This document summarizes a seminar paper on the implications of information processing for adult education practices. The paper reviewed literature on human cognitive architecture and theories of cognition and learning. It discussed how information is processed through sensory memory, working memory, and long-term memory according to information processing theory. It also described Cognitive Load Theory and the three types of cognitive load: intrinsic, extraneous, and germane. The paper concluded that understanding cognitive structures and how information is processed can help improve instructional design and enhance learning, especially for adult students in healthcare fields who must learn large amounts of complex information.
Cognitive Load Persuade Attribute for Special Need Education System Using Dat...ijdmtaiir
Human learning system is highly sensitive to
responsive system according the processing, mapping, motion,
auditory and visualization system. Special education system is
implemented to overcome the demanded sense of the human
special care sensitive signals. This responsive system is
balanced and effectively instrumented with modern
technological learning pedagogy to bring the special need
learners into the normal learning system. In the learning
process, cognitive human sensors directly influence the
learning effectiveness. This paper attempted to observe the
cognitive load such as mental , physical , temporal
,performance , effort and frustration in the long term , short
term, working , instant , responsive, process, recollect ,
reference , instruction and action memory and classify the
observed values as per the generalized and specialized
properties. The six working loads are observed in the ten types
of learning system. The classification analysis aimed to
predicate the pattern for learning system for specific learning
challenges.
DESIGNING A WORKING MEMORY CAPACITY TEST FOR COGNITIVE-FRIENDLY TANGIBLE MULT...ijma
A working memory capacity (WMC) test called “objects-span tri-tasks” is designed for preschoolers
undergoing treatment using a new genre of multimedia, tangible multimedia, created by the authors. It tests
the dual-functions of the preschoolers’ working memory (WM), namely storage and manipulation capacity,
essential in supporting academic skills. The third task in the test is the overt setting of task engaging the
long-term memory that supports the operation of WM. Tangible multimedia potentially enhances the WMC
of preschoolers to a considerable extent because firstly, it uses tangible objects that are cognitively
appropriate to the “preoperational” stage of preschoolers, and secondly, it simultaneously stimulates three
main sensory channels, prescribed as equally crucial in knowledge acquisition in human memory theories.
A pragmatic significance of the research is that it deepens the scope of multimedia research by looking into
the aspect of cognitive structure which is rarely conducted in the multimedia realm. It also demonstrates an
important step forward in multimedia research by relating WMC to the newly explored tangible
multimedia, which could determine the real capability and value of such system. This paper starts off by
discussing the underlying theories that contribute to the formation of the system and test, followed by its
procedure, and a brief report of a case study
DESIGNING A WORKING MEMORY CAPACITY TEST FOR COGNITIVE-FRIENDLY TANGIBLE MULT...ijma
A working memory capacity (WMC) test called “objects-span tri-tasks” is designed for preschoolers undergoing treatment using a new genre of multimedia, tangible multimedia, created by the authors. It tests the dual-functions of the preschoolers’ working memory (WM), namely storage and manipulation capacity, essential in supporting academic skills. The third task in the test is the overt setting of task engaging the
long-term memory that supports the operation of WM. Tangible multimedia potentially enhances the WMC of preschoolers to a considerable extent because firstly, it uses tangible objects that are cognitively appropriate to the “preoperational” stage of preschoolers, and secondly, it simultaneously stimulates three
main sensory channels, prescribed as equally crucial in knowledge acquisition in human memory theories.A pragmatic significance of the research is that it deepens the scope of multimedia research by looking into the aspect of cognitive structure which is rarely conducted in the multimedia realm. It also demonstrates an important step forward in multimedia research by relating WMC to the newly explored tangible
multimedia, which could determine the real capability and value of such system. This paper starts off by discussing the underlying theories that contribute to the formation of the system and test, followed by its procedure, and a brief report of a case study.
