Creative Education
2012. Vol.3, No.4, 557-564
Published Online August 2012 in SciRes (
Copyright © 2012 SciRe s . 557
Restoring Washed Out Bridges so ELearners Arrive at Online
Course Destinations Successfully
Ruth Gannon Cook
School for New Learning, DePaul Universi ty, Chicago, USA
Received March 5th, 2012; revised April 8th, 2012; accepted April 29th, 2012
This study researched the impact of strategic navigation improvements in an online course selected for the
study over one quarter (12 weeks) at a large Midwestern private university. The primary purpose of the
study was to see if navigation enhancements and specific graphic enhancements (semiotic tools) in the
online course selected for the study could make it easier for adult students to learn new course materials.
The study also sought to see if these factors could contribute to increased positive learning experiences
and to see whether there might be a higher percentage of completion rates in this enhanced online course
than in other online courses at the university. While not generalizable, the findings could provide infer-
ences about which factors could positively influence adult learning in online courses and contribute to in-
creased course completion rates; the study could also provide recommendations on graphic enhancements
and online course navigation that positively influence student learning in online courses.
Keywords: ELearning; Online Learning; Semiotics; Graphic Enhancements; Principal Components
Analysis; Development Design; Adult Learners; Higher Education; Andragogy
Description of Study
This study invest i g a ted specific navi g ation enhancemen t s a n d
specific graphic enhancements (semiotic tools) in one online
undergraduate course to see whether students cou ld grasp course
materials and assignments more readily in the online course
studied. In addition, the study sought to find whether students in
the online stud y co urse had a higher course completion rate th an
the national averages (Nash, 2005) shown for online courses.
The initial investigation sought to look at whether specific
graphic enhancements (semiotic tools) could have sufficient
positive effects on students participating in the online course so
as to demonstrate greater studen t satisfac tion with the course a nd
a higher completion rate than average online course completion
rates in online courses.
For the purposes of this study some terms will be used inter-
changeably, online learning and elearning; other terms, such as
distance education and Internet or web-based learning, are also
exchanged throughout the research study.
Review of Literature
The focus of this study was initially to see whether graphic
enhancements embedded strategically in one online course for
adult learners at a large Midwestern private university could
enlist and en g ag e s tudents s uffi ci en tl y so as to help students feel
more comfortable in the online course, reduce cognitive load to
some degree for those students, and perhaps contribute to a
higher course completion rate for students in that course. The
researcher compared this online course with other home uni-
versity online course completion rates, and compared the na-
tional statistics’ online course completion rates to see if the
strategically embedded graphics and media-enhancements (de-
signed with im planted graphics , metaphors, a nd multimedia ) had
a more positive effect on adult learners in the course and, ulti-
mately, also on learner retention. The researcher also reviewed
literature on adult learners and cognitive load, and on semiotics,
a topic seldom researched with respect to on-line courses and
degree programs. The purpose was to see if specifically em-
bedded graphic enhancements in th e online course could make it
easier for students to learn new course materials and master the
course competencies listed as desired course deliverables (out-
comes). The researcher also sought to see if those factors, such
as the stra te gi cally-embe dded gr aphic enhancements, could also
contribute to increased course completion rates for students
enrolled in the online course of the study.
The literature review also looked at online best practices, and
motivational theory related to student retention. While the re-
view also included factors, such as attrition, work, family re-
sponsibilitie s, financ ial stress es, a nd self-regulation, the fo cus of
the study was on online learning, course navigation and course
tools factored into the cognitive load of students in those
Adult Learners and Cognitive Load
Adult Learners
Several studies (Bradburn, Berger, Li, Peter, & Rooney, 2005;
Hudson & Shafer, 2005) of students enrolled in undergraduate
programs found that while a majority of undergraduates in tra-
ditional degree programs were younger than 24, one in four
students was actually 30 or older. About 43 percent of under-
graduates enrolled in postsecondary education in the United
States were age 24 or older (National Center for Education
Statistics, 2007). At least 36 percent of postsecondary students
were age 25 or older and 47 percent were independent students
(Center for Postsecondary and Economic Success, 2011). (The
students in participating in this study were at least 24 years of
age (University data, 2012). design, the course navigation, and the learning management
system of each course. So techniques can be applied to manage
complexity in online courses by segmenting and sequencing
complex materials, and instructional designers can limit extra-
neous load and promote germane load (Paas, 2004).
