Download Cognitive Load Theory

Cog Sci 1 + Ed Tech (Spring 2014)
Weekly Annotations
January 27, 2013 (#1)
Foundation and Assumptions of Cognitive Science
Overview of Information Cognitive Processing
Human Cognition
Introduction to Instructional Design
Foundations of Instructional Design
February 3, 2014 (#2)
Human Cognitive Architecture: Biological Bases of Learning and Memory;
Sensory, Short-Term, Working Memory Models
Information Processing
Biological Basis of Learning and Memory
Schema Acquisition and Sources of Cognitive Load.
Working Memory
The Role of Questions in Learning (Optional)
February 11, 2013 (#3)
Human Cognitive Architecture: Long-term Memory Models, Dual Coding Theory
For Whom a Picture is Worth a Thousand Words? Extensions of Dual-Coding
Theory of Multimedia Learning
Long-term Memory
Return of the mental image: Are there really pictures in the brain?
February 18 (#4)
Meaningful Learning, Schema Theory, Situated Cognition
Meaningful Learning
Situated Cognition
Cognition in the Wild (Optional)
February 25, 2014 (#5)
Cognitive Load Theory
Implications of Cognitive Load Theory for Multimedia Learning
The Split-Attention Principle in Multimedia Learning
The Redundancy Principle in Multimedia Learning
Cognitive Load Theory, Learning Difficulty, and Instructional Design
Direct Measurement of Cognitive Load in Multimedia Learning (Optional)
March 4, 2014 (#6)
Cognitive Theory of Multimedia Learning; Integrated Model of Picture & Text
Comprehension
Introduction to Multimedia Learning (Chapter 1)
Cognitive Theory of Multimedia Learning (Chapter 3)
An Integrated Model of Text and Picture Comprehension (Chapter 4)
Animations Need Narrations: An Experimental Test of a Dual-coding
Hypothesis (Optional)
March 10, 2014 (#7)
Individual Learner Characteristics, Expertise Reversal
Individual Differences and Cognitive Load
Prior Knowledge Principle in Multimedia Learning
Learning Styles: Concepts and Evidence
Analyzing the Learning Task (Optional)
March 24, 2014 (#8)
Managing essential processing in Multimedia Learning
Principles for Managing Essential Processing in Multimedia Learning:
Segmenting, Pretraining, and Modality Principles (Chapter 11)
Techniques in Generative Processing in Multimedia Learning: Open
Questions for Cognitive-Load Research (Chapter 8)
Ausubel's Meaningful Reception Learning Theory
In Defense of Advance Organizers: A Reply to Critics (Optional)
March 31, 2014 (#9)
Reducing Extraneous Processing in Multimedia Learning
Modality Principle
Worked-Out Examples Principle
Structuring the Transition From Example Study to Problem Solving in
Cognitive Skill Acquisition: A Cognitive Load Perspective (Optional)
April 7, 2013 (#10)
Control, Interactivity, and Feedback
Multimedia Learning in Games, Simulations, and Microworlds
Interactivity in Multimedia Learning: An integrated Model
Role of Guidance, Reflection, and Interactivity in an Agent-Based Multimedia
Game
Enhancing Instructional Efficiency of Interactive E-learning Environments: A
Cognitive Load Perspective (Optional)
April 14, 2014 (#11)
Affect–Motivation, Self-Regulation, and Emotion
Motivation and Self-Regulation in Learning
The Broaden-and-Build-Theory of Positive Emotions
Emotional Design in Multimedia Learning
Social Cues in Multimedia Learning, Role of Speaker’s Voice (Optional)
April 21, 2014 (#12)
Learning from Animations, Simulations, and Games
Multimedia Learning in Games, Simulations, and Microworlds
Multimedia Learning in Virtual Reality
Design Factors for Educationally Effective Animations and Simulations
Design Factors for Effective Science Simulations: Representation of
Information
Instructional Animation Versus Static Pictures: A Meta- Analysis
April 28, 2014
Cognitive Development: Stages of Development (Piaget), Interactionist Theories
(Bruner, Vygotsky)
Cognitive and Knowledge Development
Interactional Theories of Cognitive Development
January 27, 2013 (#1)
Foundation and Assumptions of Cognitive Science
Overview of Information Cognitive Processing
Driscoll, M. (2005). Psychology of Learning for Instruction (3rd ed.). Boston, MA: Allyn and Bacon. [pp.
71-77]
The states of the information processing system are sensory memory, working
memory, and long-term memory. Information is received through sensory input
either visually or audibly. It is held long enough to process for the working
memory. The working memory prepares information for storage or response, and
has limited capacity. The long-term memory is represents permanent knowledge
- anything that is processed through the first two stages successfully. This is not
necessarily a linear process as depicted in the books diagram. There is often
information being pulled from the long-term memory to assist with input and
transference from the intake of information through other processes.
Human Cognition
Guenther, R.K. (1998). Human Cognition. Upper Saddle River, NJ: Prentice Hall. [ Chapter 1 ]
What is the history of human cognition through the ages? This article, according
to Guenther (1998), presents an overview of perspectives on the human mind
since our cosmology. Several key points that Gunther (1998) summarizes follow.
First, human mental processes are governed by behavior, similar to biological
world. Second, neuropsychological processes may account for our mental
functions. Third, dualism separates the mind and brain, but neuroscience sees
the mind as part of the physical universe. Therefore, only physical things ignite
mental processes. Fourth, the mind is likened to a computer, which is misleading
based on current mental models. Fifth, cognitive psychologist espouse cognitive
development rather than materialism. Sixth, Cognitive development might be an
impetus for technological and social development. Gunther (1998) goes also
suggests that cognitive science is now underlying machine intelligence and it
verifies that in our intuition we are free - that our thought process is not
predetermined. In other words, our thought can be affected by what we know,
learn, and how we integrate that information.
Introduction to Instructional Design
Smith & Ragan (1999). Instructional Design. New York: Wiley. [ Chapter 1]
“Instructional design refers to the systematic and reflective process of translating
principles of learning and instruction into plans for instructional materials, activities,
information, resources, and evaluation” (Smith & Ragan, 1999). Instruction is the
facilitation of instruction towards goals. There are different types teaching, including
education (broad learning for all), training (skills oriented), teaching (education facilitated
by a person), and instruction (subset of education). This text is focused on instruction.
Design is defined as “a systematic or intensive planning and ideation process prior to the
development of something of the execution of some plan in order to solve a problem”
(Smith & Ragan, 2009). Design is typically a goal driven process, that involves multiple
perspectives and expertise. The instructional design process is oriented towards
measuring its success against the goals of the instruction. In other words, how well did
learning occur as expected. The ID process has three main components: Analysis,
strategy, and evaluation. It is a very iterative process with many interlinking parts.
Foundations of Instructional Design
Smith & Ragan (1999). Instructional Design. New York: Wiley. [ Chapters 2 ]
Foundations of instructional design provides a theoretical framework for instructional
design that includes constructivism, empiricism, and pragmatism. Constructivism is
emphasized with a focus on individual versus social constructivism. In constructivism
knowledge is constructed rather than discovered. The assumptions of constructivism
from an individual perspective are: knowledge is constructed from experience, learning
results from a personal interpretation of knowledge, and learning is an active process in
which meaning is developed on the basis of experience. Social Constructivism adds that
learning is collaborative with meaning negotiated from multiple perspectives.
Contextualism is another component of constructivism, which assumes learning should
be situated, and testing integrated into the task. On the contrary, there is empiricism,
which states that knowledge is gained through experience in the world. In the middle
are pragmatist who believe learning is a matter of common interpretation of expert
opinion (i.e. everything is a rational approach).
Some of the assumptions underlying instructional design include: having a clear goal of
what instruction will provide for learners (i.e. learning outcomes), designing for efficiency,
effectiveness, and appeal, learning may occur in many forms, principles apply across
age groups, evaluation must include information about the learner and the instructor,
and assessment should be based on individual learner performance; not in comparison
to others, and there should be congruence amongst learning goals, objectives and
assessments.
Although behaviorism influenced learning theories, the cognitive learning theories are
emphasized in this chapter. Especially, the information processing model of learning and
memory. Developmental theories such as Piaget's “ages and stages”, and Vygotsky are
incorporated too. Lastly, instructional theories are discussed, which focus on
characteristics of instruction that will support learning, rather than developmental
processes or how learning occurs.
This information is relevant for instructional designers, because instructional learning is
universal. The philosophies and theories discussed in this chapter provide perspectives
to consider when designing instruction for different audiences.
February 3, 2014 (#2)
Human Cognitive Architecture: Biological Bases of Learning and Memory;
Sensory, Short-Term, Working Memory Models
Information Processing
Driscoll, M. (2005). Psychology of Learning for Instruction (3rd ed.). Boston, MA: Allyn and Bacon. [ pp.
77-91]
One of the main points of this section is that sensory memory is temporary, although a great
deal of information registers visually and with the auditory system. This was identified through a
partial reporting technique, research using tones to indicate which content (i.e row of letters)
study participants were to report on. The partial reporting technique helped identify the
importance of attention in information processing. Further research solidified that attention
isn’t singular or black or white. Rather, “researchers have come to view attention as a resource
with limited capacity to be allocated and shared among competitive goals” (Driscoll, 2005). This
implies that learners attention is selective, but some tasks require little effort, and attention
might also be habitual. Thus, two concepts arise related to attention: selectivity and
automaticity.
Selectivity is defined by Driscoll as the learner’s ability to select and process certain information
while simultaneously ignoring other information. The criteria for selective attention include
meaning, similarity between competing tasks, task complexity or difficulty, and the ability of the
learner to control attention. Driscoll points out that if attention is selective, than managing
attention in an instructional situation requires a strategy. For example, using standard signals in
a classroom, or stimulus features such as voice inflection, tone, and novelty.
Automaticity, on the other hand is when attention becomes automatic because the task is
overlearned or sources of information are habitual (i.e. driving a car). The criteria of
automaticity include skills such as decoding, which is discussed in the context of reading.
Decoding assumes that a reader's ability to decode works is so automatic, they can focus on
extracting other meaning from content based on their goal for reading. There is also pattern
recognition and perception, which Driscoll defines as the process whereby environmental
stimuli are recognized as exemplars of concepts and principles already in memory.
There are several different models of pattern recognition, including template matching,
prototyping, and feature analysis. Prototyping and feature analysis are supported by research.
Together, the implication for pedagogy is concept learning, which “calls for presenting, first, a
best or prototypic concept example followed by a variety of examples that differ from the
prototype in systematic ways. The examples help learners to abstract meaningful dimensions of
the concept and determine which feature s are critical and invariant and which are non essential
and variable across examples” (Driscoll, 2005).
Lastly, for pattern recognition that’s not explained by the above, it relies on principles of
organization, context, and past experience. In terms of learning pattern recognition means
providing examples, and being mindful of teachers expectations of students based on
experience is helpful.
Although sensory memory is important for attention, it is part of the story because it remains
temporary. Therefore, It is important to consider how information that is temporary becomes
stored. “At this stage, concepts from long-term memory will be activated for use in making
sense of the incoming information” (Driscoll, 2005). Working memory, although with limited
capacity plays a part. The question becomes how to increase the capacity of working memory to
hold more information. This can be done through chunking. That is taking smaller parts of
information and breaking them into larger bits, or chunks. (i.e. a string of letters vs. sets of
letters). Chunking is one component, but information in working memory can be lost very
quickly (within 30 seconds). There are two processes to help transfer information from working
memory to long-term memory. They are rehearsal and encoding.
Rehearsal is repetition of information (i.e. memorizing a phone number). Encoding “refers to the
process of relating incoming information to concepts already in memory in such away that the
new material is more memorable” (Driscoll, 2005).
Biological Basis of Learning and Memory
Driscoll, M. (2005). Psychology of Learning for Instruction (3rd ed.). Boston, MA: Allyn and Bacon.
[chapter 8 ]
This chapter is rather complex and reviews how biological research relates to cognition. The two
major lines related to biological research are genetic inheritance and brain physiology. I think
the main ideas of this chapter are:
●
Evolutionary psychology rest on the assumption that the psychology of behavior is well
informed by evolutionary biology.
●
Evolved psychological mechanisms of the human mind are adapted to an ancient way of
life, not to conditions present in the modern world.
●
Evolutionary adaptations are both functional and specific.
●
An evolutionary view of learning and behavior in effecting integrates common notions
of instinctive vs. learned behaviour.
●
Human information processing mechanisms may have evolved to reflect the types of
problems faced by early humans and their ancestral environment.
●
What is learned and exhibited depends on genetic history.
●
Evolutionary psychologist might say that learning should be more group oriented to play
to the environmental factors of our ancestors, that is still present in our psychology
today.
●
Learning is distributed throughout the brain, with some localization related to memory,
presentation, and representation.
●
Attention involves selectivity.
●
○
Controlling Attentional States
○
Selectively Allocating Attentional Resources
○
Selectively Organizing Attention
Attention is not a unitary concept. Multiple techniques are necessary to get learners
attention.
●
The types of memory systems are procedural, perceptual representation, semantic,
primary and episodic.
●
Four conceptual models of the diversity of neuroscience research related to
development are:
○
Fixed circuitry and critical periods
■
The brain develops in a very specific and routine way, very quickly.