Cognitive Load Persuade Attribute for Special Need Education System Using Dat...ijdmtaiir
Human learning system is highly sensitive to
responsive system according the processing, mapping, motion,
auditory and visualization system. Special education system is
implemented to overcome the demanded sense of the human
special care sensitive signals. This responsive system is
balanced and effectively instrumented with modern
technological learning pedagogy to bring the special need
learners into the normal learning system. In the learning
process, cognitive human sensors directly influence the
learning effectiveness. This paper attempted to observe the
cognitive load such as mental , physical , temporal
,performance , effort and frustration in the long term , short
term, working , instant , responsive, process, recollect ,
reference , instruction and action memory and classify the
observed values as per the generalized and specialized
properties. The six working loads are observed in the ten types
of learning system. The classification analysis aimed to
predicate the pattern for learning system for specific learning
challenges.
DESIGNING A WORKING MEMORY CAPACITY TEST FOR COGNITIVE-FRIENDLY TANGIBLE MULT...ijma
A working memory capacity (WMC) test called “objects-span tri-tasks” is designed for preschoolers
undergoing treatment using a new genre of multimedia, tangible multimedia, created by the authors. It tests
the dual-functions of the preschoolers’ working memory (WM), namely storage and manipulation capacity,
essential in supporting academic skills. The third task in the test is the overt setting of task engaging the
long-term memory that supports the operation of WM. Tangible multimedia potentially enhances the WMC
of preschoolers to a considerable extent because firstly, it uses tangible objects that are cognitively
appropriate to the “preoperational” stage of preschoolers, and secondly, it simultaneously stimulates three
main sensory channels, prescribed as equally crucial in knowledge acquisition in human memory theories.
A pragmatic significance of the research is that it deepens the scope of multimedia research by looking into
the aspect of cognitive structure which is rarely conducted in the multimedia realm. It also demonstrates an
important step forward in multimedia research by relating WMC to the newly explored tangible
multimedia, which could determine the real capability and value of such system. This paper starts off by
discussing the underlying theories that contribute to the formation of the system and test, followed by its
procedure, and a brief report of a case study
DESIGNING A WORKING MEMORY CAPACITY TEST FOR COGNITIVE-FRIENDLY TANGIBLE MULT...ijma
A working memory capacity (WMC) test called “objects-span tri-tasks” is designed for preschoolers undergoing treatment using a new genre of multimedia, tangible multimedia, created by the authors. It tests the dual-functions of the preschoolers’ working memory (WM), namely storage and manipulation capacity, essential in supporting academic skills. The third task in the test is the overt setting of task engaging the
long-term memory that supports the operation of WM. Tangible multimedia potentially enhances the WMC of preschoolers to a considerable extent because firstly, it uses tangible objects that are cognitively appropriate to the “preoperational” stage of preschoolers, and secondly, it simultaneously stimulates three
main sensory channels, prescribed as equally crucial in knowledge acquisition in human memory theories.A pragmatic significance of the research is that it deepens the scope of multimedia research by looking into the aspect of cognitive structure which is rarely conducted in the multimedia realm. It also demonstrates an important step forward in multimedia research by relating WMC to the newly explored tangible
multimedia, which could determine the real capability and value of such system. This paper starts off by discussing the underlying theories that contribute to the formation of the system and test, followed by its procedure, and a brief report of a case study.
Computational Neuroscience - The Brain - Computer Science InterfaceChristopher Currin
Understanding intelligence is one of the most challenging scientific problems faced by humanity.
This talk will provide an introduction to the multi-disciplinary field of Computational Neuroscience: the questions it seeks to answer and some of the (mathematical & computational) techniques used to investigate how we fundamentally think.
Currently doing his Computational Neuroscience PhD at the University of Cape Town, Chris has a wonderfully weird background in machine learning, neuroscience, and psychology. He is fascinated by how we think and learn, sometimes to a fault, and how this works in both biological and artificial intelligence.
