E–Learning
Volume 6 Number 2 2009
www.wwwords.co.uk/ELEA
Student Perceptions and Cognitive Load: what can
they tell us about e-learning Web 2.0 course design?
JUDY LAMBERT
University of Toledo, USA
SLAVA KALYUGA
University of New South Wales, Sydney, Australia
LISA A. CAPAN
Bowling Green State University, Firelands, USA
ABSTRACT The described study investigated the effectiveness of an e-learning Web 2.0 course
redesigned from the perspective of cognitive load theory. The analyzed variables were course wiki
design features, levels of instructor support, levels of cognitive load and engagement, and values
students placed on particular pedagogical approaches used during instruction. Descriptive statistics
were used to examine potential relationships between students’ prior experience in distance learning
and using technology, anxiety, and engagement. Results suggest that prior experience in distance
education and technology is associated with lower anxiety and higher engagement. Web 2.0
technologies may not impose excessive levels of mental load when intrinsic and extraneous cognitive
loads are reduced sufficiently by providing an organized and clear course design and selecting engaging
materials and activities suitable for different levels of learner expertise. While experienced learners tend
to be more engaged in cognitively challenging activities that require higher level cognitive processes,
novices usually need more scaffolds.
Instructional designers of e-learning environments experience unique challenges unlike those
typically encountered in traditional classrooms (Morrison & Anglin, 2005). E-learning is a broad
term used to describe different forms of learning with a computer. Distance education, as used in
this article, is a form of e-learning that occurs at a computer connected to the Internet where
students and teachers are separated by location and students learn at different times (Simonson et
al, 2006). According to Morrison & Anglin (2005), e-learning instructional design occurs usually
months before student learning takes place, and this separation of design and learning places a
tremendous responsibility on students to develop understanding of course content independently
from instructor support. It also places more responsibility on the instructor to ensure adequate
support is provided for students in a variety of ways. Equally important, students learn about
delivery technologies at the same time they are learning about content. This may place excessive
demands on learners, particularly those who are inexperienced in using technology (Clarke et al,
2005).
Educational technology distance education courses could be even more complex for
designers because in these courses, students must learn to manage simultaneously the delivery
environment, course content, and educational technologies. Other challenges that must be
considered during e-learning designs are taking into account student characteristics such as levels of
prior domain knowledge and distance education experience, as well as context of learning tasks.
These factors may significantly influence students’ interactions and success in e-learning
environments (Dillon & Jobst, 2005). Finally, other factors such as learning strategies,
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Student Perceptions and Cognitive Load
communication, interaction, engagement, and assessment means are also related to the
effectiveness of distance learning using e-learning environments (Simonson et al, 2006; Richardson
& Newby, 2006).
While simple e-learning environments may be easy to design, these designs may lack
important properties that are essential for successful learning, such as flexibility and learner
engagement (Herrington et al, 2006). Such limitations are typical in proprietary learning
management systems where traditional pedagogical strategies are simply integrated into web-based
delivery of courses. In contrast, complex and newer pedagogical approaches, such as e-learning 2.0,
can be time-consuming to develop but have potential to provide students with more engaging
learning experiences. The term e-learning 2.0 has been used to describe the use of Web 2.0
applications such as blogs, wikis, podcasts and virtual worlds such as Second Life in distance
learning environments (Downes, 2005). These applications can add richness to content,
collaborative opportunities, and new possibilities for communication in courses. However, the
same features could also impose excessive levels of cognitive demands on some learners.
In fact, e-learning environments can actually inhibit learning if the instructional design fails to
account for and manage increased cognitive demands. To be effective, e-learning designs must
balance a stimulating, interactive environment with manageable levels of learner mental effort. In
distance learning, it is more difficult to achieve because once course materials and tasks have been
delivered to students, designers have a very limited control over student perceptions and learning
processes. There is often a gap between what designers expect will happen in a course, how course
materials are used, effectiveness of materials, and actual student opinions (Martens et al, 2007).
Therefore, it is important for the designers not only to understand the sources of increased
cognitive demands imposed on students in e-learning courses, but also to gauge student
perceptions of tasks in e-learning environments to ensure effectiveness from both student and
instructor perspectives.
Cognitive load theory (CLT) offers a general framework and empirically-based instructional
guidelines to control the conditions of learning within an e-learning environment (Sweller, 1999;
Clark et al, 2006; Clark & Mayer, 2007; Van Merriënboer & Sweller, 2005). This article applies
principles of CLT to the redesign of an e-learning 2.0 course using wiki technology. The
educational technology course ‘Using the Internet in the Classroom’ was originally offered in a
propriety learning management system that offered limited student choices and interactivity. The
effectiveness of the modified course was evaluated by examining students’ perceptions of cognitive
load, course delivery means, content, and activities. A review of major assumptions and principles
of CLT relevant to e-learning is presented first.
Cognitive Load Theory
The first assumption of CLT is that working memory (WM), where information is actively
processed, is very limited in its capacity and duration. It is assumed that only about seven or fewer
chunks of novel information can be processed at one time (Baddeley, 1986). Because of this
information processing ‘bottleneck’, WM could be easily overwhelmed. The amount of cognitive
processing or mental activity in WM required when performing a learning task is considered a
learner’s cognitive load (Paas, Renkl et al, 2003).