Damage to the brain can occur easily during the critical period of
development, which impacts learning.
○
Plasticity
■
There are parts of the brain that change over its lifespan.
●
■
Change is introduced by experience.
Older learners are capable of learning new things through their lives,
but doing so in a flexible manner is somewhat more difficult than it is
for younger learners.
○
Modularity
■
Memory in terms of modules.
■
Cognitive development proceeds independently in at least even
relatively autonomous domains (i.e language, music, logicalmathematical reasoning, spatial processing, bodily-kinesthetic activity,
interpersonal knowledge, and intrapersonal knowledge).
■
●
These domains are activated in context.
Implications of Neurophysiology for learning and instruction
○
Cognitive Functions are differentiated.
○
The brain is relatively plastic in nature.
○
Language may be biologically preprogrammed.
○
Learning disorders may have a neurobiological basis.
Schema Acquisition and Sources of Cognitive Load.
Kalyuga, S. (2010). Schema Acquisition and Sources of Cognitive Load. In J.L. Plass, R. Moreno, & R.
Brünken, Cognitive Load Theory, New York: Cambridge. [chapter 3]
The chapter discusses sources of cognitive load in instruction, in which cognitive load is defined
by the activity-based demands on working memory during goal-based learning. The chapter
begins with an explanation of schematic knowledge structures. Schemas, according to Kalyuga,
“represent knowledge as stable patterns of relationships between elements describing some
classes of structures that are abstracted from specific instances and used to categorize such
instances” (Kalyuga, 2010). In other words, a schema is a unit of memory that helps us acquire
and store knowledge to move around the limitations of working memory.
The concept of schemas and circumventing working memory is paramount to the chapter, which
largely focuses on extraneous cognitive load, sources of extraneous cognitive load, and how to
reduce it as a result of learning as schema acquisition. In other words, “from a cognitive load
perspective, the major goal of learning is the acquisition and automation of schematic
knowledge structures in long-term memory” (Kalyuga, 2010). Therefore, the organization and
presentation or learning tasks is essential and must pay attention limitations of the human
cognitive processing system. Principally, “CLT assumes that a proper allocation of cognitive
resources is critical to learning” (Kalyuga, 2010). Thus, reducing or eliminating extraneous load is
important for balancing the cognitive process, schema acquisition, and effective learning.
Extraneous cognitive load is a diversion of resources for activities related to the learning task.
Extraneous load results from “insufficient learning knowledge base or instructional guidance, an
overlapping knowledge base and instructional guidance, excessive step-size of changes in the
knowledge base, or interrelated instructional representations that are separated in space
and/or time” (Kalyuga, 2010). To reduce or mitigate these misplaced cognitive resources a set of
general guidelines were recommended, including providing examples, adapting to changing
levels of learners expertise, and scaffolding knowledge base change.
Working Memory
Baddeley, A.D. (1992). Working memory. Science, 255, 556-559.
This article discusses working memory, which “refers to the brain system that provides
temporary storage and manipulation of the information necessary for such complex cognitive
tasks as language comprehension, learning, and reasoning” (Baddeley, 1992). The key point in
this definition is that working memory is not permanent, and it is task oriented and specific.
The second main point of the article is the distinction between working memory as a single
unitary function, or multi-pronged system. There is research in both North America and Europe
exploring both approaches, and the deficits. On one, hand is working memory as concurrent or
combined storage and information processing. On the other hand, is working memory as a dualtask methodology. However, Baddeley, proposes a tripartite system that is comprised of three
components of memory, the Central Executive, supported by the Visuospatial sketchpad (i.e.
imagery), and the Phonological loop (i.e. speech based patterns). Baddeley’s system “suggest
that the coordination of resources is the prime function of working memory” (Baddeley, 1992).
In other words, the Central Executive coordinates the slave systems.
I’m not clear however, if this is a definitive system or if it’s hypothesized and still being research.
The concept of working memory, however seems pertinent to learning because of its
relationship to processing complex tasks.
The Role of Questions in Learning (Optional)
Wager, W., & Mory, E. H. (1993). The role of questions in learning. In J. V. Dempsey & G.
C. Sales (Eds.), Interactive instruction and feedback (pp. 55–73). Englewood Cliffs,
N.J.: Educational Technology Publications.
The main idea of this chapter is that feedback in instruction is ineffective, and can be
improved to facilitate learning. Feedback can be improved if asked “Feedback for
what?”, and for what stage of Gagne’s information processing model. Wager and Mory
use “Gange’s model of information processing as a framework for analyzing the different
roles questions might serve in learning because of the role stimuli play in their model.
“Stimuli from the learner’s environment activate receptors which transmit information to
the central nervous system” (Wager & Mory, 1993). In other words, questions act as
stimuli that activate the information processing model at its various stages. According to
Wager and Mory (1993) questions server three general functions in learning: (1) to
establish and maintain attention, (2) to facilitate encoding, and (3), to prepare for
rehearsal.
The chapter breaks down the role of questions and feedback based on each set up of the
process as follows.
Reception of Stimuli by Receptors
Questions act as an external event to grab the learners attention. Feedback for questions is
largely rhetoric.
Registration of Information by Sensory Registers
Questions activate the processes of executive control. There are two types of questions
referred to in the text, verbatim and conceptual questions. Verbatim questions, in posttest
scenarios, resulted in less recall of information then conceptual questions. “Conceptual
questions also increase performance on verbatim recall tasks when that information is
used to answer higher-order questions” (Wager & Mory, 1993). Pre-questions help
learners select or reject information based on relevancy. Feedback at this stage might
follow the questions.
Selective Perception for Storage in Short-Term Memory
At this point, questions “aide the student in recalling prerequisites to short-term memory
in order to enable integration of previously learned formation with new information or
skills” (Wager & Mory, 1993). In other words, questions help learners scan their longterm memory for related concepts, retrieve prior knowledge, and prepare for the new
information. “Feedback would ensure that skills for understanding new information are in
place and can be retrieved” (Wagner & Mory, 1993). In other words, Feedback confirms
what learners know already, and helps bridge what is needed between short-term and
long-term memory for growth.
Rehearsal to Maintain Information in Short-Term Memory
Questions serve to help learners rehearse information for better retention in short-term
memory. Since chunking and segmentation are forms of presenting information for
improved rehearsal, questions can also help learners organize information accordingly.
Semantic Encoding for Storage in Long-Term Memory
At this stage questions questions aid in the integration new information into the long term
memory by increasing encoding speeds, aiding in tuning, and deepen links between new
and existing schemas. Feedback helps reinforce learning and prevent forgetting the
information in the future.
Response Generation
Feedback provides corrective information or reinforcement of knowledge.
Performance In the Learner’s Environment
Questions are used via posttest to rate performance. According to Wager and Mory,
Feedback is used to “confirm that the skill has been generalized to a range of situations.
Questions/Feedback in terms of assessing performance, help shape a learners perception
of abilities.
Control of Processes through Executive Strategies
Questions and feedback act as a vehicle of knowledge transfer.
February 11, 2013 (#3)
Human Cognitive Architecture: Long-term Memory Models, Dual Coding
Theory
Clark, J.M., & Paivio, A. (1991). Dual Coding Theory and Education. Educational Psychology Review,
3, 149-177.
This article discusses Dual Coding Theory, which is a “theory of basic psychological mechanism
that permits unified explanation for diverse educational phenomena” (Clark & Paivio, 1991), and
its application to education. According to Clark and Paivio, DCT examines the mental system,
particularly the the verbal and nonverbal mental systems that are designed to process imagery
and linguistics. Some of the main ideas of the paper follow.
● DCT concerns basic mental structures and process. The structures are associative
networks of verbal and imaginal representation, and the processes concern the
development and activation of those structures, including the effects of context on the
spread of activation among representation.
● The verbal system contains verbal codes, or symbols that donte physical objects.
● The non-verbal system contains images for non linguistic objects and events. These
symbols are related to events they represent.
● The two systems, verbal and nonverbal are linked via referential connections. These links
allows one to connect images to words and words to images.
● Another link, associative connections, allows one to create representations within the
verbal and nonverbal systems.
● The development and activation of verbal and imaginal associative structures are
governed by DCT’s processing assumptions.
○ Individual verbal and imaginal representations vary in their activity levels (some
are high, and some are very low).
● The central role of mental representations and their interconnections is past experience.
● Relative activation of the nonverbal system is particularly important for understanding
human behavior - imagery system has unique theoretical and empirical properties.
● A second determinant of imagery processing is the imagery value or correctness of the
material being study. In other words, imagery and correctness reflect the availability and
strength of word-to-image referential connections.
● Individual differences affect nonverbal processing.
● Instructions and related context effects influence: relative activation (nonverbal systems,
and patterns of activation within the verbal and nonverbal systems (i.e. priming students
for a response).
○ Selective priming
○ Effects of context
● Unified explanations for educational psychology of DCT
○ Are focused on diverse educational phenomena that show the collective
contribution of imagery and verbal processes to human behavior and experience.
○ Probability and ease of image arousal plays an important role in the representation
of text meaning (hypothesis).
○ Predicts that word concreteness and imagery value should be central variables in
cognitive and educational tasks related to meaning.
○ Central role of nonverbal processes is to help decipher text. For educational
knowledge, this might be represented my visual information (i.e. maps, charts,
etc.).
○ Findings support: memory for words and text benefit from elaborate imagery,
concreteness, and associative organization, although the collective effect of these
processes is unknown.
○ DCT emphasis on verbal and imaginal processes fits well with several cognitive
models of learning and memory in the educational domain.
For Whom a Picture is Worth a Thousand Words? Extensions of Dual-Coding Theory
of Multimedia Learning
Mayer, R.E., Sims, V.K. (1994). For whom is a picture worth a thousand words? Extensions of a
dual-coding theory of multimedia learning. Journal of Educational Psychology, 86,
389-401.
Although Dual Coding theory looks at verbal and nonverbal systems and processes,
especially linguistics and imagery, it doesn’t do so in the context of Multimedia
Learning. Therefore, Mayer and Sims try to fill that gap through their theory of
Multimedia Learning. It examines how individual differences affect student learning
from visual and verbal instruction, and the role of spatial ability in learning from words
and pictures about how a (respiratory) system works. The goal of their research is to help
students use visual and verbal information to understand, or transfer information to new
situations, in scientific explanations. Three research issues are addressed and
summarized: contiguity effect, the role of experience in contiguity effect, and the role of
ability in the contiguity effect. Spatial ability in learning from words and pictures is also
referenced.
Mayer and Sims (1994) define multimedia as “Multimodal” meaning the learner uses two
modalities, visual and verbal processing, in multimedia learning. The dual coding model
of multimedia learning represents is adopted from Paivio’s DCT model. It uses three
process approach, building a verbal representational connection (verbal encoding),
building a visual representational connection (visual encoding), and the construction of
referential connections (mapping of structural relations). In other words, verbal or visual
presentations enter the working memory, and referential connections are created between
the verbal and visual systems, which ultimately get encoded into the long term memory.
Another component is performance, which assess knowledge transfer and retention of
visual and verbal presentations.
Mayer and Sims research explores how Multimedia Learning is impacted when
information is presented contiguously or non-contiguously, how prior domain knowledge
effects contiguity, especially with low level learners (minimal domain knowledge), and
how high or low spatial ability effects contiguity. In other words, what is the effect on
performance related to contiguity, experience, and spatial ability.
The results of their research supported their predictions, and was in line with other
research on DCT. Students with related expertise may not need visual aids with text.
When visual and verbal information was presented at the same time, inexperienced
students performed better. The simultaneous presentation of visual and verbal
information helped build referential connections more easily. Domain specific knowledge
compensated for a lack of information being presented at the same time (i.e.
uncoordinated instruction) in high-level learners. Low-spatial students must work harder
to build connections between animations and narration than high-spatial ability students.
In other words “Inexperienced students benefit from pictures being coordinated with
words. Second, students who possess high levels of spatial ability are more likely than
low-spatial ability students to be able to build mental connections between visually based
and verbally based representations. Therefore, mainly high-spatial ability students benefit
from pictures being coordinated with words. Low-experience, high-spatial ability
students are the most likely to benefit from instruction that carefully synchronizes the
presentation of verbal and visual forms of scientific explanation” (Mayer & Sims, 1994).
According to, Mayer and Sims (1994), their theory is an extension of Pavio’s DCT to
problem solving. In their version of DCT, Mayer and Sims have different assumptions
about the cognitive assumptions for problems solving. That is understanding, measured
by problem solving, occurs when referential connections between verbal and visual
representations are made.
Long-term Memory
Driscoll, M. (2005). Psychology of Learning for Instruction (3rd ed.). Boston, MA: Allyn and Bacon.
[pp. 91-110]
“Long-term memory involves selectively receiving information, retaining certain aspects of that
information, and retrieving that information as required” (Driscoll, 2005). There are two types of
long-term memory, episodic and semantic. Episodic is specific and semantic is general, and can
be recalled separate from when it was learned. Semantic memory is most relevant to educators
because instruction is targeted more often then it is generalized.
There are several models of long-term memory: network models, feature comparison models,
propositional models, and parallel distributed processing models.