Optimising the use of note-taking as an external cognitive aid for increasing...Tamas Makany
Taking notes is of uttermost importance in academic and commercial use and
success. Different techniques for note-taking utilise different cognitive pro-
cesses and strategies. This experimental study examined ways to enhance
cognitive performance via different note-taking techniques. By comparing
performances of traditional, linear style note-taking with alternative non-
linear technique, we aimed to examine the efficiency and importance of dif-
ferent ways of taking notes. Twenty-six volunteer adult learners from an
information management course participated in this study. Cognitive perfor-
mance scores from a traditional linear note-taking group were compared with
another group by using a commercially available non-linear note-taking tech-
nique. Both groups were tested in two settings: after a classroom lecture and a
panel forum discussion. Tasks included measures on story comprehension,
memory, complexity of mental representations and metacognitive skills. Data
analysis revealed that the non-linear note-takers were significantly better than
the linear group both in terms of the quantity and the quality of the learned
material. This study demonstrates the importance of using cognitively com-
patible note-taking techniques. It identifies the cognitive mechanisms behind
effective note-taking and knowledge representation. Using such techniques
enables deeper understanding and more integrated knowledge management.
Role of Executive Functioning and Literary Reapproach for Measures of Intelli...inventionjournals
Over the years, Intelligence has been a crucial part in Psychological practices. Basic operational definition behind construct of Intelligence proposed by Wechsler (1944), was to act purposefully (Plan and control behaviors) and thinking rationally (organize and direct behavior). This operational definition was afterwards incorporated in measures for intelligence but as these measures were first aligned with academics, a major part of basic definition got overlooked. Previously Intelligence was divided in two major components that are Crystalized and Fluid Intelligence but resent Literary Reaproach was intended to enlighten the basic purpose of Intelligence measures and to highlight the overlooked components of Intelligence. These components are then further aligned with behavioral interpretations of Executive functions. It is proposed that alliance of Fluid Intelligence with Executive Functioning can bring pronounced change in clinical practices and change the bookish views of Intelligence into a functional approach.
Ted Willke, Sr Principal Engineer, Intel at MLconf SEA - 5/20/16MLconf
Can Cognitive Neuroscience Provide a Theory of Deep Learning Capacity?: Deep neural networks have achieved learning feats for video, image, and speech recognition that leave other techniques far behind. For example, the error rate on the ImageNet 2012 object recognition challenge was halved with the introduction of deep convolutional nets and now they dominate these competitions. At the same time, the industry is busy putting them to use on applications spanning autonomous driving to product recommenders and researchers continue to propose more elaborate topologies and intricate training techniques. But our theoretical understanding of how these networks encode representations of the “things they see” is far behind, as is our understanding of their limitations.
To advance deep neural network design from “black magic” to an engineering problem, we need to understand the impact that the choice of topology and parameters have on learnt representations and the processing that a network is capable of. How many representations can a given network store? How does representation “reuse” impact learning rate and learning capacity? How many tasks can a given network perform?
In this talk, I’ll describe why the human brain, with its seemingly unlimited parallel distributed processing, is downright terrible at multi-tasking and why this is totally logical. And I’ll describe the theoretical implications this may have for artificial neural networks. I’ll also describe very recent work that sheds some light on how representations are encoded and how our research team is extending this work to create practical best practices for network design.
A Conceptual Model for Ontology Based LearningIJORCS
Utilizing learning features by many fields like education, artificial intelligence, and multi-agent systems, leads to generation of various definitions for this concept. In this article, these field’s significant definitions for learning will be presented, and their key concepts in each field will be described. Using the mentioned features in different learning definitions, ontology will get presented for the concept of learning. In the ontology, the main ontological concepts and their relations have been represented. Also a conceptual model for learning based on presented ontology will be proposed by means of model and modeling description. Then concepts of presented definitions are going to be shown in proposed model and after that, the model’s functionality will be discuss. Twelve main characteristics have been used to describe the proposed model’s functionality. Utilizing learning ontology to improve the proposed conceptual model can be used also as a guide to model learning and also can be useful in different learning models’ comparison. So that the key concepts which can be used for considered learning model will be determined. Furthermore, an example based on proposed ontology and definition features is explained.