Another essential component of our cognitive architecture, long-term memory (LTM),
represents our knowledge base, which includes massive amount of organized knowledge structures
(schemas) without known limitations in capacity and duration. These schematic knowledge
structures allow us to categorize information patterns and get around the above WM ‘bottleneck’
by encapsulating many elements of information into a single unit that could be treated as one
chunk of information in WM. When learners have no existing organized knowledge structures
(schemas) in LTM to categorize novel elements of information, connect them with prior
knowledge, and organize them into such chunks, this information could be difficult to process. In
this case, the new information must be randomly organized within a limited capacity of WM,
resulting in much of the information being lost (Van Merriënboer & Sweller, 2005).
CLT distinguishes between three types of cognitive load. Intrinsic cognitive load (task-related
load) refers to the complexity of the material to be processed in WM simultaneously by the learner
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(Renkl & Atkinson, 2003). Intrinsic load is determined by the number of interacting elements of
information in the material (level of element interactivity) and the level of learner expertise.
Difficult learning tasks with high levels of interactivity for novice learners could be easy and low in
element interactivity for more experienced learners. Therefore, it is critical to know levels of
learner expertise for evaluating intrinsic cognitive load (Kalyuga et al, 2003). Intrinsic load could be
reduced by using simpler tasks or omitting some of the interacting elements. In many e-learning
environments, levels of intrinsic cognitive load could be too high for novices, causing low retention
rates among distance learners.
Extraneous or ineffective cognitive load results from the manner in which information is
presented and by learning activities required of learners. Extraneous load does not contribute
directly to learning and in fact, interferes with learning. For example, requiring a learner to
mentally integrate related sources of information that are located on different pages (e.g. a diagram
and associated explanatory notes, or task statements, student solutions, and a system’s feedback)
involves unnecessary search-and-match activities that are not directly relevant to learning.
Similarly, when a learner must scroll up and down on a single page to integrate related information
extraneous load can result. The level of extraneous cognitive load is of particular importance when
intrinsic cognitive load is already high (Paas, Renkl et al, 2003). When learning material has many
interacting elements that require learners to expend WM resources on comprehending the
material, any unnecessary extraneous load could cause a cognitive overload and inhibit learning.
Germane or effective cognitive load is induced by learners’ additional efforts to process and
comprehend the material (Brüken et al, 2003). For example, asking students to self-explain the
provided task solutions or imagine running the described procedures on their own may impose an
additional cognitive load; however, it could be relevant to learning. Germane load enhances
learning and results in mental resources being devoted to schema construction (Paas, Renkl et al,
2003). When intrinsic cognitive load is relatively low and extraneous load is minimized, sufficient
mental resources can be expended on higher-level cognitive processes. In these situations, learners
should be directed toward activities that require them to use their imagination, apply their
knowledge to explain the solution steps, or elaborate on the material. As expertise develops,
instructors should offer less guidance and provide students with more choices in learning activities
to increase germane load. If load is properly managed, learners can invest more time in developing
schemas associated with the new material.
A well-organized schema consists of interrelated concepts in LTM. Learning new information
is dependent in large part on how we organize or build our schemas (Rumelhart, 1977). Effective
and efficient retrieval of this information depends on how successfully we link individual concepts
into meaningful schemas (Bransford et al, 2000). The construction of a new schema requires WM
resources to consciously process all related information that could be integrated into that schema.
Because existing schemas can be treated as single elements in WM, they increase the amount of
information that can be held in WM at the same time (Rumelhart, 1977). If learners have to expend
WM resources on activities unrelated to developing new schemas, learning may be inhibited.
Therefore, the proper allocation of available cognitive resources is imperative for learning to take
place (Kalyuga et al, 2003). A highly organized set of schemas is a key characteristic of human
expertise in any domain, allowing experts to categorize multiple elements of information into
higher-level units and recognize patterns as familiar schemas (Van Merriënboer & Sweller, 2005).
Acquired and stored in LTM, schemas that can be drawn upon in a specific task situation allow
experts to overcome the limited capacity of working memory.
More experienced learners can activate their existing schemas in a learning situation to
organize, guide, and construct mental representations of a task. If instruction is designed to provide
required guidance for novice learners who possess no prior knowledge or existing schemas,
learners with higher levels of expertise in the domain may find the instruction redundant,
unnecessary, and even conflicting with their existing schemas. Having to cross-reference and
integrate the redundant materials with available knowledge structures may impose additional
cognitive load that could inhibit learning, thus demonstrating an expertise reversal effect (Kalyuga et
al, 2003; Kalyuga, 2007a). For more experienced learners, it may be better to eliminate the
unnecessary material or instructional guidance to prevent this reversed learning effect. Prior
experience has also been found to influence the level of interactivity in e-learning environments
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(Kalyuga, 2007b; Wouters et al, 2007) and levels of scaffolds and controls needed during instruction
(Paas, Renkl et al, 2003).
