Running head: COGNITIVE SCIENCE LEARNING THEORIES APPLIED
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Cognitive Science Learning Theories Applied to a Simulated Archaeological Dig Activity
Evan M. Silberman
New York University
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Introduction
Imagine two students, John and Jane are participating in the same section of an
undergraduate archaeology course. Both students are at the same point in the lecture, a section
dedicated to Archaeological Dating. This is a particularly difficult concept, one that students
universally struggle with. Jane’s instructor lectures on the topic with minimal supporting
materials. John’s instructor uses a variety of pedagogical techniques, including a concept map of
learning goals and objectives, assigned advanced readings, in-class group work, discussions, and
exercises. Most notably, John’s instructor designed a multimedia simulation, which John must
complete after the in-class lecture. The multimedia activity challenges John to date artifacts
found in a simulated archaeological dig.
Upon the completion of the class, John and Jane compare their experiences. Jane reports
struggling to understand the concepts. Her instructor gave a pop-quiz on archaeological dating.
Jane reported failing the quiz. She recalls how complex the material was, and how rote her
learning experience was. John, on the other hand, was graded on the simulated dig activity and
received and passed the assignment. He attributes the simulation to his success and remembers
being engaged in the experience.
John and Jane both have an opportunity during the summer semester to participate in an
actual archaeological dig. John is confident he can transfer his practice from the simulation to a
real dig and takes advantage of the summer field activity. Jane, on the other hand, remains
uncertain about her knowledge of archeological dating and decides to not participate.
This is a common scenario. Two students learning the same material have different
outcomes. What accounts for the John’s mastery of the subject matter? Although the two course
sections are taught differently, this paper emphasizes the value of the simulated dig to promote
active and deep learning. In comparing the two course sections taught by two different
instructors using varying techniques, more standard assessments might be required. Assessments
are outside the scope of this paper.
Although the above situation is hypothetical, our group designed an interactive
multimedia simulation for a real-world context. Our simulation, which is the focus on this paper,
is in response to Dr. Thomatos’s experience teaching archaeological dating to college students.
The content is so complex, Dr. Thomatos no longer test students on it.
Our multimedia activity, a simulated archaeological dig (SAD) is an attempt to make the
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information comprehensible. This paper will discuss the theories of situated cognition, cognitive
apprenticeship, dual-coding, cognitive load, and cognitive theory of multimedia learning, and it
will explore principles akin to the theories. The paper will also discuss how the theories are
applied to SAD and in some case the design of the instruction.
Theoretical Framework
General Learning Theory
Situated Cognition. One of the challenges of teaching students about archaeological
dating is that the content is removed from the context. Learning archaeology in the classroom is
dissimilar to digging at an excavation site, and dating items based on the dig, and lab analysis.
For example, in order to apply the relative dating method, you need to know which layers of the
earth an artifact is extracted from. You then date it in relationship to the items found in nearby
layers. In a classroom setting, it’s difficult to replicate an excavation site.
SAD provides students a context that is a simulated archaeological dig and dating
exercise. Students are presented with four artifacts to excavate. Upon excavating each item,
students associate it within a strata of earth. Later in the activity, students related items to Carbon
dated relics and are presented a timeline of the items in the exercise. All of which starts and ends
in a virtual dig notebook, nearly identical to what’s used in the field. Therefore, SAD is a
situated activity, and grounded in the theory of situated cognition.
Learning is a socially oriented process. Clancy explains: “Situated Cognition claims that
every human thought is adapted to the environment, that is, situated, because what people
perceive, how they conceive of their activity, and what they physically do develop together” (as
cited in Driscoll, 2005, pp. 157). “The...approach is based on constructivist principles, in which
a learner actively constructs an internal representation of knowledge by interacting with the
material to be learned” (Plekhanova, 2005). Our in-class activities leading to the simulation, and
the multimedia exercise itself, reflect situated cognition.
Legitimate peripheral participation is: “Learning viewed as situated activity has as its
central defining characteristic a process [called] legitimate peripheral participation (As cited in
Driscoll, 2005, pp. 165). Through LPP, learning occurs as learners deepen their participation in a
community. Our classroom is the community, and in-class, group activities provide an
opportunity to learn together. The simulation is a sociocultural setting where cultural context is
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provided for learning. It is another way for the learner to assimilate to the community of
archaeology.