At the very heart of cognitive psychology is the idea of information processing. Cognitive psychology sees the individual as a processor of information, in much the same way that a computer takes in information and follows a program to produce an output.Cognitive psychology compares the human mind to a computer, suggesting that we too are information processors and that it is possible and desirable to study the internal mental / mediational processes that lie between the stimuli (in our environment) and the response we make.
The information processing paradigm of cognitive psychology views that minds in terms of a computer when processing information.
However, there are important difference between humans and computers. The mind does not process information like a computer as computers don’t have emotions or get tired like humans
Computational Neuroscience - The Brain - Computer Science InterfaceChristopher Currin
Understanding intelligence is one of the most challenging scientific problems faced by humanity.
This talk will provide an introduction to the multi-disciplinary field of Computational Neuroscience: the questions it seeks to answer and some of the (mathematical & computational) techniques used to investigate how we fundamentally think.
Currently doing his Computational Neuroscience PhD at the University of Cape Town, Chris has a wonderfully weird background in machine learning, neuroscience, and psychology. He is fascinated by how we think and learn, sometimes to a fault, and how this works in both biological and artificial intelligence.
Optimising the use of note-taking as an external cognitive aid for increasing...Tamas Makany
Taking notes is of uttermost importance in academic and commercial use and
success. Different techniques for note-taking utilise different cognitive pro-
cesses and strategies. This experimental study examined ways to enhance
cognitive performance via different note-taking techniques. By comparing
performances of traditional, linear style note-taking with alternative non-
linear technique, we aimed to examine the efficiency and importance of dif-
ferent ways of taking notes. Twenty-six volunteer adult learners from an
information management course participated in this study. Cognitive perfor-
mance scores from a traditional linear note-taking group were compared with
another group by using a commercially available non-linear note-taking tech-
nique. Both groups were tested in two settings: after a classroom lecture and a
panel forum discussion. Tasks included measures on story comprehension,
memory, complexity of mental representations and metacognitive skills. Data
analysis revealed that the non-linear note-takers were significantly better than
the linear group both in terms of the quantity and the quality of the learned
material. This study demonstrates the importance of using cognitively com-
patible note-taking techniques. It identifies the cognitive mechanisms behind
effective note-taking and knowledge representation. Using such techniques
enables deeper understanding and more integrated knowledge management.
Role of Executive Functioning and Literary Reapproach for Measures of Intelli...inventionjournals
Over the years, Intelligence has been a crucial part in Psychological practices. Basic operational definition behind construct of Intelligence proposed by Wechsler (1944), was to act purposefully (Plan and control behaviors) and thinking rationally (organize and direct behavior). This operational definition was afterwards incorporated in measures for intelligence but as these measures were first aligned with academics, a major part of basic definition got overlooked. Previously Intelligence was divided in two major components that are Crystalized and Fluid Intelligence but resent Literary Reaproach was intended to enlighten the basic purpose of Intelligence measures and to highlight the overlooked components of Intelligence. These components are then further aligned with behavioral interpretations of Executive functions. It is proposed that alliance of Fluid Intelligence with Executive Functioning can bring pronounced change in clinical practices and change the bookish views of Intelligence into a functional approach.
Ted Willke, Sr Principal Engineer, Intel at MLconf SEA - 5/20/16MLconf
Can Cognitive Neuroscience Provide a Theory of Deep Learning Capacity?: Deep neural networks have achieved learning feats for video, image, and speech recognition that leave other techniques far behind. For example, the error rate on the ImageNet 2012 object recognition challenge was halved with the introduction of deep convolutional nets and now they dominate these competitions. At the same time, the industry is busy putting them to use on applications spanning autonomous driving to product recommenders and researchers continue to propose more elaborate topologies and intricate training techniques. But our theoretical understanding of how these networks encode representations of the “things they see” is far behind, as is our understanding of their limitations.