Based on CLT assumptions, instructional designers need to manage cognitive load during elearning by using techniques to properly sequence instructional materials and chunk related
content into manageable units so that many related elements of information are not presented at
once. For example, a top-down organization of content could provide an overall picture of the
material before specific facts and skills are learned (Martens et al, 2007). The number of interacting
elements of information within learning materials must be attended to in order to reduce intrinsic
cognitive load for novices and increase motivation or germane load for more experienced learners
(Van Merriënboer & Ayres, 2005). Providing a variety of instructional formats to appeal to different
learners can also help manage cognitive load. Appropriately balancing effective cognitive load can
enable learners to invest freed-up WM resources into schema construction, which is the ultimate
goal of any instruction.
Schema construction can also be stimulated by providing learners with opportunities to
interact with or self-explain the learning material. Problem solving and exploratory activities may
allow learners to put new skills into practice and realistic, authentic activities would challenge
learners and encourage exploration and self-directed learning (Martens et al, 2007). Authentic
activities may also require learners to articulate their knowledge representations and discuss their
understanding of ideas with other learners as they are developing their knowledge. Authentic
activities also stimulate curiosity, deep-level learning, and explorative behavior as characteristics of
constructivist e-learning environments. Such environments provide intrinsic motivation and
opportunities for learners to monitor their own learning and use appropriate metacognitive
strategies (Ryan & Deci, 2000; Martens et al, 2007). Finally, authentic activities foster synergies
among learners, task, and technology that create innovative and immersive e-learning
environments and have real-world relevance (Herrington et al, 2006).
E-learning 2.0 environments provide the real-world resources, communication and
collaboration capabilities to connect learners, tools to build products, meaningful contexts,
ownership of learning, and built-in assessments. These new environments can be exciting and
dynamic but they also present challenges for both designers and learners. The purpose of the
present pilot study was to examine cognitive load factors and student perceptions of a newlydesigned e-learning 2.0 course as well as their relationships with learner prior experience in distance
learning and using technology, engagement, and anxiety about taking a distance education course.
The primary goal for designing the course was to offer students more engagement and interactivity
with the learning environments, and immerse learners in Web 2.0 technologies with the aim of
increasing understanding of the material (i.e. to generate learning-enhancing or germane cognitive
load). The content of this course was organized and learning activities were selected so as to
minimize unnecessary or extraneous cognitive load. Sufficient scaffolds were included to reduce
cognitively inefficient search processes associated with using such a large variety of materials and
new technologies, particularly for inexperienced learners. The exact levels of learner expertise were
not known at the beginning of the course but learners were given a choice in selecting relevant
materials based on their own needs. The course, which traditionally had been taught using a
popular proprietary content management system, was redesigned for delivery via a commercial
wiki website.
E-learning Web 2.0 Course Design
The course is aimed at examining the uses, benefits, challenges, trends, impact and issues related to
using the Internet in the classroom, exploring the latest Internet-based applications and improving
students’ skills in using them for instructional purposes and designing classroom activities that
make use of these applications. Since Web 2.0 technologies are revolutionizing the way we use the
Internet, the issues and trends (e.g. creativity, information sharing, ethics, globalization, and
collaboration) associated with these technologies serve as the guiding framework for course topics.
Web 2.0 technologies were selected since they are web-based, relatively easy to learn (thus capable
of reducing intrinsic load typically associated with learning technology) and yet interesting enough
to challenge more experienced learners (thus capable of increasing germane load). The course
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content included (1) communication tools, (2) mapping/mashups, (3) podcasting, (4) blogging, (5)
social networking, and (6) instructional games, simulations, and virtual worlds/reality (e.g. Second
Life).
The wiki-based course, designed to immerse learners in using Web 2.0 technologies as a
method of learning, was segmented into units (e.g. communication and collaboration). Unit topics
were listed on the wiki navigational sidebar. Assignments were listed sequentially on each topic
page. Simple step-by-step tutorials or instructions were provided for each assignment and due dates
posted on the course wiki, on an embedded online Google Calendar, and on a downloadable
printable copy. Technical applications and classroom issues were learned in weeks 1-8. Theory and
research were introduced in weeks 10-13 when learners had acquired basic technical skills and were
more prepared for dealing with higher-level cognitive processes related to the Web 2.0
applications. The course met International Society for Technology in Education (ISTE)
Technology Facilitation Standards and Technology Leadership Standards (International Society for
Technology in Education, n.d.) Course concepts were presented using hyperlinked articles, online
videos embedded on the course wiki, instructor-designed handouts, podcasts, blogs, websites,
readings, and examples illustrating different uses of technologies. No textbook was used in the new
course and learners were given flexibility in which materials they selected in an assignment.
Learner support was provided as needed by synchronous communication tools such as YackPack
or Google Talk embedded on the website, email, and a wiki page designated for comments and
questions. Weekly notes were emailed to learners and posted on the home page of the course wiki.
Appropriate organization of topics on the wiki sidebar, listing assignments sequentially, offering
tutorials, allowing student choices in materials, and offering multiple support systems were
strategies intended to reduce extraneous cognitive load.