Cognitive Load
Cognitive l oad theory (Sw eller, 1988 , 1994) is an instruction al
theory… (that) describes learning structures in terms of an in-
formation processing system involving long term memory,
which effectively stores all of our knowledge and skills on a
more-or-less permanent basis and working memory, which
performs the intellectual tasks associated wsith consciousness.
Information may only be stored in long term memory after first
being attended to, and processed b y, working memory. Working
memory, however, is extremely limited in both capacity and
duration… Th e fundamental tenet of cogn itive load the ory is that
the quality of instructional design will be raised if greater con-
sideration is given to the role and limitations, of working mem-
ory (Cooper, 1998: p. 1).
Cognitive load is a term th at wa s fir st u sed b y Joh n Swe ller to
describe the amount of pressure related to the mind and its
working memory (WM) (Sweller, 1988, 1994). The theory as-
sociated with cognitive load contends that the more stress, ac-
tivities, and information are added to a person’s short term
memory, the more that person has difficulty processing and
retaining the information and becomes overwhelmed by too
much information. There are varying numbers as to how much
information becomes too much, before overload. There seems to
be a general consensus of around seven pieces or chunks of
information th at the mind can retain befor e a min d begi ns to f eel
overloaded and begins to experience greater stress (Miller,
People learn better when they can scaffold new knowledge
on existing schemas, what they already understand (Vygotsky,
1962, 1978, 1981; Wertsch, 1985). But the more a person has to
learn in a shorter amount of time, the more difficult it is to
process that information in working memory. Cognitive over-
load is often difficult to identify in students, particularly online
students, because they generally cannot be directly observed in
an online course, unless the course is taught in a synchronous
environment. Students can usually be monitored solely by the
instructor auditing their participation in online course discus-
sions and other interactivities, by assessing their homework
assignments, by evaluating their emails and interactions with
the instructor, and, ultimately, by the students’ successful (or
unsuccessful) completion of the course. The researcher felt it
was critical to review cognitive load and its’ effects on students
in online courses, so that factors that produced student overload
might be identified, and factors that had positive effects on
student learning could also be identified.
Semiotics and Graphic Enhancements
Semiotics is the study of patterned human communication
behavior, including: auditory/vocabulary, including writing and
narratives; language; numerical figures; proxemics, such as
facial expressions, touch, and artificial limb extensions; meta-
phors, signs, and symbols (Merriam-Webster, 2009).
Several types of cognitive load have come to be identified:
Intrinsic cognitive load, which was first used by Chandler and
Sweller (1991) and was described as being the inherent diffi-
culty of learning material that is integral to the material that
may not be altered or changed (by an instructor, parent, leader,
etc.). Extraneous cognitive load is external information pre-
sented to individuals which is controlled by the parties deliver-
ing that information. In education it would be the curricula
designers, teachers, instructional designers, and administrators.
Because extraneous cognitive load usually consists of specific
limited cognitive resources, learning materials can be designed
to reduce the extraneous load with restrictions on the volume of
information provided in those materials. Germane cognitive
load is that aspect of cognitive load that relates to the “process-
ing, construction and automation of schemas,” (Wikipedia,
2012, Cognitive Load). Schemas, as described in the definition
of germane cognitive load, are thought patterns, or mental
knowledge frameworks, usually with specific themes that or-
ganize social information (2012, Schemas); schemas provide
structures to organize and help facilitate interpreting and proc-
essing information.
Earlier research revealed that learning occurred through a
continual interplay between cognitive and affective factors
(Gannon-Cook, 1998; Gannon-Cook & Crawford, 2007; Pin-
trich, Marx, & Boyle, 1993); research also brought to light that
students who were successful in their studies attributed partial
success to structured narration that consisted of story structure
and schema which could include semiotic elements (Mandl et
al., 1984).
