This document discusses how multimedia can be used effectively in online learning by reducing cognitive load on learners. It explores theories from neuroscience, cognitive load theory, and brain-based learning that provide principles for instructional design. Specifically, it outlines Clark and Mayer's seven principles for multimedia learning design, including using words and graphics, locating illustrations near text, splitting information across audio and visual channels, avoiding redundant information, removing extraneous material, using a conversational style, and segmenting complex lessons. Applying these principles can improve learner-content interactions and course quality by engaging learners and facilitating knowledge construction.

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Moderating the Effects of
Cognitive Load using Multimedia
Julie LaRoche
June 7, 2014
ELP 510: Developing E-Learning
Final Paper

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Introduction
The
use
of
multimedia
is
argued
to
have
the
potential
to
significantly
improve
instructional
efficacy,
particularly
with
regard
to
the
successful
learning
of
information
and
the
development
of
understanding,
but
concerns
persist
about
the
degree
to
which
its
design
and
use
have
realized
or
optimized
such
potential.
Martin,
2012
With extensive growth rates in online learning, a key concern for educators is how to
construct effective online methods to facilitate learning (Sun, Tsai, Finger, et al., 2008).
Multiple studies have examined the varied elements of online learning and numerous
findings point to learner-content interactions as having the greatest effect on learner
engagement and student success in online courses (Sun, Tsai, Finger, et al., 2008; Kuo,
Walker, Belland, & Schroder, 2013). This paper seeks to identify how to engage learners
and facilitate learning through the use of multimedia elements for knowledge
construction. More specifically, what multimedia elements can be employed to aid
learners in retaining and processing information effectively online?
E-learning Research
In a study of predictive factors for student satisfaction in online learning, Kuo, Walker,
Belland, and Schroder (2013) found that interaction and Internet self-efficacy are key
predictors of student satisfaction in online learning (p. 33). They researched three types
of interactions: learner-instructor, learner-learner and learner-content. Their study

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concluded that learner-content interaction was the highest predictor of online learning
satisfaction followed by learner-instructor interaction; learner-learner was not predictive
of online learning satisfaction. “The practical implications of this study are that both
instructors and course designers should pay attention to content design and organization
given that learner-content interaction substantially contributes to student satisfaction”
(Kuo, Walker, Belland, & Schroder, 2013).
While other research has been done to assess learner satisfaction with online learning
environments, a lack of clarity around why learners drop online learning courses
prompted an integrative model to assess six dimensions: learner, instructors, course,
technology, design and environment (Sun, Tsai, Finger, et al., 2008). Researchers
developed a survey to investigate the critical factors affecting learners’ satisfaction in e-
learning. The goal of the study was to help higher education institutions and instructional
designers better understand what factors lead to dissatisfaction in online learning. Of
these identified variables, the most significant factors included e-learning course
flexibility, e-learning course quality, perceived usefulness and perceived ease of use (p.
1192). “Of all independent variables, course quality has the strongest association with
satisfaction” (p. 1196). How can instructional designers improve course quality and
learner-content interactions online?
Neuroscience and Brain-Based Learning
First, in order to understand how learners interact with e-learning content, the field of
neuroscience seeks to illuminate how the brain incorporates and processes information to
understand how learning occurs (Clemons, 2005). Neuroscience is a relatively new field

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of research started in the 1990s; over 90% of all neuroscientists are still in practice today
(Clemons, 2005). Neuroscientists identify the executive function in the brain as enabling
cognitive processes. Executive function is the coordination of six activities: activation,
focus, effort, emotion, memory, and action (Prasad, 2014). The prefrontal cortex (PFC)
manages executive functions; the PFC incorporates new knowledge to give to the
hippocampus for memory storage.
Furthermore, emotions are critical to patterning; the reticular activating system (RAS) is
the first sensory intake filter in the brain and identifies familiarity (Prasad, 2014).
Familiarity and positive stimulation promotes a learning environment (Clemons, 2005).
Conversely, threat, high anxiety, and a sense of helplessness impair learning as these
activate the amygdala and shut down executive functioning. In panic or anxiety mode, the
amygdala takes control rather than the PFC (executive function); the amygdala handles
emotions of fear and aggression. Thus, two common obstacles to executive function and
learning are anxiety and perfectionism (Prasad, 2014). Therefore, an e-learning
environment should minimize these obstacles by aiming to create what psychologist
Vygotsgy describes as the ideal balance of learning as having a goal just out of reach but
attainable with a degree of effort. He termed this balance the “zone of proximal
development (ZPD)” (Hitchcock, Meyer, Rose, & Jackson, 2002). ZPD provides the
learner both familiarity, as well as novelty where the level of newly formed information
is achievable (Prasad, 2014). Ideally, learners work in the zone of proximal development
where tasks are measurably harder and not over simplified.