Student assessment included six major components: technical applications, a group
presentation, two discussion debates, blog reflections, a professional development workshop, and a
research paper. Instructional approaches such as blogging, online discussions, and Google web
pages were intended to encourage germane load since they required manipulation, interaction, or
self-explanations of course material. Blogs provided a metacognitive strategy to encourage
reflection of concepts and learning experiences. Questions were provided to scaffold reflections but
creativity was encouraged so that learners would have a sense of ownership in how they designed
their blogs and other information postings. In online discussions, learners were asked to take a
position on a topic and debate it with class members in a manner suggested by Palloff & Pratt
(2005). Such strategies have been found to be more motivating to students and increased mental
effort allocated to a task (Paas et al, 2005). Martens et al (2007) suggested that when learners are
required to articulate their knowledge representations and discuss their understandings with other
learners, this would help them to develop their knowledge.
Learners designed a professional development workshop using Google Page Creator. The
purpose of the workshop was to introduce teachers or colleagues to at least one Internet-based
application that could be used in supporting curriculum standards. Learners were required to use
the Web 2.0 technologies covered previously in the course as well as an online survey from
SurveyMonkey for participants’ evaluation of the workshop. This assignment provided learners
with an authentic activity that could actually be implemented in their respective settings. As
suggested by Martens et al (2007) and Herrington et al (2006), an authentic activity allows learners
to put their new understandings and skills into practice in a realistic context and intrinsically
motivates them to explore and control their own learning processes. The final assignment, writing
a research paper, asked learners to synthesize theory, concepts, and research related to using a Web
2.0 technology in the classroom. As in blogging and discussions, this assignment required students
to self-explain course concepts and form a personal rationale for using the Internet in the
classroom. Table I lists the strategies used to manage the different types of cognitive load in the
course. Intrinsic cognitive load was managed by selecting appropriate levels of learning tasks
difficulty relative to levels of learner prior experience. Extraneous load was minimized by providing
learners with sufficient instructional support and simplifying search processes to reduce
unnecessary diversion of mental resources. Germane load was generated by learner activities
designed to productively use the mental resources that became available after intrinsic and
extraneous loads had been appropriately controlled.
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Intrinsic load
Select appropriate Web 2.0
technologies and course topics
Simplify tasks
Offer learner choices based on
level of experience
Extraneous load
Organize course by topics
List topics on wiki sidebar
Offer variety of activities
Provide scaffolds or tutorials
Offer support systems
Germane load
Offer interactivity
Create collaboration
Integrate reflective activities
Require authentic activity
Table I. Strategies for managing cognitive load.
Method
Research Design
This research investigated the effectiveness of the e-learning 2.0 course and examined potential
relationships among students’ experience in distance learning, level of expertise in using
technology, engagement, and anxiety about taking a distance education course. A convenient
sample of 12 students included six master’s level students and six doctoral level students (3 male
and 9 female). Even though the course was designed for students specializing in leadership
positions in the field of educational technology, the course can also be used to fulfill elective
requirements in programs across the university. As a result, the course was comprised of a diverse
mix of students: six in the educational technology program, two in English, one in criminal justice,
one in educational foundations, one in secondary literacy education, and one in secondary math
education.
Data Collection and Analysis
Because this study was conducted in realistic rather than controlled experimental conditions, a nonrandom small sample of students was involved. In many practical situations in natural settings, it is
often not possible to implement a rigorous experimental control. As a result, the non-parametric
Kendall’s tau was used to investigate correlations between the number of distance education
courses taken (as a measure of experience in distance education), technology experience, anxiety,
and level of engagement in course. In addition, distance education experience was used to further
examine the effect of learner individual differences on different types of cognitive load. Because the
study was conducted under realistic conditions, it used indirect indicators of different types of load
based on characteristics of learning activities and materials that presumably could be associated
with generating these types of load.
A 68-question online survey was used at the end of the semester to collect participant data on
anxiety, number of distance education courses taken, technology experience, wiki design issues (i.e.
information structure, media formats, and usability); level of instructor support; engagement in
course; cognitive load (i.e. mental effort and stress) related to technologies learned, course
material, and activities; and perceived value of learning topics and activities.
The first six questions asked students to provide their teaching area (short answer), grade
level (multiple choice), level of anxiety (1 = not anxious at all to 5 = extremely anxious), number of
courses taken prior to the present course (multiple choice), level of technology experience (1 = not
very experienced to 5 = extremely experienced), and level of engagement (1 = not very engaged to
5 = extremely engaged) in the course. The next seven questions asked students to rate course
design features ranging from 1 (negatively worded) to 5 (positively worded); for example, ‘How
would you rate the structure or organization of information on the course wiki?’ Students selected
from a 5-point scale ranging from 1 (poorly organized) to 5 (extremely well organized). Higher
scores would indicate more satisfaction with course design and, therefore, presumably less
extraneous cognitive load imposed by design features.
The next 54 questions asked students to rate their levels of mental effort and stress on a 5point Likert-type scale ranging from 1 (low) to 5 (high). For example, ‘How much mental activity
was required in completing this task using this technology? (e.g. thinking, deciding, remembering,
searching, etc.)’, ‘What was your stress or frustration level while learning to use this technology?
(e.g. how insecure, irritated, content, gratified, relaxed, or complacent did you feel?)’. Twenty
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questions related to using various technologies (e.g., YackPack, wiki, Second Life), 18 related to
different materials and topics learned in the course (e.g. gaming, simulation, and virtual reality),
and 16 related to course activities (e.g. holding a class discussion in Second Life, discussion debate,
group presentation).