Students participate in their communities through cognitive apprenticeship and
assessments in-situ. According to Driscoll (2005), cognitive apprenticeship is usually the
relationship between a master teacher and student in which knowledge is transferred through the
apprentice learning ‘on-the-job’ as guided by the apprentice. While the traditional
master/apprentice relationship is difficult to reproduce in a classroom, our simulation models
skills for the students required to become a master archaeologists. The assessment in-situ does
not necessarily occur within SAD, despite a reflective journaling exercise. The animation is
embedded within a platform that collects rich analytics. Analytics tracking can be embedded into
the simulation. Therefore, the potential to gather summary statistics exists using reporting
features of systems like NYU Classes or software such as Google Analytics. According to
Driscoll (2005), “McLellan (1993) recommended a three part model...the three parts provide
three different kinds of assessments measures: (1) Diagnosis, (2), Summary Statistics, and (3)
portfolios” (As cited in Driscoll, 2005, pp. 179). The most relative model for SAD is summary
statistics such that they can help us understand how students interact with SAD.
Multimedia Specific Learning Theories
According to Plass, Homer, & Hayward (2009), there is increasing evidence that the
educational efficacy of visualizations depends on how well they are designed to reflect human
cognitive architecture. It is also a matter of learners cognitive ability to process and perceive the
essential information presented. All this to say that how visual images are processed is
complicated, and the process has the potential to increase cognitive load. The theories Plass, et al.
(2009) think are most important include the dual-coding theory, cognitive load theory of
multimedia learning, and cognitive load theory.
Dual-Coding Theory. Another basis of our design is Dual-Coding theory: “...Paivio and
his colleagues demonstrated that people were better at remembering lists of words coded visually
and verbally, rather than merely verbally...By encoding information to be learned in two
modalities rather than a single modality, people have multiple retrieval cues that help them
access information, thus enhancing memory” (Thompson et al., 2002). DCT is important because
it demonstrates a popular model for processing verbal and visual information and associating that
information with prior knowledge. The associative connections between the two representations
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are also important for effective instruction. According to Clark & Paivio (1992) verbal
associative process are a critical component of good teaching. However, SAD plays more
favorably to a modified version of DCT, the Dual-Coding Theory of Multimedia Learning.
Mayer & Sims (1994) adopted the DCT for multimedia learning in which “learners construct
referential connections between the mental representations of the verbal and visual information
presented within a hypermedia document” (as cited in Boechler, 2006, pp. 575). They studied the
effect of presenting animation and narration contiguously versus non-contiguously. The results
demonstrated that learners performed better on transfer-test and problem solving when animation
and narration were contiguous, also known as the contiguity effect.
We employ DCT in within our simulation where imagery is supported by text. Our
animation also has contiguous narration, and our students are inexperienced, which are two
factors for the contiguity effect. “Inexperienced students were better able to transfer what they
had learned about a scientific system when visual and verbal explanations were presented
concurrently than when visual and verbal explanations were separated (Mayer & Sims, 1994).
Naturally, a concurrent presentation of visualizations and spoken text help students understand
complex archaeological facts better. Through an improved understanding, they will perform well
on their worked examples and graded dig report.
Figure 1. A SAD interface with pictures supported by text and contiguous narration.
Cognitive Load Theory and Cognitive Theory of Multimedia Learning. The
Cognitive load theory is the most significant consideration for our SAD design. CLT is based on
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the idea that working memory is very limited. “The size of working memory is equal to the
amount of information that can be verbally rehearsed in approximately 2 seconds...When
information is attended to and enters working memory, if it is not consciously processed, it will
decay in approximately 20 seconds” (Boechler, 2006). According to Baddeley (1992) as load
increases on working memory, performance declines. Therefore, the goal of CLT is to reduce
cognitive load. Any way instruction decreases load is favorable, while still keeping it challenging.
CLT presents three types of loads: Intrinsic, Extraneous, and Germane. According to
Mayer & Moreno (2010), intrinsic load results from the inherent complexity of the learning
material, extraneous load results from mental activity that doesn’t contribute to learning, and
germane load results from mental activity that does contribute to learning. Intrinsic load is within
the control of the learner, while extraneous and germane load can be affected by instruction. The
goal of CLT is to reduce intrinsic and extraneous load, but foster germane load.