But semiotic elements, such as signs, metaphors, and narra-
tives may have multiple meanings, so there would need to be
careful study of which signs, symbols, etc., to choose in each
online course design in order to include semiotic elements that
were generic enough to have almost universal recognition.
(Examples of universal symbols could be a boat sailing on wa-
ter to represent navigation, a family having dinner talking and
eating around a table, people sitting around a campfire, hikers
with backpacks walking down a hiking trail, a mother or family
playing with children, etc.).
Students’ cultural and historical recognition patterns varied
significantly, depending on many factors: locale, cultural and
family traditions, socioeconomics, educational experiences, and
learning styles; and students could also be affected by the
omission of any of these factors because the absence of any
factor could result in a lack of a connection or bridge to prior
knowledge that created a virtual diaspora (Gannon-Cook, 2007,
2008; Popkewitz, 1997, 1998, 2004). This lack of bridging to
prior knowledge could present greater challenges for online
students since they must jump directly into new course materi-
als, as well as learn the online technology, the learning man-
agement system, and the course navigation. “The immersion of
students into new content material establishes new patterns of
Schemas can contribute to the enhancement or impediment
of learning. Chandler and Sweller (1991) pointed out that the
format of instructional materials could also either promote or
limit learning, therefore differences in learning (and perform-
ance) could be due to higher levels of cognitive load. So, de-
pendent upon the format of instruction, students could learn
effectively… or fail to learn. Chandler and Sweller termed this
“extraneous cognitive load” (p. 235) to describe factors outside
the teacher and student that introduce artificially induced cog-
nitive load, so, in online learning, extraneous factors could
include not only the online course content, but the instructional
Copyright © 2012 SciRe s .
Copyright © 2012 SciRe s . 559
exclusion that perpetuate an unequal educational playing”
(Popkewitz, 1997: p. 27).
Semiotics, when included in the design of online courses,
could have implications as far as learner retention because these
symbolic representations, when strategically embedded into
online courses, could resonate with students by providing sub-
liminally recognizable symbols that bridge to their prior knowl-
edge and cultural experiences. If specifically embedded graphic
enhancements could make it easier for students to learn new
course materials, it would be worth including these semiotic
elements because these additions could also contribute to better
understanding of course materials and lead to increased student
understanding. Ultimately it could also contribute to increased
course completion rates for students enrolled in the course of
the study.
One of the findings uncovered in the research is students’
constructions of knowledge that are based on their participation
(or lack of participation) in historically derived systems of rea-
soning that are not included in online course templates or con-
tent materials (Popkewitz, 1997). Since online courses usually
focus solely on the specified content material, there could be
risks for students that need some of that undergirding of his-
torical or representational meaning; students could actually add
to their cognitive load by feeling overwhelmed with a sense of
something missing, even as they are still exposed to all of the
new materials and activities in the online courses.
Ironically, most online courses are still highly text-based;
these courses are still mediated in the same ways, so there is a
need to return to an inclusion of historical and semiotic artifacts
as social forces to assist students, particularly those returning to
school, at-risk, or developmentally challenged, to feeling more
comfortable with the new knowledge on a deeper, metalinguis-
tic level. Popkewitz (1997) points out that “The eye doesn’t just
see but is socially disciplined in the ordering, dividing, making
of the possibilities of the world and self” (p. 20).
Lashley (1951), argued that (learning) materials appear to
generally be organized in linear and flat fashion, but this format
also conceals an underlying hierarchical structure. Thus se-
quences can consist of sub-sequences and these can in turn
consist of sub-sub-sequences. While linear representation and
sequencing is simple from storage point of view, there can be
potential problems during retrieval. For instance, if there is a
break in the sequence chain, something missing, subsequent
elements become inaccessible. Sakai et al. (2003) showed that
subjects spontaneously organize information into a sequence
chain, then into a number of chunks across a few sets; he also
showed that performance was poorer when the sequencing
chunks were disrupted or random.
Note: In Figure 1 there are at least nine online course activi-
ties shown here that students must use; they may approach
these one at a time, or they may become subsequently more
frustrated and become cognitively overloaded as they try to
perform each activity (If, as research has indicated, it only takes
approximately seven pieces of information or activity and cog-
nitive overload sets in, students could be entering cognitive
overload from the very beginning of an online course).