The last question asked students to rate 21 course activities based on whether they considered
them valuable for helping them learn the conceptual knowledge and skills related to using the
Internet in the classroom. These included activities such as keeping a course blog, investigating
how podcasting is used in the classroom, exploring Second Life, investigating mashups and
mapping, and investigating theory related to course topics. Items were rated on a 5-point scale
from 1 (low) to 5 (high).
Subjective measures are frequently used in cognitive load research to assess mental effort and
they have been found to reliably assess perceptions of invested effort (Brüken et al, 2003). Even so,
it remains unclear exactly how mental effort relates to actual cognitive load. A low mental effort
could be the result of low cognitive load or simply a lack of interest or engagement in activity. In
other rating scales such as the NASA Task Load Index, temporal demands and stress are
component scales weighted to reflect the contribution of these factors to the total load (Hart &
Staveland, 1988) and hence, the reason for inclusion of stress as a factor in this study. Temporal
demands were excluded since in distance learning, it would be difficult to determine how students
would allocate time to a given assignment.
Results and Discussion
Of 12 students, 4 had not taken any online course prior to this study; 1 student had taken one
course; 2 students had taken two courses; 2 students had taken three courses; and 3 students had
taken more than four courses prior to this course. Students on average were not very anxious
about taking the course (M = 1.75, SD = 2.51) and were fairly experienced in using technology
(M = 3.83, SD = 1.95). Eight students rated themselves at the two highest levels of technology
experience. Engagement in the course was high (M = 4.42, SD = 2.88). Kendall’s tau (see Table II)
indicates a negative correlation between anxiety and other variables of interest: number of courses
taken (r = –0.46, n = 12), technology experience (r = –0.40, n = 12), and engagement (r = –0.372,
n = 12). Positive correlations were exacted between all other variables. While not statistically
significant, the results indicate a trend that with more distance education and technology
experience students’ level of anxiety is decreased and the level of engagement is increased. These
results support previous research showing that learner experience is a critical factor in determining
what learners consider to be relevant, what information they attend to (Kalyuga et al, 2001), and
how motivated they are when performing a task (Paas et al, 2005). Prior experience in distance
education has been shown to be a predictor of cognitive engagement (Richardson & Newby, 2006).
Anxiety
Correlation coefficient
Sig. (2-tailed)
n
No. of DE
courses
Correlation coefficient
Sig. (2-tailed)
n
Correlation coefficient
Sig. (2-tailed)
n
Correlation coefficient
Sig. (2-tailed)
n
Technology
experience
Engagement
Kendall’s tau
Anxiety
1.00
12
-0.46
.087
12
-0.40
.152
12
-0.372
.206
12
No. of
courses
-0.46
.087
12
Technology
experience
-0.40
.152
12
Engagement
1.00
.210
.452
12
1.00
.190
.524
12
.464
.103
12
1.00
12
.210
.452
12
.190
.524
12
Table II. Relationship among anxiety, no. of distance education (DE)
courses, technology experience, and course engagement.
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12
.464
.103
12
-0.372
.206
12
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Student Perceptions and Cognitive Load
Interpretations of these results within a cognitive load framework need to consider two factors.
Firstly, more experienced learners can devote more mental effort to constructing schemas for key
course concepts rather than learning how to use the technology, thus making higher-level
cognitive processing more engaging. This explanation is supported by results of other research
studies (Greene & Miller, 1996; Stoney & Oliver, 1999) indicating that students’ perceived ability,
learning goals, and prior learning are positively correlated with meaningful cognitive engagement.
Secondly, an effective course design can sufficiently reduce extraneous cognitive load, leaving more
learners’ mental resources available for schema construction (Martens et al, 2007). This possibility
is supported by high levels of students’ ratings on course design (see Table III), as well as higher
ratings on assignments that were more cognitively challenging (see Table VIII). All aspects of the
course design were highly rated, particularly the usability of the wiki as a content management
system, indicating likelihood of relatively low levels of wasteful extraneous cognitive load that is
usually associated with poor instructional design. Minimizing the amount of mental effort that
must be expended on activities that are not directly related to schema construction (e.g. search
processes required for the orientation in the organization and structure of the course) reduces
extraneous load, allowing students to expend more relevant mental effort on understanding
essential course concepts (Martens et al, 2007).
Course features
Opportunities for collaboration
Clarity of course objectives
Selection of media
Structure of course
Sequence of topics
Instructor support
Wiki usability
Mean (SD)
4.17 (3.36)
4.42 (2.88)
4.42 (3.36)
4.67 (3.58)
4.67 (3.78)
4.67 (3.58)
4.75 (3.91)
Note: higher course design ratings are associated
with lower levels of extraneous cognitive load.
Table III. Course design ratings as indicators of
reduced levels of extraneous cognitive load.
