(16) → S(12),…,
→ S(2). The participant S(2) located on the bottom of the
consolidated matrix shared the consensus of what most
participants have felt toward the difficulty levels of the
nine criteria of skills; whereas the participant S(21) lo-
cated on the top indicated the least consensus with other
fellow students. In other words, S(21) perceives the
learning cognitive load significantly different from others.
It is worth noting that S(21) has low LGRG values in all
criteria except for two criteria—Eye contact and Postu re,
implying that S(21) feels comfortable about most of the
English speech delivery skills but considers these two
motor criteria significantly consuming her/his cognitive
capacity. Accordingly, the teacher may offer individual-
ized practices for S(21) by fostering eye contact and
body posture skills. A focus on simply a couple of skills
warrants enhancement of the participant’s overall per-
formance enormously.
6. Conclusions
This study systematically combines AHP and GRA to
investigate the EFL students’ perceptions in English
public speaking training. Nine speech criteria repre-
senting the student competence in English public speech
were extracted from a best-selling textbook Speaking of
Speech (New/e) in Taiwan. Some 31 EFL-major univer-
sity seniors in English Presentation Training class par-
ticipated in the survey. The data analysis employed the
Hierarchy Grey Relational Analysis (HGRA) to analyze
the participants’ perceived confidence in the criteria,
sorted the data in order, and then located each partici-
pant’s perceptions in a ranking matrix. The results from
the easiest to the most difficult are sequenced in terms of
the 31 participants S(21) → S(27) → S(25) → S(16) →
S(12),…, → S(2) and in terms of the nine criteria Posture
→ Preparing effective visual aids → Eye contact →
Gestures → Explaining visual aids → Voice variation →
Closing the speech → Opening the speech → Organizing
& outlining the speech body.
Responding to students’ feelings is important. By in-
viting students to express their cognitive load levels in
learning targets, the teacher can help them overcome
negative feelings that might otherwise obstruct their
learning with a curriculum that corresponds to their cog-
nitive load. When students’ anxiety is reduced and
self-confidence is boosted, skill acquisition is achieved
more easily [3,31]. Competence and confidence will then
be built in a positive cycle.
Based on the empirical results of this study, it can be
summarized as follows. The teacher should start the
training from posture and visual aids production for the
students to have a sense of achievement, and refine the
pedagogical skills with more emphases on the logical
organization of the main points and attention getters in
the beginning of the speech to strengthen a priori knowl-
edge in these regards. To establish automatic skill sche-
mas, the students should adjust their learning focuses
with more practices on an attractive opening and a logi-
cal development of speech contents. Meanwhile, the
teacher can even offer individualized programs to those
whose cognitive load are largely different from the norm,
i.e. S(21) and S(2).
Thus, this study has contributed to setting priority in
designing a speech course by utilizing the research re-
sults from the cognitive load perspective. The application
of a fine-tuned course design to suit the students’ cogni-
tive load and at the same time giving extra assistance in
establishing skills schemas where the students lack con-
fidence may be conducive to learning motivation and
interest.
In terms of research limitations, first, the proposed
data analysis procedure adopts Nagai’s GRA formula,
whereas other GRA equations can also be experimented
in the future. Second, the proposed data analysis proce-
dure is only attempted in a class of English Presentation
Training in the case university. Third, the predefined
nine criteria of skills were directly adopted from a best-
selling textbook Speaking of Speech (New/e). Finally, the
proposed procedure should be readily applicable to evalu-

Y. J. LEE 44
ate students’ perception data in different aspects of public
speaking for a more comprehensive understanding and
training design of English public speaking. However, the
most appropriate evaluation criteria affecting the cogni-
tive load in different aspects must be reexamined.
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