If students are introduced to the new online course with a
“bridge” of information that contains recognizable symbols,
metaphors, or narratives to remind them of their knowledge
base, then they have a passage that may help them feel more
comfortable with taking on new knowledge and experiences
because they can scaffold these onto their own experiences
they’ve carried with them into the course.
Cooper (1998) gives an example of the challenges students
face when having to integrate multiple sources of mental and
sensory input in learning environments; other studies concur
that students can feel bombarded with too much information
and requirements, particularly in new educational settings
(Ambron & Hooper, 1990). In online courses, students not only
need to learn the new course materials, but also have to know
or learn how to use the technology in order to navigate the
course effectively. Couple all of this with the processes related
to higher-order thinking, such as problem-solving, knowledge
transfer, and the complexities of learning, and a multiplier ef-
fect occurs that can exacerbate the effects of all these concur-
rent activities on learners. At this point an online task with two
or three simple steps could then cause frustration and negative
reactions for a learner with cognitive over-load caused by the
complex skills required for taking those steps (Cooper, 1998;
Pavio, 1990; Baddeley, 1992).
In the online learning environment “the social is presented as a
psychological or symbolic interactional process of negotiations
Figure 1.
Cognitive load activities for students in online courses.
to construct knowledge… but (one) which strips (away)… any
social mooring outside the classroom In the case of online
learning, the exclusions consist of semiotic tools and sociocul-
tural influences from the world outside the course. “For exam-
ple, how the concepts and methods of science, social science
and literature are embedded in historical relations and power
structures is not studied.” (Popkewitz, 1998: p. 551).
Since college dropout rates are consistently higher for minor-
ity and underserved students (National Center for Educational
Statistics, 2008), there could be a correlation between the ab-
sence of cultural and historical anchors and academic under-
performance, so it is important to take a look at finding a way
to introduce tools, such as signs, metaphors, narratives, that
help the learner reinforce her or his cultural and historical an-
chors. It would be sad to learn that something as simple as in-
troducing graphics and metaphors to reinforce students’ com-
fort zones could help them enter the online course more ready
for the challenges of the course and more prepared to complete
the course.
Analysis of Data
The researcher conducted a research study to see if there
could be a positive impact on students in online courses with
strategic navigation improvements, both in the course, and in
increased student course completion rates. The researcher used
a mixed methods methodology, first using quantitative research
to collect data from an electronic survey sent out to students
enrolled in both the graphically enhanced course and twenty-
four other online courses. The researcher then collected the data
into a Principal Components Analysis (PCA) to distill which
factors could have a positive impact on the online learners.
Then the research utilized a qualitative methodology, using
development design to assess whether the graphic enhance-
ments, or other innovations, could improve students’ perform-
ance in the online course.
The study was conducted over one quarter (11 weeks) at a
large Midwestern private university. The primary purpose of
the study was to see if navigation enhancements and specific
graphic enhancements (semiotic tools) in the online course
selected for the study could make it easier for students to learn
new course materials; the researcher would also look at the
final course completion rates to see if the completion rates may
be higher with students who had taken the graphically enhanced
The sample population for this study was adult learners (stu-
dents at least 24 years of age).
After receiving permission to administer the survey, it was
pretested in the summer of 2007 with 25 student participants
(see Appendix A). In the fall of 2007 it was sent out to students
in half of the courses (25) offered by the online university pro-
gram with enrollments ranging from 12 to 20 students in each
course, including one course designed with semiotic enhance-
ments (25 students). Of the approximate 500 students sent the
email request, 149 students participated in the survey, 30% of
the potential respondents.
After collection of the raw data, tests were then conducted to
validate the survey data in the early winter 2008 quarter (Ta-
bachnick & Fidell, 2001). Issues, such as sample adequacy,
missing data, data set fit to methodology were addressed; the
methodologies utilized in the study included both quantitative
and qualitative research.