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As a result, neuroscience findings and the related brain-based learning theory have helped
to identify how to minimize cognitive load and maximize student learning. Core
principles of brain-based research discern that brains are specialized and not good at
everything (Clemons, 2005); “newly formed synapses are weak and require immediate
activity to last” (Wilson, 2011). In order to retain new information, a learner must engage
in active processing - this is the selection, organization, and integration of new material
(Clark, 2008). Researchers have found that to prompt active learning “curriculums must
be created to encourage complexity, novelty, and creativity” (Wilson, 2011) to stimulate
the brain into a “desired state of alertness” (Clemons, 2005). Additionally, learning
engages the whole body, multi-sensory input is desired, and a learner’s search for
meaning is innate and comes through patterning (Clemons, 2005).
Cognitive Learning Theory (CTL)
Related to brain-based learning and the zone of proximal development is the cognitive
learning theory. The application of cognitive load theory (CLT) incorporates brain-based
learning theory and provides an explanation for how to maximize e-learning design.
“Cognitive load theory seeks to explain why some material is more difficult to learn than
other material” (Martin, 2012). CLT is based on the premise that human brains use two
types of memory: short-term (or working memory of prefrontal cortex) and long-term
(involving hippocampus). Learners construct schema by organizing information using
short-term memory then transfer the information to store in long-term memory (Martin,
2012). Further, there are three kinds of cognitive load – extraneous, germane and intrinsic.
Extraneous cognitive load is the difficulty encountered when trying to process

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information; instructional materials affect extraneous cognitive load and can hamper
learning (p.126). Germane cognitive learning is the processing and building of schema
and can be controlled by helpful instructional design. Intrinsic cognitive load is the
learner’s prior knowledge and may not be altered by instructional design. The aim of
CLT is to promote the right amount of cognitive load – not too much or too little as
compared to ZPD.
Cognitive load theory in multimedia learning is focused on three cognitive processes:
selecting word and images in the material, mentally organizing the images and text, and
integrating the information into a prior knowledge base (Clark, 2008).
Through the lense of CLT, by optimizing educational e-learning, the result is more active
learning. In a quasi-experimental study across four secondary schools, findings clearly
showed that e-learning reduced the cognitive load of a high inter-activity subject
(Shakespeare’s Macbeth). Students in the intervention group who used multimedia
learning integrated with animation, audio, explanation and analysis, scored higher than
students in the control group without the multimedia learning materials (Martin, 2012).
So, how can multimedia learning support germane cognitive load (constructing schemas)
and reduce extraneous cognitive load (working memory)?
Seven Principles of E-Learning
Focusing on cognitive development and evidence-based research, instructional designers
can employ methods to aid in the learning process based on applied theories and
neuroscience research. In Clark and Mayer’s book E-Learning and the Science of
Instruction, the authors state, “From all media comparison research, we have learned that

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it’s not the delivery medium, but rather the instructional methods that cause learning” (p.
21). Simply put, when instructional methods are the same, the learning is the same,
irrespective of how the instruction is delivered. “When a course uses effective
instructional methods, learning will be better, no matter what delivery medium is used”
(Clark & Mayer, 2008). They further posit that not all mediums are the same. The
advantages to e-learning are the ability to practice with automated tailored feedback, the
ability to integrate collaboration with self-study, the ability to adjust instruction based on
learning, and the ability to use simulation and games (p. 22).
Clark and Myer (2008) define seven principles for e-learning. The first is the Multimedia
principle – the inclusion of words and graphics to promote active learning. Studies show
that a combination of words and images increase the learner’s ability to mentally
organize the material and associate it to established knowledge (p. 57). Conversely, when
learners are given only text, shallow learning more likely takes place and the new
information is not connected to prior knowledge. This is particularly true for novice
learners as opposed to experts in a field. Of particular note, the graphics should not be
used as decorative, but should associate to the words or represent a concept or process to
strengthen the textual content. As stated by Clark and Mayer, eleven studies compared
performance outcomes and in all studies comparing the use of text alone versus pairing
with images, every study resulted in students using both text and graphics had more
correct answers, particularly those who are novice learners (p. 67-68).