Cognitive theory of multimedia learning addresses how learners interpret the messages of
multimedia presentations. It is rooted in the multimedia principle. There are three assumptions of
CTML:
● Dual Channels, according to Paivio (1986), Baddeley (1986, 1999) meaning, “humans
possess separate channels for processing visual and auditory information” (as cited in
Mayer, 2005 pp. 34).
● Limited capacity, according to Baddeley (1986, 1999), Chandler & Sweller (1991),
meaning, “humans are limited in the amount of information that can be processed in each
channel at one time” (as cited in Mayer, 2005, pp. 34).
● Active processing, according to Mayer (2001), Wittrock (1989) meaning, “meaningful
learning depends on active cognitive processing during learning, including selecting
relevant information for further processing, organizing selected material into a coherent
mental representation, and integrating incoming material with existing knowledge” (as
cited in Mayer, 2005, pp. 34).
The three assumptions are important for understanding how to present multimedia
information. Presenting information using images and spoken word benefits learning because it
uses the two channels available in our cognitive structure. To promote active processing, that
information must be structured coherently in its presentation.
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SAD focuses on reducing extraneous load, managing intrinsic load, and fostering
germane load by applying several principles of CLT and CTML. The next section of this paper
will review principles akin to our design and how they are implemented in SAD.
Reducing Extraneous Cognitive Load
Redundancy Principle
According to Mayer & Moreno (2010), students learn better when redundant on-screen
text is eliminated from an animation using narration. In our simulation, on-screen text is
excluded to the extent that our narration is unique. There is no on-screen text that is similar to the
spoken text.
Temporal Contiguity Principle
According to the temporal contiguity principle: “If meaningful learning depends on
holding corresponding words and pictures in working memory at the same time, then successive
presentation of narration and animation can easily overload the learner’s cognitive system”
(Mayer & Moreno, 2010). Therefore, presenting narration and animation simultaneously reduces
extraneous cognitive load. Each segment of SAD presents animation and narration contiguously.
Reducing Intrinsic Cognitive Load
Segmenting Principle
According to the segmenting principle: “People learn more deeply when a multimedia
message is presented in user-paced segments rather than as a continuous unit” (Sweller, 2005).
Chunking or segmenting an animation to give the learner control of the presentation reflects the
segmenting principle. Each step of the activities embedded in SAD are separate. The user can
control the progress of SAD by choosing when to continue to the previous or next section.
Likewise, narration can be turned on or off, or paused so the student controls the portion of audio
and its pacing.
Modality Principle
According to Mayer & Moreno (2010), the modality principle is important when
presenting challenging material in a multimedia presentation. According to the modality
principle, presenting animation and narration is preferred over animations and on-screen text. In
other words, using spoken word to explain animations helps share the load in our different
processing channels. SAD adheres to this principle by providing its explanations through
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spoken-word, thereby eliminating the need for written descriptions of visual concepts in most
cases.
Other principles
Multimedia Principle
According to Fletcher & Tobias (2005) the multimedia principle states: “...people learn
better from words and pictures than from words alone...that people learn more or more deeply
when appropriate pictures are added to text” (as cited in Mayer, 2005, pp. 117). This principle is
a foundation of SAD because the simulation largely uses pictures and text, especially on third
screen of the virtual dig notebook. The pictures and text correspond and together represent
artifacts and explanatory information about the items. They provide context for the relationship
between an item and its history, particularly the date.
Worked-Out Example Principle
According to Renkel (2005), a worked-out example generally consists of problem
formulation, solution steps, and the final solution. A rule is first introduced, the worked-out
example is presented, and one or more problems to be solved are provided.
Worked-out examples are an effective tool for complex subjects like archaeology,
especially for archaeological dating. Both the simulation and the course design implement
worked-out examples to help scaffold learning. For example, students are asked to complete a
partial equation for carbon dating given some of its variables. In another activity, students are
given a blank harris matrix and information from a logbook and are asked to complete the matrix.