The study summarized the descriptive data and provided a
detailed analysis of the twenty-three Likert-scale questions
ranging from 1 (strongly disagree) to 5 (strongly agree). The
questions were coded with an identifying code for each ques-
tion (variable) and analyzed to see if there were any correla-
tions among the variables.
Principal Components A nalysis (PCA)
The data was then analyzed using a principal components
analysis (PCA), the statistical technique that derives the prob-
able number and nature of the survey factors (Merenda, 1997).
The PCA combined the variables into groups (factors) which
“reflected (the) underlying processes that have created the cor-
relations among variables” (Tabachnick & Fidell, 2001: p. 582).
The factors were distilled to arrive at a new set of fewer vari-
ables from which inferences could be made (Cabrera, 1994;
Colbeck, Cabrera, & Marine, 2002). A Cronbach alpha, used
when items on a measure are not scored dichotomously (Gall,
Borg & Gall, 1996; Wiersma & Jurs, 1990), computed test
score reliability with an estimate of internal consistency of
greater than .70 alpha levels (Shiarella, Harris, McCarthy &
Tucker, 2000).
While the PCA is designed to reduce a large number of vari-
ables to a smaller number of variables (McDonald, 1985; Ta-
bachnik & Fidell, 2001); the PCA works with a smaller set of
new criterion variables (Stevens, 1996), capturing all of the
data in a few vectors (Houle, Mezey, & Galpern, 2001). The
original 16 survey questions (components) were reduced to six
factors, only four of which met the criterion for meaningful
correlations (.32). These accounted for 65% of the variance:
Instructor Interaction (Factor 3), .92; Graphics, Podcasts, &
Phone Conferences (Factor 2), .85; Navigation and Explana-
tions (Factor 1), .78; and Learning Styles (Factor 4), 70 (Ap-
pendix B).
Students had taken the time to respond to the survey and
many had written responses in the open-ended questions, so it
was important to look at these factors to see which elicited the
strongest student responses because these could provide in-
sights into which factors induced more student stress and which
ameliorated it. Factor 1, Instructor Interaction, spoke to the
need for students to feel their voices were heard by the instruc-
tor and university, also that their questions and concerns were
addressed expeditiously. The next factor, Factor 2, including
graphics to bridge cultural knowledge (which also included
voice over Internet protocols, such as Skype, and podcasts),
students ranked highly because they seemed to feel that the
graphics and semiotic tools helped them to better connect with
the instructor, other students, and with the new content materi-
als. Factor 3, Navigation and Explanations, could assist the
students to become more comfortable with finding materials
and navigating through the courses with consistency to help
minimize the anxiety and stress of not locating strategic course
items. Last, Factor 4, Learning Style Factors, could be ad-
dressed more comprehensively so that instructional designers
could address and implement measures to help students with
activities and features that included their learning styles.
There were three open-ended questions, Questions 22, 17 and
23 that allowed students to discuss their feelings and needs
which were not included in the PCA. Question 17 asked the
question, Are there any other thoughts that you would like to
share about your on-line learning experiences? Here are some
Copyright © 2012 SciRe s .
of the 91 responses (58 either did not respond or said “no”): 31
responded they were happy with their online learning (10 of
these did add other comments, such as, “would like more hy-
brid courses”, “would like more timely responses from instruc-
tors”, and “would like more interactivities with and control by
instructor”. Interestingly, only three students mentioned they
would like more graphics and student aids in their courses, and
only one student commented that (s)he would not take any
more online courses or continue with the program. Question 22,
What Do You Like or not like about Online Courses?, posed
the answers “Freedom to Learn at my own pace, Time savings,
Online courses are easier, and, Other ” which allowed students
to respond with three answers or add their own. One hundred
thirty-two students responded they liked the freedom to learn at
their own pace, eight liked the time savings, five thought online
courses were easier (which did not identify whether easier
meant more convenient or easier learning materials), two did
not respond, and only two responded that online courses were
less stressful.