To advance deep neural network design from “black magic” to an engineering problem, we need to understand the impact that the choice of topology and parameters have on learnt representations and the processing that a network is capable of. How many representations can a given network store? How does representation “reuse” impact learning rate and learning capacity? How many tasks can a given network perform?
In this talk, I’ll describe why the human brain, with its seemingly unlimited parallel distributed processing, is downright terrible at multi-tasking and why this is totally logical. And I’ll describe the theoretical implications this may have for artificial neural networks. I’ll also describe very recent work that sheds some light on how representations are encoded and how our research team is extending this work to create practical best practices for network design.
A Conceptual Model for Ontology Based LearningIJORCS
Utilizing learning features by many fields like education, artificial intelligence, and multi-agent systems, leads to generation of various definitions for this concept. In this article, these field’s significant definitions for learning will be presented, and their key concepts in each field will be described. Using the mentioned features in different learning definitions, ontology will get presented for the concept of learning. In the ontology, the main ontological concepts and their relations have been represented. Also a conceptual model for learning based on presented ontology will be proposed by means of model and modeling description. Then concepts of presented definitions are going to be shown in proposed model and after that, the model’s functionality will be discuss. Twelve main characteristics have been used to describe the proposed model’s functionality. Utilizing learning ontology to improve the proposed conceptual model can be used also as a guide to model learning and also can be useful in different learning models’ comparison. So that the key concepts which can be used for considered learning model will be determined. Furthermore, an example based on proposed ontology and definition features is explained.
At the very heart of cognitive psychology is the idea of information processing. Cognitive psychology sees the individual as a processor of information, in much the same way that a computer takes in information and follows a program to produce an output.Cognitive psychology compares the human mind to a computer, suggesting that we too are information processors and that it is possible and desirable to study the internal mental / mediational processes that lie between the stimuli (in our environment) and the response we make.
The information processing paradigm of cognitive psychology views that minds in terms of a computer when processing information.
However, there are important difference between humans and computers. The mind does not process information like a computer as computers don’t have emotions or get tired like humans
Reflection (1)This chapter explains learning and memories base.docxdebishakespeare
Reflection (1)
This chapter explains learning and memories based on the biology. Driscoll shows some theories that human’s learning is related to the genetic inheritance and brain physiology in Biology. There are two kinds of causes to explain human’s behavior: proximate cause and ultimate cause. Ultimate cause is kind of instinctive desires our ancestors have had been formed to survive for a long time and inherited, the other one, proximate desire is related to the expression of genes or presence of certain behaviors. Ultimate cause interacted with environment leads evolution effects on conditions and cognition. Proximate cause drags on the interest of neurophysiologists, which is studied in the area of the brain with attention, learning and memory, and cognitive development.
This chapter shows that implication of evolution psychology for learning and instruction. First, human may be predisposed to certain fear but it is possible to overcome it with appropriate instructions. Second, it is very difficult to establish if behaviors are not predisposed to learn, but it also can be established using certain instructions. Third, previously adapted behaviors and “actions associated with decreased fitness in ancestral population may be difficult to overcome and establish, respectively, but if we give proper instructions to overcome and establish, it is possible.
In addition, Driscoll shows implication of neurophysiology for learning and instruction. Cognitive functions play different roles in learning and human development, the brain has plasticity naturally, the learning of language may be biologically pre-programmed and disabilities with learning may be related to neurological basis. Yet we don’t know still how the brain works to store memory and information, and what roles the brain play in learning. Many researches are ongoing to find out how we improve our faculties in learning and developing.