Worked-out examples are especially effective in our simulation because our learners are
low-prior knowledge students. As their expertise increases, the effectiveness of worked-out
examples, especially fully or partially completed models will be less effective (i.e. expertise
reversal effect).
Over the course of the class, and in combination with the simulation, we adhere to these
guidelines. Students are learning these concepts for the first time, and we think problem-solving
through worked-out examples will deepen their understanding of the concepts, and sharpen their
skills.
Active Processing and Reflection Principle
A significant aspect of our simulation is the end of the activity in which we ask students
to write a dig report. We provide an open-ended forum for them to reflect on their simulated dig.
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Journaling, in this scenario, is meant to promote active cognitive engagement. It uses the active
processing principle of CTML as a basis, because “Students may fail to learn unless instruction
includes methods aimed at engaging the learner…” (Moreno & Mayer, 2010). It is important to
note, that reflection is only accessible after the student correctly completes the preceding
activities. As noted by Moreno & Mayer (2010), reflection against correct information deepens
learning.
Feedback Principle
According to the feedback principle: “Novice students learn better when presented with
explanatory feedback during learning” (Moreno & Mayer, 2010). During the dating segments of
SAD, students are prompted with explanatory feedback to aid learning. Like the reflection
principle, students will learn better if given corrective feedback plus explanation. This helps
them create mental models of the newly acquired information.
Personalization Principle
According to Moreno & Mayer (2010), a personalized environment aids active and
meaningful learning. In a related study, Mayer (2003) discovered that social cues in multimedia
messages ignite social conversation schema in learners. This results in conversational like
behavior and enacts rules as if the person was in a social conversation. This is particularly true
when the voice of the spoken text is a standard-accented human Therefore, deep cognitive
processing is possible when narration is conversational and human.
The narration in our simulation will be a familiar and welcoming voice. It will address
students as if they have a personal relationship using terms like “you”, “we”, and “our”. By
scripting the spoken words to be conversational, the simulation will feel more personal.
Pretraining Principle
According to the pretraining principle: “people learning more deeply from a multimedia
messages when they know the names and characteristics of the main concepts” (Mayer, 2005).
SAD is completed after the in-class lecture on archeological dating. The names and
characteristics of the main concepts are provided through the lecture, group work, reading, and
other activities leading up to the simulation. Therefore, the students have the prior knowledge
necessary for deeper learning because pretraining, according to Mayer (2005), also helps reduce
intrinsic cognitive load.
Guided Activity Principle
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According to the guided activity principle: “instruction that allows students to interact by
dialoguing and manipulating the learning materials is more likely to lead to meaningful learning
then instruction that does not…” (Moreno & Mayer, 2010). SAD is an interactive simulation,
and students must manipulate objects to complete the partial worked-out examples. The
interaction is indented to provide students with deeper learning. For example, students must
select a trench, then an artifact, and ultimately place the selected relic in the appropriate location
on two different graphics to represent its age. Each step of the process is guided through a
narration. Together, the spoken text and control of objects promotes active learning.
Related Concepts
Advanced Organizers
According to Ausubel's Meaningful Reception Learning Theory: “Meaning occurs when
learners actively interpret their experiences using certain internal, cognitive operations. To
account for these cognitive operations and how they interact with experience give rise to learning,
Ausubel proposed a theory of meaningful, reception learning” (Driscoll, 2005). In other words,
meaningful learning occurs when learners, using existing mental models, are able to interact with
the learning experience. Advanced organizers are a tool help learners by “bridging the gap
between what the learner already knows and what he needs to know before he can meaningfully
learn the task at hand” (as cited in Driscoll, 2005, pp. 138).
Prior to starting the course lecture for archaeological dating, students are presented with a
concept map of the learning goals and outcomes. In the simulations, the first screen is a virtual
notebook with a graphical map of the dig site, and pictures of the artifacts students will explore
throughout the simulation. The concept maps of the course structure help students build
connections between the elements of the lecture, and recognized what they may already know.
The virtual notebook helps students identify concepts of archaeology previously discussed in the
lecture. Through the connections to prior knowledge of the course content, students are prepared
to dive deeper into the facts, concepts, and practice ahead.