Question 23 asked “If you have any other comments to share,
such as one of the comments below, please do so here.” (The
survey question also posted some suggested responses, see
Table 1):
23. Do you have any other comments to share, if so, please
do so here (the survey question also posted some suggested
responses, see below):
a) It is up to my own determination to complete the course
b) More online chats and interactivities
c) More online study aids more help from instructor
Only ten students did not respond to the open-ended question
23; the fact that 139 students felt it important enough to re-
spond made a statement that they did want their thoughts to be
“heard” or represented. Eighty-seven felt they were pressing
forward with their degree progress through their own efforts,
another 20 felt they could use the help of additional graphics
and study aids, eighteen wanted more interaction from their
instructors, and 14 wanted more podcasts, Skype or phone con-
Development Design Re s ea rch
The qualitative methodology utilized in the study was a devel-
opmental research design (Richey & Klein, 2007). “Design and
development research seeks to create knowledge grounded in
data systematically derived from practice” (Richey & Klein,
2007: p. 1). The primary method utilized was a content analysis
of the survey data with an in-depth review of the open-ended
survey questions. The researcher identified contextual factors
and existing philosophies, and conditions that enhanced or in-
hibited the questions used in the survey, including any noted in
Table 1.
Question 23 student responses.
Study aids and graphics 20
More help fr om instruc t or 18
More chats and interactivities 14
(Succeed) Through my own determination 87
Blanks (no response) 10
Total 149
the students’ open-ended responses (Forsyth, 1998; Richey &
Klein, 2007). The research was designed to look at the “impact
of the many factors that exist in the natural workplace (envi-
ronment), factors that are eliminated in most laboratory re-
search… which give(s) design and development researchers the
opportunity to draw conclusions based upon a dataset that is not
only realistic, but (is) also isolated from irrelevant details” (p.
80). The researcher’s observations supported the findings of the
quantitative survey in several key areas: first, that students
seemed to be more interested in interaction with the instructor
and desirous of meaningful feedback, not just “good job” or
“receipt acknowledged”; despite so many students having ex-
posure to using personal email and texting, even technologi-
cally sophisticated students seemed confused and frustrated by
inconsistent information and formatting in their online courses;
some students indicated they would very much like more
graphics and pictorial representations, such as screen captures
and visual aids.
The development design research included a review of some
of the courses in the survey so that the researcher could search
for elements that could positively affect or potentially frustrate
students in virtual environments, as well as a search for ele-
ments that could include aspects of students’ cultural environ-
ments. These aspects would not be observable in the survey
data, so the researcher looked at whether the instructional de-
signers had included semiotic and sociocultural elements that
might be embedded in the actual courses being studied.
Design and development theory “can be informed by con-
textualized findings as well as those generated by data derived
from traditional sampling techniques” (Richey & Klein, 2007: p.
130). This type of data can provide important insights, such as
the large number of respondents who took the time to voice
their thoughts about questions raised in the survey, and did so
in the discussion threads of the courses as well as in the open-
ended survey questions. The findings suggested that the human
need for approval and to be heard were very much present in
these students’ minds, even more so than just their expressed
desires for quick response time or for more technologies, al-
though some did indicate they would like more podcasts and
Skype sessions.
In summary, the study researched adult-learner cognitive
load in online courses and compared text-based online courses
(traditional courses transferred to online formats that do not
contain graphics, metaphors, or multimedia) and graphics and
media-enhanced online courses with embedded graphics, me-
taphors, or multimedia (Cobley, 1997) to see which had a more
positive effect on adult-learner cognitive load and, whether this
load could affect learner retention. The variables found to best
predict how to regulate cognitive load and reduce attrition were
recommended for inclusion in future instructional online course
designs at the Midwestern private university. While the study
was too small to be generalizable, the findings could still pro-
vide valuable insights into which factors could reduce student
cognitive load, reinforce student retention in online courses,
and contribute to the body of knowledge on elearning in post-
secondary education.
Ambron, S., & Hooper, K. (Eds.) (1990). Learning with interactive
Copyright © 2012 SciRe s . 561
Copyright © 2012 SciRe s .
multimedia: Developing and using tools in education. Washington:
Microsoft Press.
Baddeley, A. D. (1992). Working m emory. Science, 255, 556-559.
Berker, A., & Horn, L. (2005). Work first, study second: Adult under-
graduates who combine employment and postsecondary enrollment.