Mental effort and stress ratings related to using various technologies are shown in Table IV. These
ratings are considered as indicators of intrinsic cognitive load determined by the level of
interactivity between essential elements of these technologies. High levels of element interactivity
when using various technologies may substantially increase cognitive load depending on the
student’s level of experience. According to the ratings, Web 2.0 technologies themselves were not
overly difficult for students to learn or use. On the other hand, some technologies that were
perceived as difficult to learn or use were considered less stressful. The Podomatic podcasting
application was experiencing problems during the week of podcasting assignments, making the
completion of the assignments difficult, as evidenced by ratings. Additionally, based on student
comments, YackPack was not considered a necessary form of communication in some cases when
working with group partners even though one of the purposes of the course was to examine the
use of such technologies in educational environments. When using a wiki to create personal pages,
mental effort was less than when using the same technology as a presentation medium for group
projects. High levels of interactivity experienced in group activities potentially made students
perceive this technology as more difficult and stressful to use under these circumstances.
It is also unclear how students define stress related to using a technology. It was evident that
Podmatic was problematic because of the website issues students experienced. However, Google
Page Creator may have been perceived as relatively more stressful just because of the nature of the
assignment, which was more lengthy and comprehensive than other technical assignments. In
other words, the stress rating does not tell us if it was actually the technology itself that was
difficult to use; how much the technology was related to the nature of an assignment that was
deemed stressful, unnecessary, or ineffective; if stress was related to the low value students placed
on a specific technology; or how little time students gave to the assignment. Using Facebook, an
assignment that was rated low in mental effort and high in stress, is an example of this interpretive
difficulty. When evaluating the assignment using Facebook (see Table VIII), students rated this
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activity as least valuable, indicating that mental effort was likely to be associated with perceived
value or other factors, not necessarily with the technical difficulty of the application. Even though
the question included the imperative, ‘Keep in mind that you are only rating the actual ease in
using the technology, not the learning activity that incorporated its us,,’ this comment may have
been overlooked. It is most likely the case that in an educational technology course where parts of
the content or activities it integrates are aimed at learning technology, it is difficult to judge the use
of technology isolated from a particular assignment.
Gmail
Facebook
Blogger
YackPack
Google groups (online debates)
Wiki (personal page)
Wiki (group project)
Google Page Creator (professional
development workshop)
Second Life (discussion)
Podomatic (podcast)
Mental effort
Mean (SD)
1.67 (1.37)
1.83 (0.94)
2.08 (1.08)
2.17 (1.27)
2.25 (1.06)
2.50 (1.09)
2.58 (1.31)
3.42 (0.90)
Stress
Mean (SD)
1.08 (0.29)
2.58 (1.31)
1.25 (0.45)
2.58 (1.31)
1.75 (1.06)
1.75 (0.97)
2.00 (0.85)
2.42 (1.31)
3.67 (0.98)
3.75 (1.22)
3.67 (1.56)
3.67 (1.44)
Note: associated course activities are provided in parentheses for cross referencing purposes.
Table IV. Mental effort and stress ratings related to technologies used as indicators of intrinsic cognitive load.
Mental effort and stress ratings related to course materials (as indicators of intrinsic cognitive load
resulting from course materials) are shown in Table V. Course materials imposed a moderate to
high level of intrinsic cognitive load. As would be expected, more cognitively challenging materials
such as research and theory required the greatest mental effort. However, an anomaly exists in the
low level of stress associated with these assignments. As in the study of Richardson & Newby
(2006), it is possible that students more experienced in technology, which were the majority of
students in this course, found more engagement in the more cognitively challenging assignments
and hence, the lower level of stress associated with this material. Similar to ratings related to
technologies, stress and mental effort related to course material could be coupled with
technological problems, making it difficult to separate the two in educational technology courses.
This was evidently the case in podcasting and Tapped In assignments.
Web 2.0 user-created content
Mapping and mashups (group project)
Games, simulations, and virtual reality
Social networking (Facebook) & Second Life (discussion)
Communication and collaboration
Podcasting and blogging (podcast/blog)
Related theories
Related research
Online professional development (Tapped In)
Mental effort
Mean (SD)
2.75 (0.75)
3.00 (0.74)
3.08 (0.67)
3.08 (0.79)
3.17 (1.11)
3.58 (0.67)
3.58 (0.79)
3.67 (0.89)
3.92 (1.24)
Stress
Mean (SD)
2.25 (0.87)
2.25 (0.75)
2.58 (1.16)
3.33 (1.23)
2.92 (1.16)
3.17 (1.03)
2.25 (1.06)
2.42 (0.90)
3.08 (1.56)
Table V. Mental effort and stress ratings related to course materials as indicators of intrinsic cognitive load.
Mental effort and stress ratings related to course activities as indicators of extraneous or ineffective
cognitive load associated with course activities are presented in Table VI. As suggested by Hart &
Staveland (1988), time was most likely a factor contributing to the mental effort of course activities.
Activities with the highest mental effort (i.e. podcasting, Second Life, and professional
development workshop) also required the longest amount of time for completion. As mentioned
earlier, podcasting was stressful due to problems with the Podomatic podcasting application, and
while the Tapped In assignment did not require as much mental effort as other assignments it was
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also deemed stressful. Tapped In is an educator professional development and networking website
that offers introductory tours for new users. Even though students were notified that they should
not wait until the last minute to begin this activity since tours were only offered on certain days
and times, this still affected completion of assignments and most likely perceived level of stress. It is
also possible that online debates were perceived as more difficult because of the cognitive difficulty
of the activity.