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The second principle is the Contiguity principle to locate illustrations, audio or video in
close proximity to the explanatory text. The contiguity factor avoids extraneous mental
processes that increase cognitive load and reduces effective learning. An example of
incongruous elements of learning are placing text and images on separate pages so a
learner must flip back and forth between screens. Additionally, requiring a learner to
click an audio icon rather than presenting the spoken words at the same time as the text is
incongruous. (p. 93). Rather, by intentionally designing the layout of visual information,
the course reduces unnecessary cognitive load for the learner.
Next, Clark and Mayer define the Modality principle as an aid to cognitive load by
splitting information across two cognitive channels – audio and visual. This principle is
useful when presenting and describing a complex graphic. By activating two modes
(sight and sound), the learner can visually focus on the graphic rather than reading text
and trying to view the graphic simultaneously. The modality principle is not intended for
all text and graphics (p. 99-101). Rather it is used for complex material that is presented
at a rapid pace. In such cases, the split channel effect reduces cognitive load to increase
active learning.
The Redundancy principle specifies use of either text or audio to explain a visual, but
emphasizes refraining from using both. Cognitive learning theory assumes that
overloading the visual channel with text and graphics when audio accompanies the
information is unnecessary and counterproductive. Learners may waste cognitive load

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trying to reconcile audio to the printed text and the information becomes a distraction
from active learning (p. 121-123).
According to Clark and Mayer, the most important principle is the Coherence principle,
which emphasizes eliminating extraneous words, sounds, and graphics. Educators often
include extra information with the intention to motivate learners. However, these
additions may not support the instructional goal. As an example, studies show that
background music added to e-learning reduces the effectiveness of the learning
environment. Based on CLT, the learner is working to make sense of the material
presented and extraneous audio/visuals can impede the process, particularly for a novice
or at-risk learner (p. 140-145). Providing knowledge organized as a pattern can ease the
understanding of difficult subjects; patterns can be a mnemonic device, an acronym or a
rhyming song (Clemons, 2005).
The Personalization principle is the sixth principle and is based on the psychological
theory that suggests the learner engages socially with the computer through audio or
textual conversational style. By using a conversational tone of text or audio and
employing a second-person active voice, the instruction involves the learner. Using an on
screen coach to present information is another form of the personalization principle
(Clark & Mayer, 2008). To reinforce the relevance of the personalization principal,
consider that executive function skills (activation, focus, effort, emotion, memory, action)
used in cognitive activity are the same skill sets used for both knowledge construction as
well as social interaction (Prasad, 2014).

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Finally, the Segmenting and Pretraining Principle involves breaking complex lessons
into manageable segments, providing definitions of terms and key concepts prior to
describing new procedures in a lesson (Clark & Mayer, 2008). This is often referred to as
“chunking” data (Prasad, 2014). Similarly, brain-based research suggests that for every
10 minutes of knowledge construction, the multimedia lesson should provide learners two
minutes to process information in the form of an activity in order to incorporate prior
knowledge (Clemons, 2005).
Summary
To conclude, online learning has advantages that other delivery mediums do not have.
Particularly, e-learning provides learner scaffolding and support through self-paced
instruction, practice opportunities with feedback, the integration of collaboration with
self-study and the use of gaming and simulations. Instructional designers and educators
can benefit from understanding brain-based research and using evidence-based
approaches to design online courses. By incorporating Clark and Mayer’s (2008) seven
multimedia design principles, e-learning becomes more effective and enables learners to
acquire new information, improve performance and construct new knowledge. Through
the use of design principles, the brain-based learning theory, and the cognitive load
theory, instructional designers can improve learner-content interactions and course
quality for effective multimedia courses.

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Appendix
Clark, R. C., Mayer, R. E. (2008). E‐learning and the science of instruction: Proven
guidelines for consumers and designers of multimedia learning. Pfeiffer Press.
Clemons, S.A. (2005). Brain-based learning: Possible Implications for online
instruction. Retrieved from http://www.itdl.org/journal/sep_05/article03.htm
Hitchcock, C., Meyer, A., Rose, D. & Jackson, R. (2002). Providing new access to the
general curriculum: universal design for learning. TEACHING Exceptional Children,
35(2), 8-17.
Kuo, Y., Walker, A. E., Belland, B. R., & Schroder, K. E. (2013). A Predictive Study of
Student Satisfaction in Online Education Programs. International Review Of
Research In Open And Distance Learning, 14(1), 16-39.
Martin, S. (2012). Does instructional format really matter? Cognitive load theory,
multimedia and teaching english literature. Educational Research And Evaluation,
18(2), 125-152.
Prasad, S. Strategies for improving executive function and school success. Conference
for Diverse Learners presented by Landmark College Institute for Research and
Training. Marylhurst University, Lake Oswego, OR, April, 18, 2014.
Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a
successful e-learning? An empirical investigation of the critical factors
influencing learner satisfaction. Computers & Education, 50(4), 1183-1202.
Wilson, C. 2011. Neuroandragogy: making the case for a link with andragogy and brain-
based learning. Midwest Research-to-Practice Conference in Adult, Continuing,
Community and Extension Education. Lindenwood University, St. Charles, MO,
Sept. 21-23.