Motivation and Emotion
According to Domagk, Schwartz, & Plass (2010) “Interactivity in the context of
computer-based multimedia learning is reciprocal activity between a learner and a multimedia
learning system, in which the [re]action of the learner is dependent upon the [re]action of the
system and vice versa”. The process is initiated by the learner and therefore requires motivation.
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According to the Integrated Model of Multimedia Interactivity (INTERACT) that Plass et
al. (2010) introduced, the learners’ affective state during multimedia instruction impacts their
connectedness to the concepts. The INTERACT model views emotions and motivation as part of
the learning process, which both influences and is influenced by cognition. It connects different
components via a feedback loop, which makes it interactive. The components can also behave
independently.
In particular, we are interested in emotion and motivation because one goal of our design
is to promote positive emotions. Aspects of motivation and emotion in multimedia learning was
studied by Um, Plass, Hayward, & Homer (2011): “Applying emotional design principles to
learning materials can induce positive emotions and that positive emotions in in multimediabased learning facilitate cognitive processes and learning”. We used soft visual shapes and warm
colors that, according to empirical research, induce positive emotions. The result, which is
something that requires evaluation, we hope is that students feel good about being in the
environment, and their positive affect translates to deeper learning.
Figure 2. SAD interface with curved visual shapes and warm colors.
Future Considerations
While the theory and principles above are the foundation of our design, with any
instructional development, there is room for improvement. As noted previously, the analytics we
collect will help us make design enhancements based on how students interact with the
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simulation. Given this is the only evaluation, it is critical for understanding how long students
spend within each segment of SAD and if students are struggling with a concept or problem.
If, for example, the carbon dating equation is challenging, we can use guided activities,
supporting instructional material, or corrective-self explanation. According to Roy & Chi (2005),
people learn better when they are encouraged to generate self-explanations.
More problem solving activities can be incorporated into the simulation to supplement
the worked-out examples. The worked-out examples might also be scaffold, to support expert
learners. This addresses the expertise reversal effect. The worked-out examples provided in SAD,
are goal oriented. That is students must complete sets of equations to reach the end of the
simulation. According to Sweller (1989), if material is structured with learning rather than goal
attainment in mind, problem-solving skills can be enhanced substantially. In other words, if our
simulation had practice activities that were goal-free, students might benefit more.
The simulation can also include different challenges, situations, or problems similar to
what students may face in the real world. This may include difficulties like weather, the location
of the excavation site, fragility of artifacts, and different types of artifacts from varied
civilizations and time periods, for example. If SAD approached learning in this manner, it would
be incorporating the variability principle: “Organizing learning tasks in such a way that they
differ from each other on dimensions that also differ in the real world has a positive effect on
inductive learning and transfer” (Van Merrienboer & Kester, 2005). The more diverse the tasks
are within the simulation, the more prepared students would be to transfer knowledge to a real
dig.
Lastly, as our designed for Dr. Thomatos’s archaeological dating class. When Dr.
Thomato’s incorporates the classroom session the project team developed, it will be good to
inquire about her experience teaching, and the students learning experience. Dr. Thomato’s
mentioned that material is too complex to test. If here perception changes based on the
instruction we designed, that is an indication of success.
Conclusion
In summary, SAD is an interactive multimedia simulation primarily using animation and
narration. The design is grounded in Cognitive Load Theory, Cognitive Load Theory of
Multimedia Learning, and Dual-Coding Theory. It uses various principles akin to these theories
to provide an active and meaningful learning experience for low-prior knowledge students. The
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primary goal of the design is to help students learn complex, scientific concepts by reducing
intrinsic and extraneous load, but foster germane load. This is accomplished by using
visualizations and spoken text or images and text, providing concurrent animation and narration,
giving learners control of pacing sections of SAD, structuring worked-out examples, providing
correct, explanatory feedback, using narration that is conversational and human, setting up the
simulation (and instruction generally) through advanced organizers, and inducing positive
emotion through design (i.e. visual shapes and warm colors).
The simulation will be evaluated via analytics, but the true test is the practical application
of SAD, and the instruction designed leading to SAD as an activity. Our assumption that deep
learning will occur remains to be seen. This will also reveal how to improve the surrounding
instruction, and SAD in future iterations as discussed in the future considerations section.
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