Blogging
Tapped In
Wiki group project
Online debates
Second Life discussion
Podcast
Professional development workshop
Research paper
Course activities
Mental effort
Stress
Mean (SD)
Mean (SD)
2.75 (1.06)
1.67 (0.78)
2.92 (1.24)
3.00 (1.41)
3.42 (0.67)
2.83 (1.34)
3.58 (0.67)
2.50 (1.00)
3.58 (0.90)
3.58 (1.62)
3.67 (1.15)
3.75 (1.22)
3.92 (1.08)
3.75 (1.22)
4.08 (0.79)
3.08 (1.38)
Table VI. Mental effort and stress ratings related to course
activities as indicators of extraneous cognitive load.
When grouping students by the number of distance education courses taken, differences in
cognitive load are shown in Table VII. Five students had taken either one or no previous distance
education courses (less DE exp). Seven students previously had taken two, three, or four courses
(more DE exp). Students with less distance education experience expended more mental effort than
more experienced learners on technologies used and on understanding course materials. This
supports prior research on the role of experience in e-learning environments and cognitive
engagement (Kalyuga et al, 2003; Richardson & Newby, 2006; Kalyuga, 2007b). Stress related to
materials and activities and mental effort imposed by course activities were slightly higher for more
experienced learners. This finding could represent an example of the expertise reversal effect that
results from the additional cognitive load experienced by experienced learners having to crossreference and integrate redundant materials.
Course design
Mean (SD)
Less DE exp
More DE exp
4.38 (0.60)
4.63 (0.51)
Technology used
Mean (SD)
ME
Stress
3.04 (1.04)
2.54 (0.92)
2.27 (0.98)
1.93 (0.91)
Course material
Mean (SD)
ME
Stress
3.44 (0.78)
2.64 (1.07)
3.22 (0.90)
2.74 (1.12)
Course activities
Mean (SD)
ME
Stress
3.43 (0.25)
2.93 (0.36)
3.54 (0.24)
3.05 (0.33)
Table VII. Ratings for learners with different levels of distance education (DE) experience.
The final survey question asked learners to rate course activities based on their value in helping
students to learn the conceptual knowledge and skills related to using the Internet in the classroom.
In order to associate this rating with the level of generated germane cognitive load, the following
note followed the question: ‘Do not judge whether you liked or disliked the activity or topic, had
difficulty with it, or did not investigate it as thoroughly as you would have liked. Rather, rate the
activity or topic solely for its VALUE in giving you an understanding of the technologies, issues,
trends, etc. of using the Internet in the classroom’. Table VIII displays student ratings and could be
considered as a summary of perceptions about germane or effective cognitive load that enhances
schema construction and greater understanding of the material (Paas, Renkl et al, 2003).
Ratings showed moderate to high values for all assignments. The mean value rating for
students with less experience in distance education was 3.95 (SD 0.43) and for those with more
experience was 4.31 (SD 0.24). As with all previous ratings, these ratings could have also been
affected by the nature of the course material or technology used rather than the value of a
particular topic or activity. The lowest rated value was assigned to investigating Facebook, an
unexpected result considering that social networking sites such as Facebook are expected to be
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widely adopted in education within one year (New Media Consortium, 2007). It is possible that if
more context was provided for this assignment during the course, ratings may have been different.
For example, if Facebook were used regularly as the course website or if students had been asked to
become integrally involved in a particular social group within this environment, more value might
have been given to using the technology. Similar to other ratings, students placed greater values on
more challenging topics or activities such as theory or research, and lower values on actually using
or learning Internet applications such as Second Life, wikis, and mashups. Again, this could be due
to the fact that most of the students rated themselves as more experienced in technology and/or
distance education and would find using the various technologies tedious or unnecessary (in
accordance with the expertise reversal effect as described by Kalyuga et al, 2003; Kalyuga, 2007b).
Course activities
Investigating Facebook
Participation in discussion debate
Investigating Second Life
Watching online videos
Reading articles on simulations, games, and virtual reality
Writing a research paper related to course topics
Creating a personal wiki page
Having a discussion in Second Life
Investigating simulations, games, and virtual reality
Participation in a group project on mashups
Investigating mashups and mapping
Investigating how podcasting is used in the classroom
Creating a Google website
Exploring professional development websites
Creating a podcast
Investigating the history, impact and trends of the Internet in education
Investigating Web 2.0
Investigating theory related to course topics
Investigating research related to course topics
Learning about how blogging is used in the classroom
Keeping a course blog
Mean(SD)
3.17 (1.40)
3.67 (1.23)
3.75 (1.14)
3.83 (0.94)
3.92 (1.51)
4.00 (1.04)
4.08 (1.08)
4.08 (1.16)
4.08 (1.38)
4.25 (0.75)
4.25 (0.97)
4.25 (1.06)
4.42 (0.67)
4.42 (0.79)
4.42 (0.90)
4.50 (0.80)
4.50 (0.67)
4.50 (0.80)
4.50 (0.80)
4.58 (0.79)
4.67 (0.65)
Table VIII. Ratings of the value of course activities as indicators of germane load.