The Education Statistics Quarterly, 5, 2004.
Bradburn, E., Berger, R., Li, X. Peter, K., & Rooney, K. (2004). A
Descriptive summary of 1999-2000 bachelor’s degree recipients 1
year later: With an analysis of time to degree. The Education Statis-
tics Quarterly, 5.
Cabrera, A. (1994). Logistic regressi on analysis in higher educa t i on: An
applied perspective. In J. C. Smart (Ed.), Higher education: Hand-
book of theory and research (pp. 225-256). New York: Agathon
Center for Law and Social Policy (2011). Statistics on nontraditional
learners. URL.
Chandler, P., & Sweller, J. (1991). Cognitive load theory and the for-
mat of instruction. Cognition and Instru c ti o n , 8, 293-332.
Chang, S. (2006). Online versus print reading: A cognitive load per-
spective. Association for Education Communications and Technol-
ogy, Dallas, October 2006.
Cobley, P., & Jancz, L. (1997). Introducing semiotics. Royston: Icon
Colbeck, C., Cabrera, A., & Marine, R. (2002). Faculty motivation to
use alternative teaching methods. The Annual Meeting of the Ameri-
can Educational R esearch Association, New Orleans, April 2002.
Cooper, G., (1998). Research into cognitive load theory and instruct-
tional design at University of New South Wales (UNSW). URL (last
checked 1 December 2006 ).
Forsyth, J. E. (1998). The construction and validation of a model for the
design of community-based train-the-trainer instruction. Ph.D. Thesis,
Detroit, MI: Wayne State University.
Gall, M. D., Borg, W. R., & Gall, J. P. (1996). Educational research.
(60th ed.). White Pl a i ns , NY: Longman.
Gannon-Cook, R. (1998). Semiotics in technology, learning and culture.
Bulletin of Science, Technology & Society, 18, 174-179.
Gannon-Cook, R. (2008). Web 2.0: How signs, symbols and podcasts
affect elearning. In T. Kidd, & I. Chen (Eds.), Wired for learning: An
educator’s guide to Web 2.0. Charlotte, NC: InfoAge Publishing.
Gannon-Cook, R., & Crawford, C. (2007). What can cave walls teach
us? In A. Edmondso, (Ed.), Globalized E-Learning Cultural Chal-
lenges. Minneapolis, MN: eWorld Learning.
Gobet, F., Lane, P. C. R., Croker, S., Ch en g, P. C. H. , Jones, G., Oliver,
I., & Pine, J. M. (2001). Chunking mechanisms in human learning.
Trends in Cognitive Sciences, 5, 236-243.
Houle, D. Mezey, J., & Galpern, P. (2001). Interpretation of the results
of common principal components analyses. Evolution, 56, 433-440.
Hudson, L., & Shafer, L. (2005). Undergraduate enrollments in aca-
demic, career, and vocational education. The Education Statistics
Quarterly, 6.
Lashley, K. S. (1951). The problem of serial order in behavior. In L. A.
Jeffress (Ed.), Cerebral mechanisms in be havior. New York: Wiley.
Mandl, H., Stein, N., & Trabasso, T. (Eds.) (1984). Learning and com-
prehension of text. Hillsdale, NJ: Lawrence Erlbaum Associates.
Maybery, M. et al. (2001). Grouping of list items reflected in the timing
of recall: implications for models of serial verbal memory. Journal of
Memory and Language, 47, 360-385.
McDonald, R. (1985). Factor analysis and related methods. Hillsdale,
NJ: Lawrence Erlbaum Associates Publishers.
Merenda, P. (1997). Methods, plainly speaking. A guide to the proper
use of factor analysis in the conduct and reporting of research: Pit-
falls to avoid. Measurement and Evaluation in Counseling and De-
velopment, 30, 156-164.
Merriam-Webster’s Dictionary Online (2009). Definition of discourse.
URL (last checked 20 July 2 0 09).
Miller, G. A. (1956). The magical number deven, plus or minus two:
Some limits on our capacity for processing information. Psychologi-
cal Review, 63, 81-97. doi:10.1037/h0043158
Nash, R. (2005). Course completion rates among distance learners:
Identifying possible methods to improve retention. Online Journal of
Distance Learning Administration, 8. URL.