Network Models “assume the existence of nodes in memory, which correspond to concepts - i.e.
things and properties. These nodes are thought to be interconnected in a vast network structure
that represents learned relationships among concepts” (as cited in Driscoll, 2005). The
advantage of network models is it favors individual differences, but the commonality of
concepts is a concern.
Feature comparison models suggest that concepts in memory are stored with sets of defining
and/or characteristic features. This model is limited because it’s not economical and inflexible
when accounting for semantics. Features can be fuzzy, and therefore require a great deal of
time and energy to learn. How something is stated conceptually changes its
defining/characteristic feature sets.
Propositional Models adopt the network model, but instead of “concept nodes compromising
the basic unit of knowledge that is stored in memory, propositional models take this basic unit
to be the proposition. A proposition is a combination of concepts that has a subject and
predicate (.e. a bird has wings)” (as cited in Driscoll, 2005). According to Driscoll (2005), John
Anderson developed adaptive control of thought, which is a widely accepted network model of
memory that emphasizes propositional structure. It includes a system for storing procedural
knowledge, and his system plays to the changing nature of cognition.
Parallel distributed processing models are models in which parallel cognitive operations occur
simultaneously, according to Driscoll (2005). Therefore the search task (for a concept) is
distributed across all nodes simultaneously and not sequentially. At the core of PDP, are
connectionist models of long-term memory, in which researchers try to understand cognition
from a behavioral level. PDP helps explain human information processing, offer a convenient
way to incorporate goals into the dynamics of information processing systems, and have
potential to explain cognitive development, according to Driscoll (2005). On the other hand, PDP
models “lack forthcoming evidence to support PDP models as a mirror of neural processes in the
brain” (as cited in Driscoll, 2005). In other words, a unitary processing model may be insufficient
to represent the brain.
Other areas of this chapter cover how we retrieve learned information from long-term memory,
how it is forgotten, and the implications of CIP for instruction. The process of retrieval from
long-term memory involves bringing back previously learned information for to understand
some new input or to make a response. Retrieval of learned information is accomplished
through recall or recognitions. Recall is retrieval with no cues or hints, while recognition is the
contrary - cues and hints are provided. At the point of recall, there are conditions that influence
retrieval. First, encoding specificity - whatever cues are used by a learner to facilitate encoding
will also serve as the best retrieval cue for that information at test time (as cited in Driscoll,
2005). Second, state-dependent learning, which suggest that “information learned in a certain
state of mind is best recalled in the same state of mind” (as cited in Driscoll, 2005).
How is memory forgotten? Either failure to encode (information was never learned to begin
with), failure to retrieve (unable to access prior knowledge) or interference (something got in
the way of encoding. The importance of reminding us that memory is forgotten, is to plan our
instruction to prevent any of the aforementioned failure mechanisms of memory. For example,
practice helps prevent failure to retrieve.
Lastly are implications of CIP for instruction. Generally, these are providing organized
instruction, arranging extensive and variable practice, enhancing learners encoding and
memory, and enhancing learners’ self-control of information processing. Driscoll (2005)
elaborates on the last item by suggesting metacognition as a strategy (i.e. self-awareness) of
which there are many variables ranging from personal attributes of the individual to task
variables to strategy variables to self regulatory skills. The two of most importance are domain
specific knowledge and self-regulator strategies.
In summation, what we know about long-term memory is valuable, but there are many ways to
look at memory that are and will prove useful now and moving forward.
Return of the mental image: Are there really pictures in the brain?
Pylyshyn, Z.W. (2003). Return of the mental image: Are there really pictures in the brain? Trends in
Cognitive Science, 7, 113-118.
According to Pylyshyn (2003) there is a revival of the picture theory, based in neuroscience,
which is the idea that mental imagery involves a special format of thought, one that is pictorial
in nature. He argues that evidence from neural imaging and clinical neuropsychology provide
little evidence to support the resurgence of the picture theory because it doesn’t address the
format of the mental image.
Pylyshyn (2003), also believes that pictorial forms of thought differ from other forms of
reasoning. Pictorial forms of thought are content based, rather than related to form. In other
words, there is a difference in thinking about what a picture means versus what it looks like. This
is a distinction that isn’t clear in current research on picture theory.
In fact, the current research is a “ ‘null hypothesis’ ” because is makes no assumptions about
format - it appeals only to the tacit knowledge that people have about how things tend to
happen in the world together with certain basic psychophysical skills” (Pylyshyn, 2003). In other
words, experimental findings to date demonstrate the ability of individuals to compare mental
images to what they know in the real world, and form as close a resemblance as possible.
Neuroscience research specifically assumes “that to have a mental images to is to project twodimensional moving pictures onto the surface of your visual cortex” (Pylyshyn, 2003). Such
findings don’t account that imagery and vision may involve similar forms of representation
without being pictorial in either case. Also, it ignores differences between retinal and cortical
images and mental images, such as access and interpretation and a dissociation between vision
and imagery capacities in the visual cortex. In other words, the idea that our brain projects
images like a movie ignores form of representation of hard science about parts of our brain
engaged in producing mental imagery.
Pylyshyn (2003) also discusses spatial characteristics of mental imagery. That is, orienting a
mental image in relationship to to one another, or objects nearby. The ideal being that we
exploit spatial properties in the real world when we superimpose a mental image. This is true for
scenarios when our eyes are open or closed, and may explain how we see images separately. It’s
not clear that spatial imagery supports or doesn’t support the picture theory. However, it is an
interesting representation of mental images that doesn’t require coordination with a reference
in the brain.
It seems because of the connection between an image in the mind’s eye and our conscience, it is
difficult to explain how images are created in the brain. Pylyshyn (2003) seems to think neither
picture theory or the results of imagery experiments help much, if at all.
February 18 (#4)
Meaningful Learning, Schema Theory, Situated Cognition
Meaningful Learning
Driscoll, M. (2005). Psychology of Learning for Instruction (3rd ed.). Boston, MA: Allyn and Bacon.
[chapters 4]
Aubel’s meaningful, reception learning theory addresses how “learners actively
interpret their experiences using certain internal, cognitive operations” (as cited in
Driscoll, 2005). Ausubel differentiates learning as either reception, or discovery learning,
or rote or meaningful learning. According Ausubel, rote or meaningful learning can
occur in reception or discovery learning. Meaningful learning is what was important to
Ausubel and “refers to the process of relating potentially meaningful information to
what the learner already knows in a nonarbitrary and substantive way” (Driscoll, 2005).
The emphasis being prior knowledge is the critical component of this definition.
Therefore, Ausubel conceptualized hypothetical constructs of memory structure and
learning processes to support meaningful learning. These include the learner's cognitive
structure, or integrated body of knowledge, and provide a framework for relating
potentially meaningful information. A key characteristic of this framework includes
anchoring ideas, or a hook to which new information is assimilated through the process
of meaningful learning.
The process of meaningful learning describes how new information is likely to be added
to the existing cognitive structure. This process occurs through derivative and
correlative subsumption or superordinate and combinatorial learning. Ausubel coined
the process of retention the assimilation theory.
Readiness for learning is another component of the meaningful learning process. It
states that a learner must possess a relevant, stable, and organized cognitive structure
to be ready to learn new material. Two influences on a person's readiness to learn,
however, include age and diversity. In terms of instruction, younger learners require
more concrete instruction. Of course, insatiating learning also depends on our mental
schema.
According to Driscoll (2005), a schema is a data structure for representing the generic
concepts stored in memory. Schemas are packets of knowledge, and schema theory is a
theory of how these packets are represented and how that representation facilitates the
use of the knowledge in particular ways. In other words, schema help us process
information to guide our actions.
Related to meaningful learning, schema are important for processing new information
through accretion, tuning, and restructuring. Another aspect of schema is cognitive load,
in which the goal is to reduce the strain on working memory from extraneous cognitive
loads, to engage the appropriate schema for learning.
In terms of implication for instruction, meaningful learning and schema theory both
emphasize prior knowledge. The question then becomes, how do you develop
instruction that taps into what learners might already know about a domain. Driscoll
calls this activating prior knowledge, and discusses the concepts of advanced organizers
and schema signals. Advanced organizers help to bridge a gap between what the
learners already know and need to know for meaningful learning. Schema signals are
cues to enact appropriate or inappropriate behavior in instructional situations.
Lastly, Driscoll reviews how to make instructional materials meaningful by discussing
concepts such as comparative organizers, elaboration, and conceptual and pedagogical
models: All with the goal of aiding in transfer of knowledge.
Situated Cognition
Driscoll, M. (2005). Psychology of Learning for Instruction (3rd ed.). Boston, MA: Allyn and Bacon.
[chapters 5 ]
Situated Cognition is preceded by an ecological approach to perception, critical
pedagogy, and everyday cognition. It differs from other learning theories because of its
considers social and cultural influences on learning. It “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). The elements of the theory of situated cognition include cultural
context, co-constitutive nator of individual-action environment, and multiple knowledge
communities (as cited in Driscoll, 2005). In brief, learning is viewed as a social activity.
The primary process of situated cognition is legitimate peripheral participation, which
defines ways of belonging to a community. It refers to the social organization and
control of resources. It distinguishes between old and new members of a community. It
“encompasses the multiple, varied, more-or-less engaged ways of being located into the
fields of participation defined by a community” (as cited in Driscoll, 2005). In the case of
this theory, the focus is on the individual and their means of participating in a
community.
There are clear implications for situated cognition in instruction including cognitive
apprenticeship, anchored instruction, learning communities, and assessment in situ.
Cognition in the Wild (Optional)
Hutchins, E. (1996). Introduction. In Cognition in the wild (pp. 1–7). Cambridge, MA: MIT Press.
This narrative is a story about a naval ship, the U.S.S Palau, that was returning to port
when it lost steam and power. It details the events of the crew over the course of only a
few moments, while they safely navigated the vessel two miles to port without steering,
radar, and other critical systems that are common and necessary to steer and dock a
ship of its size.
In particular, this story tells the tale of how the crew reacted, what steps were taken to
problem solve, including communication and leadership assignments, and ultimately
how they safely anchored the ship without any casualties (despite a near miss with a
passing sailboat). These steps included a series of complicated maneuvers that are
typically managed by the ships steam powered and electrical systems. For example,
adjusting the rudder was done manually.
More to the point, the series of events illustrated are demonstrative of another major
system at play - human cognition. Human cognition is discussed throughout the book
through the telling of this story. “It is about human cognition - especially human
cognition in settings like this one, where the problems that individuals confront and the
means of solving them are culturally structured and where no individual acting alone is
entirely responsible for the outcomes that are meaningful to the society at large”
(Hutchins, 199). In other words, how do individuals solve problems in a particular
culture or structure, when they aren’t individually responsible for the outcome, but the
outcome impacts many.
February 25, 2014 (#5)
Cognitive Load Theory
Implications of Cognitive Load Theory for Multimedia Learning
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York: Cambridge.
[Chapter 2]
The premise of this chapter is how working memory is limited when dealing with
elemental information, and how long-term memory addresses these limitations. Sweller
(2005) explains that familiar information in long-term memory can act as central
executive and eliminate the need for working memory. Sweller (2005) also suggests
that instruction might substitute for the missing central executive when dealing with
novel information.
Since the limitation on working memory relates to new information, this concept is
particularly important for novice learners, in which their goal is to acquire a similar
fluency to experts in a particular domain. To accomplish this, they must transfer new
information to long-term memory where all knowledge is structured in long-term
memory through schema. Schema are “Cognitive constructs that allow multiple
elements of information to be categorised as a single element” (Sweller, 2005). In other
words, to achieve expert performance, the acquisition of schema are required. In the
context of multimedia learning knowledge held in the long-term memory can be
pictorial or verbal, spoken word, or written.
Again, long-term memory is important because of the limits of working memory.
Working memory is limited, especially with elemental information because it increases
cognitive load. Cognitive load is increased as a result of inquiry based learning in which
information is randomly generated, and then tested against available knowledge. This is
a long, slow, and ineffective method for acquiring knowledge. To extend this to
multimedia learning, information presented that requires the use of multiple
processors, makes facilitating learning difficult.
The limitations discussed related to working memory do not apply to long-term
memory. In fact, there are no duration or capacity limitations of receiving sensory
information in long-term memory. Therefore, information in long-term memory
supports working memory, but is highly dependent on its organization. In fact, the
synthesis of information between working and long-term memory correlate to an
understanding of new information. This is accomplished through working memory
playing the role of central executive. It processes new information with familiar
information to facilitate understanding.
A concept important to what is discussed above is the Cognitive Load Theory. CLT
explains how extraneous cognitive load prevents the effective use of working memory,
whereas germane load does not. Extraneous cognitive load prevents the schema
construction and automation of knowledge. Ineffective instructional design creates
extraneous cognitive load and ignore limits of working memory, while effective
instructional design promotes cognitive load that aids schema construction and
automation. Several cognitive load effects are discussed briefly, including the splitattention effect, the modality effect the redundancy effect, and the expertise reversal
effect.
In summation, research findings about cognitive load have influenced instructional
design, especially when related to novel information.
The Split-Attention Principle in Multimedia Learning
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York: Cambridge.