Reflection (2)
This chapter of Driscoll’s Psychology of Learning for Instruction evaluates the effects of biology in memory and learning. This affects are divided into two parts: evolution and neurophysiology. Evolution has an effect on cognition and conditioning. It is considered the main cause or ultimate of learning and memory. Neurophysiology is the direct cause of learning and memory. The indirect causes of neurophysiology’s effect on learning and memory are the brain and attention. Evolution and conditioning refer to the age old psychology argument nature vs. nurture. It is between what we are born knowing and what the environment gives (teaches ) us. According to Driscoll (2005), there is evidence to recommend that operant and classical conditions are subject to biological influences. The reason for that is based on the study pointed by Garcia and Koelling. They made a research on taste aversion focus on how rats regarded illness and pain.
The chapter also claims that our evolutionary heritage and genetic require specific constrain ...
STUDY AND IMPLEMENTATION OF ADVANCED NEUROERGONOMIC TECHNIQUES acijjournal
Research in the area of neuroergonomics has blossomed in recent years with the emergence of noninvasive techniques for monitoring human brain function that can be used to study various aspects of human behavior in relation to technology and work, including mental workload, visual attention, working memory, motor control, human-automation interaction, and adaptive automation. Consequently, this interdisciplinary field is concerned with investigations of the neural bases of human perception, cognition, and performance in relation to systems and technologies in the real world -- for example, in
the use of computers and various other machines at home or in the workplace, and in operating vehicles such as aircraft, cars, trains, and ships. We will look at recent trends in functional magnetic resonance imaging (fMRI), with a special focus on the questions that have been addressed. This focus is
particularly important for functional neuroimaging, whose contributions will be measured by the depth of the questions asked. The ever-increasing understanding of the brain and behavior at work in the real world, the development of theoretical underpinnings, and the relentless spread of facilitative technology in the West and abroad are inexorably broadening the substrates for this interdisciplinary area of
research and practice. Neuroergonomics blends neuroscience and ergonomics to the mutual benefit of both fields, and extends the study of brain structure and function beyond the contrived laboratory settings often used in neuropsychological, psychophysical, cognitive science, and other neurosciencerelated fields. Neuroergonomics is providing rich observations of the brain and behavior at work, at home, in
transportation, and in other everyday environments in human operators who see, hear, feel, attend, remember, decide, plan, act, move, or manipulate objects among other people and technology in diverse, real-world settings. The neuroergonomics approach is allowing researchers to ask different questions
and develop new explanatory frameworks about humans at work in the real world and in relation to modern automated systems and machines, drawing from principles of neuropsychology, psychophysics, neurophysiology, and anatomy at neuronal and systems levels. The neuroergonomics approach allows researchers to ask different questions and develop new explanatory frameworks about humans at work in
the real world and in relation to modern automated systems and machines. Better understanding of brain function can, for example, provide important guidelines and constraints for theories of information presentation and task design, optimization of alerting and warning signals, development of neural prostheses, and the design of robots. As an interdisciplinary endeavor, neuroergonomics will continue to
benefit from and grow alongside developments in neuroscience, psychology,
Abstract - Human learning system is highly sensitive to responsive system according the processing, mapping, motion, auditory and visualization system. Special education system is implemented to overcome the demanded sense of the human special care sensitive signals. This responsive system is balanced and effectively instrumented with modern technological learning pedagogy to bring the special need learners into the normal learning system. In the learning process, cognitive human sensors directly influence the learning effectiveness. This paper attempted to observe the cognitive load such as mental , physical , temporal ,performance , effort and frustration in the long term , short term, working , instant , responsive, process, recollect , reference , instruction and action memory and classify the observed values as per the generalized and specialized properties. The six working loads are observed in the ten types of learning system. The classification analysis aimed to predicate the pattern for learning system for specific learning challenges.