National Center for Education Statistics (NCES) (2002). A profile of
participation in distance e ducation: 1999-2000. URL (last checked 15
January 2002).
National Center for Educational Statistics (NCES) (2007). The condi-
tion of education 2006: Distance education by postsecondary faculty.
Washington DC: Institute of Educational Science, National Center
for Educational Statistics.
Paas, F., Renkel, A., & Sweller, J. (2004). Cognitive load theory: In-
structional implications of the interaction between information struc-
tures and cognitive architecture. Instructional Science, 32, 1-8.
Pavio, A. (1990). Mental representations: A dual coding approach.
New York: Oxford University Press.
Pintrich, P. R., Marx, R. W., & Boyle, R. A. (1993). Beyond cold con-
ceptual change: The role of motivational beliefs and classroom con-
textual factors in the process of conceptual change. Review of Educa-
tional Research, 63, 167-199.
Popkewitz, T. (1997). A changing terrain of knowledge and power: A
social epistemology of educational research. Journal of Educational
Research, 26, 18- 29.
Popkewitz, S. D. (1998). Review of the effects of research and practice.
Journal of Educational R esearch, 35, 535-570. doi:10.2307/1163459
Popkewitz, T. (2004). The alchemy of the Mathematics curriculum:
Inscriptions and the fabrication of the child. American Educational
Research Journal, 41, 3-34. doi:10.3102/00028312041001003
Richey, R., & Klein, J. (2007). Design and development research.
Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.
Sakai, K., Kitaguchi, K., & Hikosaka, O. (2003). Chunking during hu-
man visuomotor sequence learning. Experimental Brain Research,
152, 229-242. doi:10.1007/s00221-003-1548-8
Shiarella, A. H., McCarthy, A. M., & Tucker, M. L. (2000). Develop-
ment and construct validity of scores on the community service atti-
tudes scale. Educational and Psychological Measurement, 60, 286-300.
Stevens, J. (1996). Applied multivariate statistics for the social sciences
(3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Publishers.
Sweller, J. (1988). Cognitive load during problem solving: Effects on
learning. Cognitive Science, 12, 257-285.
Sweller, J. (1994). Cognitive load theory, learning difficulty and in-
structional design. Learning and Instruction, 4, 295-312.
Tabachnick, B., & Fidell, L. S. (2001). Using multivariate statistics
(4th ed.). New York: HarperCollins College Publishers.
University Data (2012). Adult student requisites. URL (last checked 23
February 2012).
Vygotsky, L. S. (1962). Thought and language. Cambridge, MA: MIT
Press. doi:10.1037/11193-000
Vygotsky, L. S. (1978). Mind in society. Cambridge, MA: Harvard Uni-
versity Press.
Wertsch, J. V. (1985). Cultural, communication, and cognition: Vygot-
skian perspectives. Cambridge: Cambridge University Press.
Wiersma, W., & Jurs, S. (1990). Educational measurement and testing.
Needham Heights, MA: Allyn and Bacon.
Wikipedia (2012). Definition of chunking. URL (last checked 24 Feb-
ruary 2012).
Wikipedia (2012). Definition of cognitive load. URL (last checked 23
February 2012).
Wikipedia (2012). Definition of schemas. URL (last checked 23 Febru-
ary 2012).
Appendix A
Comparisons of Course without Graphic Enhancements versus With Graphic Enhancements
Online Course in Learning Management Templates
Without Graphic Enhancements:
With Graphic Enhancements:
Copyright © 2012 SciRe s . 563
Copyright © 2012 SciRe s .
Appendix B
PCA Scree Plot
The first four factors accounted for 65% of the variance: Instructor Interaction (Factor 3) .92; Graphics, Podcasts, & Phone Con-
ferences (Factor 2), .85; Navigation and Explanations (Factor 1), .78; Learning Styles (Factor 4), .70; (Factor 5, Technology skills,
and Factor 6, External Experiences, comprised; a combined 18%, with Factors 7-10 comprising the remaining 17% of the variance).