[Chapter 8]
Split attention refers to instruction that “requires the learner to split their attention
between and mentally integrate several sources of physically or temporally disparate
information, where each source of information is essential for understanding the
material” (Ayres & Sweller, 2005). In other words, learners are processing two separate
streams at once. In effect, this increases extraneous cognitive load. It is ideal of
information is presented in an integrated format to begin with. It is important to note,
as Ayres and Sweller (2005) do the split attention effect can only be obtained when
multiple sources of information are essential for understanding and so cannot be
understood in isolation. In other words, there are specific criteria for when the split
attention effect might occur; it is not universal.
Research supports the split-attention effect, and also discovered the important role of
element interactivity. That is “the number of elements that must be simultaneously
processed in working memory in order to understand the information” (Ayres & Sweller,
2005). What was found is that element interactivity affects intrinsic cognitive load,
whereas the split-attention effects is considered extraneous cognitive load because it is
created by the format of the instructional materials. From this, we can deduce the splitattention effect results from poorly designed instruction, and doesn’t play to the limits
of working memory.
The research also brought light to a gap in the split-attention principle, which is the
split-attention effect also occurs temporally. In other words, it isn’t just sources of
information, but information presented in different time and space that results in
increasing cognitive load. If information is temporally integrated extraneous cognitive
load will decrease, and so will the need for mental integration.
The implications for instructional design are clear. “Where instruction includes multiple
sources of information that must be mentally integrated in order to be intelligible, those
sources of information should be both physically and temporally integrated in order to
reduce unnecessary search for ferrets and to reduce extraneous cognitive load” (Ayres
& Sweller, 2005). In other words, physical integration of sources is part of good
instructional design. However, Ayres & Sweller (2005) suggest several key points about
the split-attention principle to keep in mind.
1. If all sources of information are intelligible in isolation and redundant,
elimination of redundancy rather than physical integration should be pursued.
2. The principle only applies to high element interactivity.
3. The intelligibility of information also depends on learner characteristics and not
just elemental interactivity, etc.
The Redundancy Principle in Multimedia Learning
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York: Cambridge.
[Chapter 10]
This chapter discusses the redundancy principle. “The redundancy principle suggest that
redundant material interferes with rather than facilitates learning. Redundancy occurs
when the same information is presented in multiple forms or is unnecessarily
elaborated” (as cited in Mayer, 2005). This definition implies there are two types of
redundancy, and that removing redundant information enhances learning. The two
types of redundancy are multiple forms of media, and elaboration of information.
By removing multiple forms or unnecessary information, extraneous cognitive load on
the learner is reduced. As we learned from the split-attention principle, this is
particularly true when new information is being processed. The cognitive load theory
supports the redundancy principle.
Research depicted in the article also supports this principle. In particular, a critical
finding from the experiments reviewed is that a concept and how it is explained further
need to be coordinated. Another discovery is that redundancy is dependent on content.
In other words, what is replicated or drawn out in one discipline, and considered
impairment to learning, might be beneficial to learning in a different context. Therefore,
redundancy is context specific, which is why it’s difficult to summarize ways to avoid
redundancy. Also, the learners play a role in determining if there is too much of the
same information. This last point hints at relationship between the redundancy effect
and the expertise reversal effect ( i.e. the more expert one is, the less valuable
redundancy becomes).
In brief, removing redundancy facilitates learning and information should not be
presented in multiple forms, or elaborated on unless it is required by context.
Cognitive Load Theory, Learning Difficulty, and Instructional Design
Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and
Instruction, 4, 295-312.
This article explores what makes learning difficult. Sweller (1994) establishes learning as
“schema acquisition and transfer of learned procedures for controlled to automatic
processing”. He places an important emphasis on schemas. Schemas, or units of
knowledge, determine how new information is handled.
Schema acquisition is often thought of as a conscience activity, or an automatic process.
However, the transition between the two is slow. It progresses as domain expertise
evolves, which is an important note because automatic processing is required for
optimal mental performance. The process described is another way of explaining the
working memory. It is the place between active learning and retention of information in
the long term memory. The working memory, however, has severe limitations and if it’s
not used effectively inhibits learning.
Therefore, learning must be facilitated, and Sweller provides examples of several
conditions for doing so, including goal-free problems and worked examples. What these
demonstrate is that proper design of instruction reduces load on the working memory
and improves its efficiency for schema acquisition. This is referred to as Cognitive Load
Theory.
Cognitive load can be attributed to extraneous load as the result of instructional design,
or the result of intrinsic load required by learners to process instruction. Learning is
broken down by Swell as elements. The extent to which elements require association for
learning is considered elemental interactivity. The distinction between low and high
element interactivity is part of extraneous intrinsic cognitive load. High element
interactivity is a primary concern for extraneous cognitive load. This is an important
distinction too because Swell suggest that understanding, and not just learning, mostly
occurs with high element interactivity.
It appears that material that CLT really applies to has elements with a lot of associations
because of the complex information related to learning that learners are trying to
process. In other words, when something has lower element interactivity it doesn’t
require heavy processes. I suspect it is something that’s simple enough by the time it’s
learned, we didn’t reach capacity.
Direct Measurement of Cognitive Load in Multimedia Learning (Optional)
Brünken, R., Plass, J.L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia
learning. Educational Psychologist, 38, 53-61.
Until now Cognitive Load Theory provided an explanation for limitations of working
memory and learning. CLT also informed how to develop instruction. However, a more
legitimate measurement is needed to validate the theoretical assumptions of
Multimedia Learning and how it affects Cognitive Load. This article discusses a new way
of measuring Cognitive Load in Multimedia Learning, called the Dual-Task Method.
The basis for this measurement is free cognitive resources. “If the difference between
the total cognitive load and the processing capacity of the visual or auditory working
memory approaches zero, then the learner experiences a high-cognitive load or
overload”. In other words, how much space remains to process information is a
measurement for extraneous load.
How information is processed in Multimedia Learning is an important component of the
Dual-Task Method. It is evaluating the differences in the modality effect. “Focusing on
the sensory modality of information, this principle states that knowledge acquisition is
better facilitated by material presented in a format that simultaneously uses the
auditory and the visual sensory modality then by a format that uses only the visual
modality” (as cited in Brunken, et al., 2003). In other words, what is the Cognitive Load
in multimedia when visual and auditory information is processed together, rather than
multiple visual materials.
“The significance of these experiments is that we were able to show, for the first time in
the domain of learning with complex multimedia systems, that the differences in
learning outcome found in the modality effect are indeed related to different levels of
cognitive load induced by different presentation formats” (Brunken, et al., 2003). The
findings from experimentation measuring CLT in multimedia instruction were reflective
of general theoretical assumptions. Learners experience extraneous cognitive load when
using sound and imagery sensory vs. only visual.
According to Brunken (2003)the dual-task method isn’t flawless. There were some
issues discovered such as a dependency on the sensory modality of the information, the
use of reaction time measures requires within-subject designs, and the impact of the
secondary task on the learning outcomes in the primary tasks. The authors also
discovered that to reduce Cognitive Load, the instructional materials should match the
level of prior knowledge of a learner.
Overall, the Dual-Task Method was as successful tool for evaluating CL in Multimedia
Learning.
March 4, 2014 (#6)
Cognitive Theory of Multimedia Learning; Integrated Model of Picture &
Text Comprehension
Introduction to Multimedia Learning (Chapter 1)
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York: Cambridge.
[Chapter 1]
This chapter provides an overview of multimedia and multimedia learning as a basis for
the remainder of the book. The premise of the chapter is that “people can learn more
deeply from words and pictures than from words alone” (Mayer, 2005, p. 2). Mayer
(2005), refers to this as the multimedia learning hypothesis (p. 2).
Therefore, multimedia is the presentation of pictures and text. “Multimedia Learning
occurs when people build mental representations from words and pictures” (Mayer,
2005, p. 3). In other words, how people interpret multimedia instruction determines
what is learned from multimedia. As our reading reflects, there are different views on
the best way to present multimedia for learning - single channels vs. dual channels.
What appears to be relevant, Mayer (2005) suggests, is how learners build connections
between different types of representations (p. 5).
There are two types of instruction, then, for teaching with multimedia. First, technology
centered. Second, learner centered. The chapter, and the book focus on learner
centered, which teaches based on an understanding of human cognition. Otherwise,
instruction is designed to fit to a specific technology, which, based on the history of
instruction, tends to fail.
The chapter goes on to address different metaphors for Multimedia Learning - response
strengthening, information acquisition, and knowledge construction. Mayer (2005)
“favors knowledge construction view (aide to knowledge construction) because is it
more consistent with the research base on how people learn and because it is more
consistent with my goal of promoting understanding of presented material” (p. 12). In
terms of promoting understanding, Mayer also underscores meaningful learning and the
importance of cognitive active engagement, which is important for understanding.
Cognitive Theory of Multimedia Learning (Chapter 3)
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York: Cambridge.
[Chapter 3]
Cognitive Theory of Multimedia Learning addresses how learners interpret the messages
of multimedia presentations that we learned about in Chapter 1. The three
assumptions of CTML are dual channels, limited capacity, and active processing. Dual
channels states “humans process separate channels for processing visual and auditory
information” (Mayer, 2005 p. 34). This describes the presentation-mode and sensorymodality approaches. The primary difference is whether the information is presented
verbally or nonverbally or through a sensory input (i.e. eyes, ears, etc.). Limited capacity
states “humans are limited in the amount of information that can be process in each
channel at one time” (Mayer, 2005, p. 34). This refers to the limitations of working
memory as described by Baddeley. Active processing states “humans engage in active
learning by attending to relevant incoming information, organizing selected information
into coherent mental representations, and integrating mental representations with
other knowledge” (Mayer, 2005 p. 34). Therefore, for active processing to occur
information must be structured and coherent in its presentation.
The three memory stores of CLMT are sensory memory, working memory, and longterm memory. The presented information is process through the senses, organized in
the working memory, and paired with prior knowledge from the long-term memory. The
five related processes are selecting relevant words, selecting relevant images, organizing
selected words, organizing selected images, and integrating representations. The model
has five forms of representation: words and pictures, acoustic and iconic, sounds and
images, verbal model and pictorial model in working memory, and knowledge in the
long term memory. In this process, the learner identifies verbal and pictorial
representations to organize information, and integrate it into a coherent mental model.
The CLMT takes elements of other information processing models but elaborates to
focus on elements of multimedia learning as described above.
An Integrated Model of Text and Picture Comprehension (Chapter 4)
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York: Cambridge.
[Chapter 4]
As previously discussed multimedia learning involves processing and integration of
different forms of representation. There are two types of forms of representation
external and internal, which can be narrowed down to either descriptions, consisting of
symbols and work well for abstract concepts, or depictions consisting of signs and work
well for concrete information.
As a result of these representations, a structure was proposed for processing
information. It consists of a text and picture channel with various elements to inspect
and construct mental models or represented information. In other words, information is
processed through a series of representations, and analyzed at the symbolic or structure
mapping level. In this process “Learners understand text and pictures (and) they
construct multiple mental representations in a cognitive system, which has a specific
architecture” (Mayer, 2005, p. 54). These are referred to as memory systems.
Memory systems include working memory, sensory registers, and long-term memory.
Each plays a role in the comprehension of text and pictures. In particular, the working
memory has capacity limitations, sensory registers are a vehicle for incoming
information only, and long-term memory implies prior knowledge is required to
understand words and images. Within each memory system, a process occurs to
understand text and pictures. This is called the Integrative model of text and picture
comprehension (ITPC). “The model...refers to the single or combined comprehension of
written text, spoken text, visual pictures, and auditory pictures (i.e. sound images)
(Mayer, 2005, p. 56).
The ITPC model has several positive attributes. It enacts the modality effect, which
reduces split-attention in how it combines pictures and text. It also employs the picturetext-sequencing effect that makes descriptive narratives of pictures easier to
understand. These directly benefit learners with low prior knowledge. On the other
hand, there is risk of redundancy and expert reversal effect, among other concerns.
The ITPC model is similar to the CTML model in some of its principles for instructional
design. These principles include multimedia, spatial contiguity, temporal contiguity,
modality, specific redundancy, and coherence. In other words, keep instruction simple,
organized, clear. The ITPC model adds picture-text sequencing, structure mapping,
general redundancy as principles, and the control of processing principle.
How do we learn from pictures and text? That is what the ITPC aims to answer. What it
does do is provide a cognitive structure for processing multimedia forms of
representation. By doing so, it helps guide our design of multimedia instruction for
optimal comprehension. There remains room to improve, and research is needed to
validate this claim.
Animations Need Narrations: An Experimental Test of a Dual-coding Hypothesis
(Optional)
Mayer, R.E., & Anderson, B. (1991). Animations Need Narrations: An Experimental Test of a
Dual-coding Hypothesis. Journal of Educational Psychology, 3, 484-490
This experiment addresses the question of efficacy of computer-based animations for
understanding scientific explanations. The research is based on previous studies by
Mayer that demonstrated words with pictures helped students perform better on recall
tests. In this experiment, these studies are extended by adding animations. Another
important aspect of this study is that it extends the dual coding theory by focusing on
problem-solving transfer, rather than only recall. The authors of the research are trying
to evaluate if the same links between pictures and words are valuable for understanding
science scenarios. In this case, they are using the example of a bike pump animated in
various ways (i.e. animation with narrations, spoken word only, animation only,
animation before text, et.).