Of particular interest is the value associated with blogging. In support of previous research,
strategies that require students to elaborate on course material or discuss their understandings with
others enable the allocation of more effective mental effort on schema construction and the
development of knowledge (Paas, Renkl et al, 2003; Martens et al, 2007). These activities are also
more motivating to students and increase the desire to allocate more mental effort to a task (Paas
et al, 2005). Surprisingly, while assignments such as these encourage schema construction,
‘discussion debates’ that required learners to explain course issues and discuss understandings with
class members were rated as one of the least valuable activities. This warrants further investigation,
especially in e-learning environments where discussions take place asynchronously and require a
lot of typing of thoughts, a difficult task for some students.
Conclusion
Results of this investigation showed that wiki Web 2.0 technology was a suitable environment to
offer an e-learning 2.0 course for students, both for those who are experienced in using technology
and in taking distance education courses and those with less experience in both areas. This study
supports previous findings showing that prior experience influences what learners consider relevant
and how engaged they are in particular tasks. Experienced learners were more engaged in more
cognitively challenging activities. In educational technology courses where technology is a critical
component of the course content and the number of interacting elements may be relatively high,
additional cognitive load must be factored into mental effort required of learners. Intrinsic
cognitive load can be imposed by the difficulty of the material or the number of interacting
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elements of information relative to the level of learner experience in a domain. A majority of
students in this course rated themselves as experienced in using technology and as a result, most
Web 2.0 technologies did not impose a high intrinsic cognitive load on them, with the exception of
technologies that presented unexpected problems during assignments (e.g. podcasting) or those
associated with relatively more lengthy assignments (e.g. Google web pages). In general, students
rated the level of intrinsic cognitive load related to course material from moderate to high.
Coinciding with other research (Richardson & Newby, 2006), cognitively challenging material
was sometimes associated with lower levels of stress, a finding that might indicate that some topics
were more engaging. Less experienced students expended more mental effort on technologies and
course materials while more experienced students were stressed by these. Mental effort was also
higher for more experienced students in course activities, possibly due to the expertise reversal
effect (some of the activities may have been redundant for these learners). All topics and activities
were highly valued for their inclusion in the course, indicating a possibility of relatively high levels
of germane cognitive load. This is supported by high ratings on students’ levels of engagement in
the course. Blogging was one of the highest rated activities, showing the importance of selfreflection to learning. Taken together, these findings suggest the importance of understanding
levels of learner expertise and the need for differentiated instructional strategies for different
learners in e-learning 2.0 environments. Student perceptions and ratings of mental effort provide
valuable insight into learner satisfaction and the effectiveness of Web 2.0 environments for offering
distance education courses.
Measurement of cognitive load, especially distinguishing between its different types (intrinsic,
extraneous, and germane), is a difficult task even in laboratory studies with very restricted and welldefined task domains (Paas, Tuovinen, et al, 2003). This study was conducted in realistic rather
than controlled experimental conditions and used indirect indicators of different types of load based
on macro-characteristics of learning activities and materials that presumably could be associated
with generating different types of load. Future studies need to develop and use more direct and
objective measures of these types of load. Other limitations of this study include a small sample size
and the overlapping of subjective ratings on materials and activities of a technical and conceptual
nature. Further research is needed on adapting instruction in these new environments to different
levels of learner experience and on using more objective measures for evaluating the effectiveness
of e-learning Web 2.0 courses.
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JUDY LAMBERT is an Assistant Professor and Coordinator for the undergraduate Educational
Technology program at the University of Toledo. She focuses her university research on examining
effective strategies to improve pre-service teachers’ abilities and attitudes towards integrating
technology in the classroom. Her contributions include the investigation of the conceptual
knowledge and practice of technology integration in expert and novice teachers using knowledge
acquisition methods and Pathfinder network scaling software and the validation of instruments to
measure computer skills and applications of those skills during teaching and learning experiences.
Correspondence: Judy Lambert, University of Toledo, 2801 West Bancroft Street, Mail Stop 924, GH
2000G, Toledo, OH 43606, USA ([email protected]).
SLAVA KALYUGA is an Associate Professor in the School of Education at the University of New
South Wales, Australia. He has extensive experience in conducting research in evidence-based
instructional design principles for multimedia learning environments, and in developing online
instruments for evaluating cognitive components of performance. His specific contributions
include detailed experimental studies of the role of learner prior knowledge in cognitive load effects
(the expertise reversal effect); the redundancy effect in multimedia learning; the development of
rapid online diagnostic assessment methods; and studies of the effectiveness of adaptive procedures
for tailoring instructional guidance to levels of learner task-specific expertise. He was awarded an
Australian Research Council Postdoctoral Research Fellowship (2001-2003) and Goldstar Award
(2005) During his previous work in Russia (until 1991), he published more than thirty refereed
research articles and several books and textbooks. Correspondence: [email protected].
LISA A. CAPAN, a doctoral student, is the program director of Visual Media Technology, and of
Visual Communication Technology at Bowling Green State University, Firelands, USA, and online
instructor of Visual Communications Technology. Her areas of interest are in interactive media,
digital photography and videography. Her interest is in examining ways to utilize social networking
systems to foster interaction and communication during classroom learning. Correspondence:
[email protected].
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