The experiments examined three hypotheses: single-code hypothesis (words and
pictures are encoded as the same), separate dual-code hypothesis (words and pictures
have distinct representations), and integrated dual-code hypothesis (words and pictures
are encoded separately and learners build connections between them). The dual code
hypothesis is most important in this experiment because it takes elements of dualcoding theory, and is predicted to have the most impact on learning with animations. In
fact, the predictions for that experiment are “words-with pictures group will perform
better than the words-only, pictures only, or control groups on problem solving
transfer” (Mayer & Anderson, 1991, p. 486). In brief, the results validated this
prediction. Animations with verbal narration and visual animation had better outcomes
for problem-solving transfer. This research is not exhausting and leaves room for
additional discovery, especially in terms of delayed testing. What it does highlight is
“speech overlay presented concurrently with an animated sequence may have helped
students to build the needed connections between words and pictures” (Mayer &
Anderson, 1991, p. 490). This is important for instructional designers so they understand
how to build animations to improve knowledge transfer.
March 10, 2014 (#7)
Individual Learner Characteristics, Expertise Reversal
Individual Differences and Cognitive Load
Plass, J.L. & Kalyuga, S., & Leutner, D. (2010). Individual Differences and Cognitive
Load Theory. In J. L. Plass, R. Moreno, & R. Brünken (Eds.), Cognitive Load
Theory. New York: Cambridge. [ chapter 4 ]
How do we account for individual differences when designing instructional
material? This chapter addresses aptitude-treatment interactions, “The study of
influence of individual differences on learning” (Plass, Kalyuga, & Leutner, 2010).
In particular, Plass, et al. (2010) emphasis the role of prior knowledge and spatial
abilities because of the relationship between these individual differences and
working memory (p. 66). This entire discussion is from the perspective of
Cognitive Load, with a focus on information processing as a category of
individual differences in learners.
Prior knowledge is highlighted as an individual difference because instruction can
be designed for novice or expert learners. However, when instruction designed
for novice learners becomes ineffective for expert learners, it increases
extraneous cognitive load. This is known as the expertise reversal effect, and
processing information for expert learners becomes difficult because of the extra
content required for new learners. Principally, instruction needs to be adjusted as
the learners knowledge increases, thus accounting for individual differences in
existing schema. Otherwise, the split-attention and redundancy effect will occur
adversely impacting learners with domain expertise.
“Individual differences in spatial abilities are attributed to differences in spatial
working memory” (as cited in...Plass, et al., 2010). This section highlighted a few
key findings from research about Spatial Ability and Extraneous Cognitive Load.
Mainly, how the presentation of material increases the load for low vs. high
spatial ability learners. “Learners spatial abilities, and the resulting different
hypothesized levels of extraneous cognitive load, may have influenced learning
strategies in processing…” (Plass, et al., 2010). High spatial ability learners
benefited from instruction with concurrent presentation of animation and spoken
text. Low spatial ability learners benefited from successive presentation of the
same content.
Self-regulation is another individual difference with a relationship to Cognitive
Load. “...Results suggest that higher intrinsic load may lead to lower selfregulation activity compared with lower intrinsic load conditions” (Plass, et al.,
2010). Intrinsic load affects self-regulations, which impacts performance.
Matching learner to prior knowledge is a possible instructional technique to help
in this scenario. Experts are better at regulating themselves to manage increased
loads for optimal performance. On the other hand, self-regulation can be
considered extraneous cognitive load and decrease performance. To aid selfregulations Plass, et al., (2010) discuss research that suggested implementing
study goals (p. 77) and scaffolding instruction (i.e. human tutors).
Lastly, the chapter addresses the potential of an adaptive learning environment
to tailor instruction to learners individual differences. “...Instructional designs
should be tailored to learners’ levels of knowledge, skills, and abilities” (as cited
in Plass, et al., 2010). A virtual learning environment might present information in
multiple ways, at different levels of learning, and based on performance
measures. In other words, it would account for differences and adapt to them, so
one environment can be used to teach many.
Prior Knowledge Principle in Multimedia Learning
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York:
Cambridge. [Chapter 21]
“The main theoretical issue associated with the prior knowledge principle
concerns the integration in working memory of instructional information with
information held in long-term memory. The major implication for instructional
design is the need to tailor instructional formats and procedures to changing
levels of expertise”. This principle is mainly concerned with designing instruction
for expert learners, and takes into consideration the expertise reversal effect.
Research on prior knowledge demonstrates a few key points. First, “highknowledge learners’ use their knowledge to compensate for a lack of instructional
guidance” (Mayer, 2005). Second, visualizing (i.e. imaging principle) instruction is
a powerful tool for high-level learners. Third, narrated explanations with images
are unnecessary. Fourth, worked out examples were beneficial for both low and
high-knowledge students.
The research makes clear that high-knowledge learners require less instructional
support and guidance, especially for domain related learning. “To be effective,
instructional design should be tailored to changing levels of learner expertise.
The general strategy for tailoring instruction to levels of learner expertise is to
gradually replace high-structured instructional procedures and formats with lowstructured instructions and knowledge levels increased” (Mayer, 2005). Mayer
(2005) ultimately suggests that as levels of learners’ expertise in a domain
increases, the instruction needs to consider prior knowledge.. To be more
specific, “more low-structured instructional environments...with minimal or no
textual explanations, exploratory (discovery) learning environments, or problemsolving exercises could be used to assist high-knowledge individuals in learning
advanced complex materials in their area of expertise” (as cited in Mayer, 2005).
Again, students who already know a lot about a specific subject matter, require
less instructional material and guidance then a learner who is approaching the
information for the first time.
Learning Styles: Concepts and Evidence
Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning Styles: Concepts
and Evidence. Psychological Science in the Public Interest, 9(3), 10–119.
“The term learning styles refers to the view that different people learn information
in different ways” (Pashler, McDaniel, Rohrer, & Bjork, 2008). Learning styles is a
popular term in education and there are many companies selling products to
educational organizations that offer learning assessments based on varying sets
of learning styles. This article investigated “scientific evidence underlying
practical application of learning-style assessment in school contexts” (Pashler et
al., 2008). The authors were measuring the validity of the learning-style
hypothesis, specifically the meshing-hypothesis “the claim that presentation
should mesh with the learner’s own proclivities” (Pashler, et al., 2008). In other
words, the learners inclination for a particular learning style is best matched with
instruction for that type of presentation.
Based on the literature and empirical evidence review of the authors study, it was
found there is little support for using learning style assessments in school
settings. “The contrast between enormous popularity of the learning-style
approaches within education and the lack of credible evidence for its utility is
striking and disturbing” (Pashler, et al. 2008). Most of the research was
contradictory to the meshing-hypothesis and, if anything, validated the
importance of differentiated instruction, but did not crystallize the necessity of
learning-style intervention compared to its cost.
Analyzing the Learning Task (Optional)
Smith, P.L. & Ragan, T.J. (2005) “Chapter 5: Analyzing the Learning Task”. Instructional
Design (3rd Ed.). Boston, MA: Allyn and Bacon. pp. 75-124.
The chapter talks about the process of task analysis, which integrate used in the
design process. It provides an overview of the learning task analysis process that
includes goal writing, determining learning goal types, analyzing information
processing, reviewing prerequisites, constructing learning outcomes, and writing
test specifications.
“The final product of the learning task analysis is a list of goals, amplified with
test specifications, that describe what the learners should know or be able to do
at the completion of instruction and the prerequisite skills and knowledge that
learners will need in order to achieve those goals” (Smith & Ragan, 2005). The
learning task analysis helps to correct two problems. First, eliminating information
that is non-essential or non-supportive in reaching a learner goal (Deadwood).
Second, failing to make clear the prerequisite information and skills to achieve a
learner goal.
Ultimately, the task analysis process helps designers select the best approach
for the targeted learning environment they are creating.
March 24, 2014 (#8)
Managing essential processing in Multimedia Learning
Principles for Managing Essential Processing in Multimedia Learning: Segmenting,
Pretraining, and Modality Principles (Chapter 11)
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York:
Cambridge. [Chapter 11]
This chapter talks about three strategies for reducing essential overload in
Multimedia Learning. These principles are segmenting, pertaining, and modality.
“Essential overload occurs when the amount of essential cognitive processing
required to understand the multimedia instructional message exceeds the
learner’s capacity” (Mayer, 2005). In other words. the working memory is over
taxed because intrinsic load is too high. “Essential material refers to the total
amount of processing that can be supported by both the auditory and visual
channels of the learner’s working memory at any one time” (Mayer, 2005). In this
chapter, Mayer (2005) uses the example of a concise narrated animation (p.
169). Therefore, if a multimedia presentation is too fast paced, and the
information is presented beyond the capacity of the learners working memory to
process the information, reduced learning will occur.
Segmenting slows down the presentation so the learner can engage more
deeply. Pretraining gives the learner prior knowledge (schemas) with which he
can better process the information. Modalities transfers some of the processing
between the auditory and visual systems.
“The theoretical rationale for segmenting is that is slows the pace of
presentation, thereby enabling the learner to to carry out essential
processing….for pretraining is that is equips the learner with prior
knowledge that the learner can use to process the subsequent narrated
animation with less cognitive effort” (Mayer, 2005).
Each of these principles was demonstrated by Mayer’s research, and supports
the CLTM pertaining to the limited capacity of working memory. In terms of
application to instruction, based on the aforementioned principles, some tactics
include breaking animations into segments, sequencing instruction, and narrating
animation instead of using text on the screen.
Research can be improved by evaluating how long to segment content, the
impact of learner control on pacing content, and addressing individual differences
in learners to name a few.
Techniques in Generative Processing in Multimedia Learning: Open Questions for
Cognitive-Load Research (Chapter 8)
Moreno, R., & Mayer, R.E. (2010). Techniques that Increase Generative Processing in
Multimedia Learning: Open Questions for Cognitive-Load Research. In J. L.
Plass, R. Moreno, & R. Brünken (Eds.), Cognitive Load Theory. New York:
Cambridge. [ chapter 8 ]
This chapter examines how to increase generative processing (relating new
information to prior knowledge to build new knowledge structures) in multimedia
learning. It uses the active processing principle of CTML as a basis, because
“Students may fail to learning unless instruction includes methods aimed at
engaging the learner…” (Moreno & Mayer, 2010). This is important because
research demonstrates that deep learning requires active cognitive engagement.
Therefore, how do you promote generative processing without creating
extraneous cognitive load? Moreno & Mayer (2010) present some principles
toward this end. They are the Multimedia Principle, Personalization Principle,
Guided Activity Principle, Feedback Principle, and Reflection Principle.
The multimedia principle states “instruction that includes verbal and pictorial
representations of knowledge are more likely to lead to meaningful learning than
those that present verbal information (as cited in...Moreno & Mayer, 2010). It is
based on dual-coding theory and supported by the dual channel assumption of
CTML. “When learners are presented with verbal and pictorial representations of
the system to-be-learned, they become more cognitively active…” (Moreno &
Mayer, 2010).
The personalization principle states “Instruction that includes personalized
messages is more likely to lead to more meaningful learning than those that use
non-personalized messages (Moreno & Mayer, 2010). Through personalized
messaging, students must develop a relationship between the instruction and
themselves. Thus, making it a form of active learning.
The guided activity principle states “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 allow for dialoguing or for
manipulating the learning materials” (Moreno & Mayer, 2010). Moreno & Mayer
(2010), believe this leads to meaningful learning because of learners must select,
organize, and integrate new information actively (p. 162).
The feedback principle refers to corrective feedback and corrective feedback plus
explanation. The principle states “novice students learn better when presented
with explanatory feedback during learning” (Moreno & Mayer, 2010). Giving
feedback helps to create a mental model when none exists.
“Encouraging students to provide principle-based explanations for their thinking
promotes the organization and integration of new information with students’ priori
knowledge” (Moreno & Mayer, 2010). This promotes deep processing of
information, especially in non-interactive learning environments. In effect, this is
the reflection principle.
Again, these principles are designed to promote generative learning and are
supported by CTML - when learning from Multimedia Instruction. While research
demonstrates evidence of these principles effectiveness, additional research is
needed to measure Cognitive Load in all three types of load situations, and to
account for varied learners.
Ausubel's Meaningful Reception Learning Theory
Driscoll, M. (2005). Psychology of Learning for Instruction (3rd ed.). Boston, MA: Allyn and
Bacon. [chapters 4]
Aubel’s meaningful, reception learning theory addresses how “learners actively
interpret their experiences using certain internal, cognitive operations” (as cited in
Driscoll, 2005). Ausubel differentiates learning as either reception, or discovery learning,
or rote or meaningful learning. According Ausubel, rote or meaningful learning can
occur in reception or discovery learning. Meaningful learning is what was important to
Ausubel and “refers to the process of relating potentially meaningful information to
what the learner already knows in a nonarbitrary and substantive way” (Driscoll, 2005).
The emphasis being prior knowledge is the critical component of this definition.
Therefore, Ausubel conceptualized hypothetical constructs of memory structure and
learning processes to support meaningful learning. These include the learner's cognitive
structure, or integrated body of knowledge, and provide a framework for relating
potentially meaningful information. A key characteristic of this framework includes
anchoring ideas, or a hook to which new information is assimilated through the process
of meaningful learning.
The process of meaningful learning describes how new information is likely to be added
to the existing cognitive structure. This process occurs through derivative and
correlative subsumption or superordinate and combinatorial learning. Ausubel coined
the process of retention the assimilation theory.
Readiness for learning is another component of the meaningful learning process. It
states that a learner must possess a relevant, stable, and organized cognitive structure
to be ready to learn new material. Two influences on a person's readiness to learn,
however, include age and diversity. In terms of instruction, younger learners require
more concrete instruction. Of course, learning also depends on our mental schema.
According to Driscoll (2005), a schema is a data structure for representing the generic
concepts stored in memory. Schemas are packets of knowledge, and schema theory is a
theory of how these packets are represented and how that representation facilitates the
use of the knowledge in particular ways. In other words, schema help us process
information to guide our actions.
Related to meaningful learning, schema are important for processing new information
through accretion, tuning, and restructuring. Another aspect of schema is cognitive load,
in which the goal is to reduce the strain on working memory from extraneous cognitive
loads, to engage the appropriate schema for learning.
In terms of implication for instruction, meaningful learning and schema theory both
emphasize prior knowledge. The question then becomes, how do you develop
instruction that taps into what learners might already know about a domain. Driscoll
calls this activating prior knowledge, and discusses the concepts of advanced organizers
and schema signals. Advanced organizers help to bridge a gap between what the
learners already know and need to know for meaningful learning. Schema signals are
cues to enact appropriate or inappropriate behavior in instructional situations.
Lastly, Driscoll reviews how to make instructional materials meaningful by discussing
concepts such as comparative organizers, elaboration, and conceptual and pedagogical
models: All with the goal of aiding in transfer of knowledge.
In Defense of Advance Organizers: A Reply to Critics (Optional)
Ausubel, D.P. (1978). In Defense of Advance Organizers: A Reply to the Critics. Review
of Educational Research, 48, 251-257.
As the title implies, this publication is a direct response to Ausubel's critics about
his concept of advanced organizers. It is interesting to read how other perceived
the advanced organizer concept, but also helped me to understand more about
advanced organizers. Also, I learned how researchers might respond to criticism
about their work through this type of academic discourse.
The strongest criticism of the advanced organizer is they are not clearly defined
or structured. Thus, leaving room for interpretation for what the criteria for an
advance organizer is. According to Ausubel (1978) the construction of the
organizer is dependent on the type of material, the age of the student, and prior
knowledge (p. 251). To the criticism about not defining advanced organizers,
Ausubel (1978) responds:
“I define advance organizers as introductory material at the higher level of
abstraction, generality, and inclusiveness than the learning passage itself,
and an overview as a summary presentation of the principal ideas in a
passage that is not necessarily written at a higher level of abstraction,
generality, and inclusiveness, but achieves its effect largely by the simple
omission of specific detail” (p. 252)
In other words, advanced organizers provide an overview of instructional material
without the nuanced detail of the instruction.
Other criticism included the limited capacity to identify an advanced organizer.
“One can identify an advance organizer by the simple comparison with is
accompanying learning passage and from knowledge of the pupils” (Ausubel,
1978). I’m not sure why this matters, but it was helpful to learn how to know
when one might be used.
There are additional critiques but the key takeaway for me is the proven
effectiveness of advanced organizers in school-based learning and how they can
be operationalized.
March 31, 2014 (#9)
Reducing Extraneous Processing in Multimedia Learning
Techniques that Reduce Extraneous Cognitive Load and Manager Intrinsic
Cognitive Load
Mayer, R.E., & Moreno, R. (2010). Techniques that Reduce Extraneous Cognitive Load
and Manage Intrinsic Cognitive Load during Multimedia Learning. In J. L. Plass, R.
Moreno, & R. Brünken (Eds.), Cognitive Load Theory. New York: Cambridge. [ chapter
7]
How do you improve instructional design for computer-based multimedia
learning? That is the what this chapter attempts to answer by providing eight
principles to improve the design of short narrated animations. The principles are
based on CTML, which is based on CTL, and are geared to reduce extraneous
cognitive load, manage essential load, and create germane load.
“Specifically, we draw on a central tenet common to CLT and the cognitive
theory of multimedia learning, which can be called the triarchic theory of
cognitive load. The triarchic theory of cognitive load specifies three kinds
of cognitive processing demands during learning...Extraneous cognitive
load, intrinsic cognitive load, and germane cognitive load” (Mayer &
Moreno, 2010).
If a multimedia activity is designed poorly the learner will be over taxed, which
will degrade the learning experience. “...Problems occur when the total amount of
extraneous, essential, and generative processing exceeds the learner’s capacity”
(Mayer & Moreno, 2010). Therefore, According to Mayer & Moreno (2010), the
goals for improving multimedia design are to reduce cognitive processing,
manage essential cognitive processing, and foster generative processing (p.
135).
The research-based principles to reduce cognitive load are the redundancy
principle, the signaling principle, the temporal contiguity principle, and spatial
contiguity principle. The redundancy principle suggest excluding redundant onscreen text. The signaling principle suggests providing cues to signal what
information is important to pay attention to. The temporal contiguity principle
suggest to present animation and narration at the same time. The spatial
contiguity principal suggest placing on screen text near the relevant item where
the learner is being directed to look.
The research-based principles to manage intrinsic cognitive load are the
segmenting principle, and the modality principle. The segmenting principle
suggest giving the learner control over chunks of information to set the pace for
how its digested. The modality principle, which is very popular, suggests
presenting narration and animation rather than on-screen text and animation.
Overall, these principles are designed to reduce extraneous cognitive load,
manage essential load, and foster germane load in order to prevent the learner’s
mental capacity from being overloaded to the point that learning degraded.
Modality Principle
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York:
Cambridge. [Chapters 9]
Chapter 9 brings to bear how the modality principle in multimedia learning can increase
working memory. “...Information in the auditory mode can expand effective working
memory capacity and so reduce the effects of an excessive cognitive load” (Mayer,
2005). This is called the modality effect, which is derived from the split-attention
principle in CLT. It works by presenting information in mix modes (i.e. visual and
auditory).
In the split-attention principle, information is presented in different modes but requires
integration in memory. In the modality principle, the integration occurs in the
presentation of information instead. In this case, text and pictures are combined, or
spoken word is combined with animations. “Under split-attention conditions, rather than
physically integrating disparate sources of information, learning may be facilitated by
presenting a written source of information in auditory mode. While care must be taken to
ensure the auditory material is essential and not redundant and that the instructional
material is sufficiently complex to warrant the use of a cognitive load reduction
technique, under appropriate circumstances the instructional gains can be large” (Mayer,
2005). In other words, the modality effect is a cognitive load reduction technique. It
improves working memory because the integration occurred in the delivery of content.
Therefore, it can be processed by the various parts of the working memory model Baddeley’s to be specific. It is also important to note that the information must be
essential to learning and not redundant. Otherwise, the modality effect is non-existent.
Worked-Out Examples Principle
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York:
Cambridge. [Chapter 15]
“The worked-out examples principle states that people gain a deep understanding of a
skill domain when they recieve worked-out examples in the beginning of a cognitive skill
acquisition” (Mayer, 2005). Worked out examples that are typical and do not follow the
worked-out examples principle increase cognitive load because the learner must integrate
so much information presented in the worked-out example. Also, as expertise increases,
the effectiveness of worked-out examples declines.
To produce effective worked-out examples, the following guidelines must be met. The
guideline of self-explanation shows that self-explanation activity is related to learning
outcomes. Self-explanation activity includes principle based explanations, explication of
goal-operator combinations, and example comparisons. In brief, self-explanation that is
active promotes knowledge transfer.
The help guideline, is a set of instructional principles to support self-explanation of
worked-out examples. It includes provision on learner demand, minimalism, and focus on
principles.
The above are guidelines for aiding in processing examples. The following are guidelines
for example design. First, the easy mapping guideline, which suggests “Integrating, that
is making the mapping between representations easier, makes cognitive resources
available for productive learning process…” (Mayer, 2005). In other words,
incorporating different modalities and parts of worked-out examples to reduce processing
supports self-exploration.
The structure emphasizing guideline, which says that “learners can be guided to perceive
examples and problems in terms of their solution -relevant structure when multiple
examples are employed” (Mayer, 2005). In other words, when there are multiple
examples, learners can evaluate them based on structural features related to the relevant
problem solving method.
The meaningful, building blocks guideline that suggests “worked-out examples should
include conceptually oriented (modular) solution procedures and the single (sub-) goals
should be made salient by visually isolating them, by assigning a label or by a subgoaloriented step-by-step presentation. This helps learners see the building blocks of a
worked-out example when the problem is new to them.
These above guidelines must be employed together to increase the effectiveness of
worked-out examples. They also demonstrate that multiple worked-out examples are
better for knowledge transfer then a singular example.
Structuring the Transition From Example Study to Problem Solving in Cognitive
Skill Acquisition: A Cognitive Load Perspective (Optional)
Renkl, A., & Atkinson, R. K. (2003). Structuring the Transition From Example Study to
Problem
Solving in Cognitive Skill Acquisition: A Cognitive Load Perspective. Educational
Psychologist, 38(1), 15–22.
I selected this article because is I wanted to read about a even more practical
way to implement worked-out examples. What I discovered in the excerpt was an
approach to avoiding the expertise reversal effect using worked-out examples.
The article aims to explain how to reduce cognitive load at different levels of
cognitive skill acquisition. It is based on the idea that as learners become more
expert in a domain, worked-out examples either produce extraneous cognitive
load, or promote germaine load, depending on the goals of the instruction. Renkl
& Atkinson (2003) “distinguish among early, intermediate, and late phases of skill
acquisition” (p. 16).
“Two important propositions can be derived from our CLT assumptions: (a)
Intrinsic load gradually decreases over the course of cognitive skill acquisition so
that a gradual increase of problem-solving demands is possible without imposing
an excessive load. (b) When understanding is acquired, self-explanation
activities become extraneous and problem solving is germane, because speed
and accuracy should be heightened and automation should be achieved. Hence,
problem-solving elements should not be introduced too late because example
study and self-explanations are transformed from germane to extraneous load”
(Renkl & Atkinson, 2003)”. This represents the idea of fading out worked-out
solution steps in favor or problem solving at the intermediate step of cognitive
skill acquisition, which is the most important point for transitioning from germaine
to extraneous load.
I think this is a useful article, but their argument is limited by the instructional
goals. I would think as an instructor, it is important to know when most learners
reach an intermediate level of skill acquisition, so you can introduce problem
solving according to the authors suggestion. Considering a diverse set of
learners with many individual differences, the article isn’t specific about
assessing for the timing of this transition.
April 7, 2013 (#10)
Control, Interactivity, and Feedback
Multimedia Learning in Games, Simulations, and Microworlds
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York: Cambridge. [Chapter
33]
This chapter reviews research on multimedia learning in games, simulations, and
microworlds. To start, Mayer (2005) distinguishes between interactive or
experiential educational multimedia. The important difference is that experiential
multimedia is interactive, and is the type of multimedia under focus in the
chapter. Regardless, the experience of the learner in any environment is first and
foremost.
For the purposes of this annotation, I think the examples aren’t as relevant as
what might be learned from them. “Much of my own research has explored some
of the many decisions about how to design the simulation’s interface to help
convey the underlying model of the user” (Mayer, 2005). Another way of framing
this is to say that the design must elicit a clear goal, and the user will benefit from
instruction. Also, “The challenge of the game should be proportional to the skill
level of the users” (Mayer, 2005). In fact, a challenging, or well designed game
can also help the learner focus their attention and organize information. Lastly,
simulation, microworlds and games can be used as inquiry building tools. “The
use and role of simulations and microworlds in education can also be
distinguished, respectively, in terms of model using vs. model building (As cited
in...Mayer 2005). In other words, the student is either learning with or creating the
learning environment.
Similar to the above, a great deal was learned from research on multimedia
learning in games, simulations, and Microworlds. One of the most salient lessons
from simulations is that “graphical feedback was more beneficial than textual
feedback for implicit, or near-transfer tasks. Yet, on explicit, or far-transfer, tasks
requiring students to translate graphical symbols intro verbal symbols, the
difference was not as compelling” (Mayer, 2005). In other graphical feedback is
more beneficial for learning. Another insight that providing supported instruction
(i.e. explanations) helped students organize information. Finally, as simulations
become more complex students benefit.
In terms of games, the research focused on students learning from designing
their own games because there current research on using games in the
classroom doesn’t demonstrate a clear benefit. The outcome of the research
appears to be the characteristics children like in a game including the quality of
the storyline, competition, and appropriate challenge.
Two key points from the discussion of theory as it applies to simulations, games,
and microworlds are: (a) verbal systems is likely to store explanatory accounts of
conceptual relationships, whereas the visual system should be more suited to
handle experiential. This is based off the dual-coding theory. Also, Microworlds
allow users to build their own mental models.
This research is important because it tells the story of what is being done to
evaluate experiential, interactive multimedia learning environments. As these
environments develop with improved technology, the foundational investigation
will be informative to future researchers (possibly myself or others in DMDL).
Interactivity in Multimedia Learning: An integrated Model
Domagk, S., Schwartz, R., & Plass, J.L. (2010). Interactivity in Multimedia Learning: An
integrated Model.
Computers in Human Behavior.
Interactivity is a term that is used often in many discipline, and is defined
differently depending on the context in which it applies. However, the lack of a
unitary definition leaves something to be desired for multimedia learning.
Therefore, the Domagk, Schwartz, & Plass (2010) developed a new, integrated
model of Multimedia Interactivity that factors commonalities of the various
definitions such as learner characteristics, learners emotions, motivation, and
learning outcomes.
Domagk, Schwartz, & Plass define Interactivity as a reciprocal activity between
the learner and a multimedia system, in which the [re]action of the learner is
dependent upon the [re]action of the system. However, the process is initiated by
the learner (motivation)
Interact has six components that combined comprise the Interact model. They
are: the learning environment, behavioral activities, cognitive and metacognitive
activities, motivation and emotion, learner variables, and the learner’s mental
model. What makes this a dynamic process is that feedback is represented
between the different elements. It is also unique because the model can be
decomposed into individual elements. Practically, this model provides designers
a framework to develop interactive multimedia components for learning.
Role of Guidance, Reflection, and Interactivity in an Agent-Based Multimedia Game
Moreno, R. (2005). Role of Guidance, Reflection, and Interactivity in an Agent-Based
Multimedia Game.
Journal of Educational Psychology, 97, 117 -128.
This article discusses three research experiments that address the role of
guidance, reflection, and interactivity in an agent-based multimedia game. The
research is based on the cognitive model of multimedia learning in which,
according to Moreno (2005), meaningful learning occurs when the learner can
build representations in their working memory (p. 118). There are two
components to this process: selecting relevant images and words, and linking
those to prior knowledge and organizing the information. There are three
instructional treatments embedded in the multimedia game used in this research
to promote meaningful learning. They are guidance (i.e. guided explanation
about choices), reflection (i.e. asking learners to justify their answers - correct or
incorrect), and interactivity (i.e. construct answers to problems proposed).
Overall, the results of the research showed that students learn more deeply from
guided discovery. Related to reflection, it did not improve learning in an
interactive environment. On the other hand, reflection techniques benefit
students in non interactive environments, especially in far-transfer tests.
Reflection is only fosters meaningful learning when responding to correct
answers. This is summarized by Moreno & Mayer as seen below:
“Guidance in the form of explanatory feedback produced higher transfer
scores, fewer incorrect answers, and greater reduction of misconceptions
during problem solving. Reflection in the form of having students give
explanations for their answers did not affect learning. Experiments 2 and 3
showed that reflection promotes retention and far transfer in non
interactive environments but not in interactive ones unless students are
asked to reflect on correct program solutions rather than on their own
solutions” (Moreno & Mayer, 2005)
Enhancing Instructional Efficiency of Interactive E-learning Environments: A
Cognitive Load Perspective (Optional)
Kalyuga, S. (2007). Enhancing Instructional Efficiency of Interactive E-learning
Environments: A Cognitive
Load Perspective. Educational Psychology Review, 19, 387-399.
Interactive E-Learning environments are pervasive in education, and designing
them them for efficiency is important. Yet, a major limitation of efficiency in
interactive E-Learning environments is our cognitive load. This paper talks about
how these environments create cognitive load, and how to design them to reduce
it.
The design discussion adheres to the cognitive load theory in which the goal is to
overcome the limitations of the working memory, by promoting germane load. In
this context, the goal of efficiency is to help learners organize information into
their existing knowledge structures, while reducing activity that interferences with
the learning process. In other words, reduce extraneous cognitive load.
Kalyuga (2007) presents several types of interactive E-Learning environments
and defines the primary feature as the responsiveness to learners’ actions.
Through his introduction to these environments, he classifies them into levels of
interactivity - feedback level, manipulation level, adaptation level, and
communication level. He states that complex interactive E-Learning
environments typically have more than one, if not all levels. These levels are
distinguished by their controlling characteristics - information delivery,
representational forms, and content.
Kalyuga (2007) also mentions the different types of interactivity provide different
means to manage cognitive load. Feedback interactivity elicits the feedback
principle related to instruction-provided by guidance. Adaptation interactivity
helps balance executive guidance and the rate/amount of information provided.
Communication interactivity may increase essential load by asking learners to
explain or predict. “What is important though is that the guidance should be
provided when learners lack sufficient task-specific knowledge base to serve in
the executive role to prevent reverting to unproductive search activities”
(Kalyuga, 2007). In other words, in most cases, guidance balances the central
executive with LTM stores to help structure new information with prior
knowledge.
To reduce extraneous load created by Interactivity, which Kalyuga (2007)
suggest is possible, appropriate learner control can be employed. For example,
means of pacing or segmenting can be used to allow the learner reduce things
like the split-attention effect that may occur from a highly interactive environment
with many elements.
April 14, 2014 (#11)
Affect–Motivation, Self-Regulation, and Emotion
Motivation and Self-Regulation in Learning
Driscoll, M. (2005). Psychology of Learning for Instruction (3rd ed.). Boston, MA: Allyn
and
Bacon. [ Chapter 9 ]
I particularly enjoyed this chapter about motivation and self-regulation in learning
because I’m interested in what moves people to act. In this case, as I read the
excerpt, I was thinking about a presentation I’m giving for work, and how to apply
the model of motivational design to my audience.
I can see from the chapter that Driscoll (2005) defines motivation a goal directed
behavior that is instigated and sustained throughout the learning case. Motivation
can be determined by several factors, such as curiosity and interest, goals and
goal orientation, and self-efficacy beliefs. There are two elements of curiosity of
relevance that are perceptual arousal and fantasy. These help peaks learners
interest, especially when instruction is beginning. This is surface level curiosity,
and deeper levels may be reached through problem solving scenarios. Goals
prompt persistence in learning, according to Driscoll (2005), but they need to be
specific, time bound, and oriented to the learning task. Short-term goals are
typically more effective at improving self-motivation and performance than longterm goals. Also, performance goals aren’t desirable compared to learning goals
because learning goals instill learner confidence. They empower the learner to
use a skill set to accomplish a task instead of performance goals that are
perceived to require fixed attributes like intelligence. Self-efficacy beliefs are how
the learner rates their own confidence to complete a task, and is related to
behavior and outcomes. Self-efficacy can be influenced through four strategies:
enactive mastery experiences, vicarious experiences, verbal persuasion, and
physiological states. The above reflects instigated motivation.
However, to continue motivation, different considerations are required: satisfying
expectations, attributions (i.e. internal/external, stable/unstable,
controllable/uncontrollable), and self-regulation. Learners are likely to stay
motivated during a learning scenario when they have success with the learning
experience. According to Driscoll (2005) this results in a natural occurrence of
learning and success beget success. Likewise, a learners self-perception affects
their motivation. Lastly, self-regulation helps the learner manage all the aspects
of motivation in the learning situation.
Driscoll (2005) also discusses a model of motivational design that is based on
four conditions of motivation: attention, relevance, confidence, and satisfaction.
All conditions are required for a motivated learner, and in total affect the amount
of attention and energy supplied to a learning task. The process of motivational
design, on the other hand, are instructional strategies for simulating motivation. It
is a four stage process consisting of the following steps: gaining and sustaining
attention, enhancing relevance, building confidence, and generating satisfaction.
The Broaden-and-Build-Theory of Positive Emotions
Fredrickson, B.L. (2001). The Role of Emotion in Positive Psychology: The broadenand-build theory of
positive emotions. American Psychologist, 56, 218-226.
It is known that positive emotions affect well-being, but Fredrickson (2001) in this
article also argues it produces optimal function. He does so through is broadenand-build theory of positive emotion, that states positive emotions broaden
people’s momentary thought-action (outcome of a psychological process)
repertoires and build their enduring personal resources. Another way of saying it
is people use their positive emotions to broaden their perspective and usual ways
of thinking or acting, which improves well-being.
Fredrickson (2001) applies this to current research on positive emotions and links
it as follows. First, distinct positive emotions widen the array of thoughts and
actions that come to mind. Second, broadening positive emotions at the cognitive
level reverses the impact of negative emotion at the cardiovascular level. Third,
positive emotions fuel psychological resilience. Fourth, positive emotions help
build our capacity to seek positive outcomes in our daily lives. Fifth, positive
emotions are linked to living longer and feeling better.
In brief, this research suggest that positive emotions - according to Fredrickson’s
theory - produce health and well-being, and therefore, being positive is
transformative for individuals. They fuel human flourishing, psychologically
speaking.
Although this article isn’t directly related to learning, I can’t help but to think that
one fraught with positive emotions might also learn more!
Emotional Design in Multimedia Learning
Um, E. & Plass, J.L. (2012). Emotional Design in Multimedia Learning. Journal of
Educational Psychology.
The research discussed in this article evaluates positive emotions in multimedia
design. The research investigated external mood induction and emotional design
induction. The study is based on the idea that positive emotions improve, rather
than impede learning, and it challenges the traditional perspective that emotions
create extraneous load. It also considers how emotions enhance learning
outcomes. The methods used in the study focus on inducing emotions through
the design of the environment (i.e. colors, visual shapes) rather than procedures,
which is more typical.
Generally, the results indicate “applying emotional design principles to learning
materials can induce positive emotions and that positive emotions in in
multimedia-based learning facilitate cognitive processes and learning” (Um &
Plass, 2012). In other words positive emotions are influenced by the design of an
environment. To be more detailed, the study results demonstrated externally
induced positive emotions decreased over the duration of learning, while positive
emotions induced by design remained until the end of learning. Secondly,
students using materials designed to induce positive emotions performed better
on comprehension tests and transfer tests. That is positive emotional states
enhance learning outcomes. Third, design that induced positive emotions did not
create extraneous load. Lastly, intrinsic motivation increased related to internally
or externally induced emotions.
Social Cues in Multimedia Learning, Role of Speaker’s Voice (Optional)
Mayer, R. E. (2003). Social cues in multimedia learning: Role of speaker’s voice.
Journal of Educational
Psychology, 95(2), 419–425.
I selected this article after a search for affect and multimedia learning because it
relates to how learners perceive their interaction with computers, and multimedia.
In this scenario, the study by Mayer (2003) evaluates the role of voice in
promoting deep learning from a multimedia lesson. He is looking at the impact of
the speakers voice in narration.
The study is based on the notion of social conversation where the learner and
the computer are in a discussion. It is also based on the social agency theory,
which states 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. Therefore, deep cognitive
processing is possible. Another way of viewing this is that by being in a social
conversation, learners can select relevant information, organize into coherent
representation and link it to prior knowledge, and encode it into memory.
Effectively, promoting knowledge transfer and learning.
The results of the study supported the social agency theory and the cognitive
load theory. In other words, students who experienced human-like narration
learned more deeply. The human voice (non-foriegn) also helped reduce
cognitive load. This was coined as the voice principal:
“Students learn more deeply when the narration in a multimedia lesson is spoken
by a standard-accented human voice rather than a foreign-accented human
voice or machine voice” (Mayer, 2003). The implication for design is clear consider the social cues of narration and use a human-narrated voice.
April 21, 2014 (#12)
Learning from Animations, Simulations, and Games
Multimedia Learning in Games, Simulations, and Microworlds
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York:
Cambridge. [Chapter 33]
This chapter reviews research on multimedia learning in games, simulations, and
microworlds. To start, Mayer (2005) distinguishes between interactive or
experiential educational multimedia. The important difference is that experiential
multimedia is interactive, and is the type of multimedia under focus in the
chapter. Regardless, the experience of the learner in any environment is first and
foremost.
Mayer (2005) provides an examples. For the purposes of this annotation, I think
the examples aren’t as relevant as what might be learned from them. “Much of
my own research has explored some of the many decisions about how to design
the simulation’s interface to help convey the underlying model of the user”
(Mayer, 2005). Another way of framing this is to say that the design must elicit a
clear goal, and the user will benefit from instruction. Also, “The challenge of the
game should be proportional to the skill level of the users” (Mayer, 2005). In fact,
a challenging, or well designed game can also help the learner focus their
attention and organize information. Lastly, simulation, microworlds and games
can be used as inquiry building tools. “The use and role of simulations and
microworlds in education can also be distinguished, respectively, in terms of
model using vs. model building (As cited in...Mayer 2005). In other words, the
student is either learning with or creating the learning environment.
Similar to the above, a great deal was learned from research on multimedia
learning in games, simulations, and Microworlds. One of the most salient lessons
from simulations is that “graphical feedback was more beneficial than textual
feedback for implicit, or near-transfer tasks. Yet, on explicit, or far-transfer, tasks
requiring students to translate graphical symbols intro verbal symbols, the
difference was not as compelling” (Mayer, 2005). In other graphical feedback is
more beneficial for learning. Another insight that providing supported instruction
(i.e. explanations) helped students organize information. Finally, as simulations
become more complex students benefit.
In terms of games, the research focused on students learning from designing
their own games because there current research on using games in the
classroom doesn’t demonstrate a clear benefit. The outcome of the research
appears to be the characteristics children like in a game including the quality of
the storyline, competition, and appropriate challenge.
Two key points from the discussion of theory as it applies to simulations, games,
and microworlds are: (a) verbal systems is likely to store explanatory accounts of
conceptual relationships, whereas the visual system should be more suited to
handle experiential. This is based off the dual-coding theory. Also, Microworlds
allow users to build their own mental models.
This research is important because it tells the story of what is being done to
evaluate experiential, interactive multimedia learning environments. As these
environments develop with improved technology, the foundational investigation
will be informative to future researchers (possibly myself or others in DMDL).
Multimedia Learning in Virtual Reality
Mayer, R.E. (Ed.) (2005). Cambridge Handbook of Multimedia Learning. New York:
Cambridge. [Chapter 32]
One theme is clear from this chapter, which is virtual reality - at least when this
book was written, is an emerging technology. Until recently VR environments
were created without an emphasis on instructional design, and there isn’t enough
evidence to support significant learning gains using VR.
VR defined by Cobb & Fraser (2005) is a combination of computer systems used
to build and support a VR space, and peripherals or devices used to interact
within the VR space. VR is another way of coining an immersive simulation. In
most cases the user is either totally or partially immersed in the environment. As
noted previously, there isn’t agreement on what learning theories are applicable.
However, constructivism and experiential learning are widely reported because
VR allows learners explore the environment at their own pace. VR is also used to
support learning environments that are constructivist, constructionist, and
situated because of its attributes (i.e. flexible, real-time interaction, etc.). VR is
beneficial to many types of learners too because it provides a safe way to
explore a multitude of topics. For example, users with physical disabilities can
learn life skills. Since the application of VR is broad, this chapter focuses on
three areas: spatial cognition, life skills rehearsal, and social skills training. There
is accumulating evidence that VR is beneficial in the application of these areas,
however it’s not clear if transferring knowledge from VR to the real-world is
equivalent.
“It was concluded that allowing students freedom to explore a VR is no sufficient
learning...Just because virtual environments offer a motivating learning space,
this is on it’s own does not ensure that they will learn. Students also need to
understand how the virtual environment relates to the real world and apply
learning” (Cobb & Fraser, 2005). This is why it’s important to continue
researching learning and VR environments because there isn’t enough
experimental evidence to demonstrate learning gains.
Design Factors for Educationally Effective Animations and Simulations
Plass, J.L., Homer, B., & Hayward, E. (2009). Design Factors for Educationally Effective
Animations and
Simulations. Journal of Computing in Higher Education, 21(1), 31–61.
This article is an excellent overview of the many theories and principles we
learned this semester. It also introduces emerging principles of design, and
discusses future directions of research. The article, in brief, is a discussion of
“empirically validated design principles that assure educational effectiveness”
(Plass, Homer, Hayward, 2009).
According to Plass, et al. (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 related theories include dual coding, cognitive load, the cognitive theory of
multimedia learning, and the integrated model of text and picture comprehension.
Principles akin to our processing of visual images include the multimedia
principle and the modality principle, and the conjoint retention hypothesis too.
Established visual design principles include the split-attention principle, and the
spatial temporal contiguity principle. Additional principles for information design
of simulations and animations includes the cueing principle, representation type
of information, color coding, and the integration of multiple dynamic visual
representations. Interaction design principles include learner control, segmenting,
and guided discovery. Additional interaction principles include learner controlledpacing, task appropriateness, and manipulation of content.
“This research has important theoretical and educational design implications. On
the theoretical side, our review provides a summary of existing research that
shows a strong need for additional theoretical and empirical work, especially
connecting the study of learning from dynamic visualizations with work in the
area of neuroscience and cognition. On the educational design side, this paper
presents existing and emerging design guidelines specifically for the design of
dynamic
visualizations” (Plass, et al., 2009)
I think this quote represents the distance required to link educational design with
science for a truer understanding of how to design for educational effectiveness.
It makes sense considering the technology enabling multimedia design is still
new, and changing rapidly. Therefore, there is a lot to learn.
Design Factors for Effective Science Simulations: Representation of Information
Plass, J.L., Homer, B.D., Milne, C., Jordan, T., Kalyuga, S., Kim, M., & Lee, H.J. (2009).
Design Factors
for Effective Science Simulations: Representation of Information. International
Journal of Gaming and Computer-Mediated Simulations, 1(1), 16–35.
This article focuses on the use of pictorial representations in design of science
simulations, in particular icons. The rationale is that using icons will reduce
intrinsic cognitive load because a typically simulation has a high level of
elemental interactivity. Therefore, low level learners may be adversely affected
by complex science simulations. According to Plass, Homer, Milne, Jordan,
Kalyuga, Kim, and Lee (2009), the current research supports that significant
cognitive resources are required to process interactive simulations. Research
also suggests that low level prior knowledge learners in a particular domain may
benefit from iconic visual representations incorporated into visual materials. Prior
knowledge, in the study discussed in this article is the defining learner
characteristic. The present study is concerned with how the use of icons will help
low level learners in scientific multimedia simulations.
The results of the study, and research “suggests that design factors such as the
type of representation of key concepts in the simulation affect the effectiveness
of computer simulations when cognitive load is high, and that individual
differences variables such as prior knowledge moderate these effects” (Plass, et
al.). The research also demonstrated adding icons improved comprehension,
especially for low prior knowledge learners, icons also improve learners’ selfefficacy, and that individual differences matter. In other words, icons facilitate
learning for low prior knowledge learners’ in a domain within a science
simulation.
Optional:
Instructional Animation Versus Static Pictures: A Meta- Analysis
Höffler, T., & Leutner, D. (2007). Instructional animation versus static pictures: A metaanalysis. Learning
and Instruction, 17, 722 -738.
This article is a meta-analysis review of selected research about the educational
effectiveness of instructional animation compared to static pictures. It effectively
challenges the assumption that animations have no learning value over static
pictures.
The research is based in the theoretical framework of the ITCP model, Cognitive
Theory of Multimedia Learning, Cognitive Load Theory, and others. Elements of
these theories that are applicable to the research range from the learners
construction of knowledge, to the types of cognitive load, to representations, to
learner characteristics (i.e. spatial ability, prior knowledge). Animations have the
potential to provide external representations of visualizations, reduce cognitive
load, and promote meaningful learning.
The results of the meta-analysis demonstrated that animations, under the right
conditions, are better learning tools then pictures. In particular, when they are
relevant to the learning material, have realism, for declarative, procedural, and
problem-solving knowledge acquisition, and
April 28, 2014
Cognitive Development: Stages of Development (Piaget), Interactionist
Theories (Bruner, Vygotsky)
Cognitive and Knowledge Development
Driscoll, M. (2005). Psychology of Learning for Instruction (3rd ed.). Boston, MA: Allyn and
Bacon. [ Chapters 6 ]
When I read about Jean Piaget, the adage “Ages and Stages” comes to mind.
His view of cognitive development is called constructivism. “Piaget believed that
children actively approach their environments and acquire knowledge through
their actions” (Driscoll, 2005). According to Piaget, knowledge is within the child,
and is constructed through the child's’ interaction with the world. The construction
of knowledge happens in stages of the childs development. The types of
knowledge the child acquires includes physical knowledge, logical-mathematical
knowledge, and social knowledge. There are four stages of development ranging
from age two - eleven, and the stages (1) represent a qualitative change in the
children’s cognition, (2) occurs in a culturally invariant sequence, (3) represent
cognitive structures and abilities of the preceding stage, and (4) schemas and
operations form an integrated whole. According to Piaget, all children reach the
final stage and once a stage is passed, there is no regression. The process of
development through the stages includes assimilation, accommodation, and
equilibration.
Piaget’s theory - of genetic epistemology - is not without criticism, especially by
information processing theorists.For example, some research indicated that
culture impacts the age at which children transition through stages, regression
between stages can occur, and learning can advance stage development.
There are no clear implications for instruction regarding Piaget. However, some
general principles exists: the learning environment should support the activity of
the child, children's interactions with their peers are an important source of
cognitive development, and, adopt instructional strategies that make children
aware of inconsistencies in their thinking. On the information-processing
perspective, instructional implications demonstrate the role of rules in children's
thinking and promotion of conceptual change.
Interactional Theories of Cognitive Development
Driscoll, M. (2005). Psychology of Learning for Instruction (3rd ed.). Boston, MA: Allyn and
Bacon. [ Chapters 7 ]
Contrary to to Piaget, there is Bruner who believes children learn through their
experience by creating representations through which they understand the world.
Secondly, culture plays a role in children's cognitive growth, and schooling is an
instrument of culture. The three modes of representation - the systems by which
people understand the world, are enactive representation, iconic representation,
and symbolic representation. Unlike Piaget, Bruner believed that anyone can
learn through these representations at any stage of development, but that
development wasn’t related to ages. Like Piaget, the representation models are
sequential. The use of these modes, Bruner suggests, is goal oriented to the
learning scenario. Children transfer between stages of representation through
interaction with others, and with culture.
Another significant theme of Bruner’s discovery learning, which is a pedagogical
strategy to systematically help children discover information for themselves (in
their own heads). It is a way of guiding the learner through inquiry, use of priori
knowledge, reflection, and contrast. It is the basis for another Bruner-like model
of inquiry teaching. Again, another strategy based approach to assist children to
discover in their minds a concept and learn through the guided inquiry. Both
approaches are problem-solving approaches.
Also, culture plays an important part of children interpreting their experience for
learning, and school - through inquiry activities - aids the interpretation.In other
words, “getting students to transfer skills they already possess to other situations
relevant to the school contenxt” (Driscoll, 2005).
Driscoll (2005) shares that Bruner believed in theories of development and
instruction as complementary. To that end, Driscoll (2005) suggest implications
for instruction ought to consider how efficiently children transfer knowledge
through representation, economically. This sounds to me like avoiding Cognitive
Load. To achieve this, Bruner suggest methods of instruction that promote
problem solving within the context of culture. “Exposing children to discovery
learning can...promote a sense of self-reward...students become motivated to
learn...” (Driscoll, 2005).
Akin in some ways to Bruner is Vygotsky who also focused on the process of
development of intellect, but excluded developmental stages.
“Vygotsky’s theoretical framework has three core themes: (1) reliance on a
genetic or developmental method; (2) the claim that higher mental
processes in the individual have their origin in social processes; and (3)
the claim that mental process can be understood only if we understand the
tools and signs that mediate them... Vygotsky’s perspective developed
from studying the natural process of development in humans, make crossspecies comparisons, and to consider the socio-cultural factors that
mediate development” (Driscoll, 2005).
Perhaps Vygotsky is best known for his Zone of Proximal Development, which is
the gap between what the child's current developmental level of versus what the
developmentally capable of. It is in the gap where instruction plays a role, which
helps the child reach his highest limits. Through the instruction, the child is
interacting with others. Therefore, learning is a social activity - he learning tools
and skills through practice with others.
Appendix 1: Feedback from Elizabeth
Feedback from Elizabeth
Make sure you know the similarities and differences between the ITCP and CTML. (Week 6)
I think you asked me about citations in an edited book. So for examples for chapter 15 in
Mayer...
Citation for chapter in book:
Renkl, A. (2005). The Worked-Out Examples Principle in Multimedia Learning. In R.E. Mayer,
The Cambridge Handbook of Multimedia Learning (pp. 229-246). Cambridge University Press.
In-Text Citation (Paraphrase):
(Renkl, 2005)
In terms of how to assess for timing of transitions - all this work has to be done up front and
planned for by expert or expert/algorithm. The expert decides when the learner has reached a
particular level based on design - mapping of cognitive domain, learning objectives, and items.
Spatial Ability and Extraneous Load
Temporal contiguity effect; which describes “learning advantages for materials with concurrent
presentation of narration and animation over the successive presentation of narration and
animation, was strong for students with high but not for those with low spatial abilities”
Low-spatial ability better with guided structures (compared to high spatial-ability, though spent
more cognitive resources on low-level processing and decoding of vocabulary words and less
on the comprehension of text.
Adaptive learning: Can only really adapt in terms of cognitive abilities. Adapt in terms of ability
and as students become more proficient increase complexity as complexity changes,
presentation formats change.
Learning Style:
Not enough evidence to support learning styles as a strong predictor of individual differences in
learning.
Learning Preferences:
1. While there may be verbalizers and visualizers in terms of mode preferences despite these
differences in preferences both learn vocabulary best when presented with both visual and
verbal modes. Each will select their preferred cue for retrieval.
2. While there may be verbalizers and visualizers in terms of mode preferences despite these
differences in preferences visualizers learn reading comprehension best when both visual and
verbal modes are available but not on verbal only whereas verbalizers perform well on both
visual and verbal AND verbal only. If the visualizer is presented with verbal only for reading
comprehension the visualizer will not be able to make the verbal and visual referential
connection for comprehension and thus will fail during retrieval.
Thus: Provide options for learners to select and process material that is presented in both visual
and verbal modes. Differences have no affect in terms of differences in design because you are
presenting both in the design. Presenting both visual and verbal formats will work for both even
if verbalizers choose only need one - presenting both will not harm the verbalizer.
Preferences are basically the same as learning styles.
The differences that are affected by instructional design are cognitive abilities. Ability can be
changed with effort and time.