Computers in Human Behavior xxx (2012) xxx–xxxContents lists.docxpatricke8
Computers in Human Behavior xxx (2012) xxx–xxx
Contents lists available at SciVerse ScienceDirect
Computers in Human Behavior
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / c o m p h u m b e h
Critical thinking in E-learning environments
Raafat George Saadé a,⇑, Danielle Morin a,1, Jennifer D.E. Thomas b,2
a Concordia University, John Molson School of Business, Montreal, Quebec, Canada
b Pace University, Ivan Seidenberg School of CSIS, New York, NY, USA
a r t i c l e i n f o
Article history:
Available online xxxx
Keywords:
E-learning
Critical thinking
Assessment
Information technology
0747-5632/$ - see front matter � 2012 Elsevier Ltd. A
http://dx.doi.org/10.1016/j.chb.2012.03.025
⇑ Corresponding author. Tel.: +1 514 848 2424; fax
E-mail address: [email protected] (R.G. Sa
1 Tel.: +1 514 848 2424; fax: +1 514 848 2824.
2 Tel.: +1 212 346 1569; fax: +1 212 346 1863.
Please cite this article in press as: Saadé, R. G., e
10.1016/j.chb.2012.03.025
a b s t r a c t
One of the primary aims of higher education in today’s information technology enabled classroom is to
make students more active in the learning process. The intended outcome of this increased IT-facilitated
student engagement is to foster important skills such as critical thinking used in both academia and
workplace environments. Critical thinking (CT) skills entails the ability(ies) of mental processes of discern-
ment, analysis and evaluation to achieve a logical understanding. Critical thinking in the classroom as well
as in the workplace is a central theme; however, with the dramatic increase of IT usage the mechanisms by
which critical thinking is fostered and used has changed. This article presents the work and results of
critical thinking in a virtual learning environment. We therefore present a web-based course and we
assess in which parts of the course, and to what extent, critical thinking was perceived to occur. The course
contained two categories of learning modules namely resources and interactive components. Critical
thinking was measured subjectively using the ART scale. Results indicate the significance of ‘‘interactivity’’
in what students perceived to be critical-thinking-oriented versus online material as a resource. Results
and opportunities that virtual environments present to foster critical thinking are discussed.
� 2012 Elsevier Ltd. All rights reserved.
1. Introduction
One of the primary aims of higher education in today’s informa-
tion technology (IT) enabled classroom, is to make students more
active in the learning process (Ibrahim & Samsa, 2009). The in-
tended outcome of this increased IT-facilitated student engage-
ment is to foster important skills such as critical thinking. Given
the importance of information technology for critical thinking in
learning, it is vital that we understand better the associated key
factors related to: background of students, beliefs, perceptions
and attitudes and associated anteceden.
Developing Cognitive Systems to Support Team Cognitiondiannepatricia
Steve Fiore from the University of Central Florida presented “Developing Cognitive Systems to Support Team Cognition” as part of the Cognitive Systems Institute Speaker Series
Describe how you would identify bottlenecks in your value stream .docxtheodorelove43763
Describe how you would identify bottlenecks in your value stream? How would you ensure sustained flow through those bottlenecks?
Answer:
The term ‘bottleneck’ (capacity constraint) comes from the area at the top of the bottle that limits the flow coming out. It doesn’t matter how big the rest of the bottle is—liquid will only flow out as fast as the size of the neck will allow.
There are two main types of bottlenecks:
· Short-term bottlenecks – These are caused by temporary problems.
· Long-term bottlenecks – These occur all the time.
Bottleneck identification in value stream:
The simplest and most logical way to identify process bottlenecks is to look for the biggest causes of stress. Consider these questions:
· Is there a routine or system that has a high level of employee stress involved in it? If there is, then it is not a well-laid out system.
· Is work continually delayed because employees are waiting for reports, products, more information or other resources?
· Is there too much work piled up at one end of the production (or service) cycle and not enough at the other end?
· Are certain departments always late in delivering needed items to both internal and external customers?
· Perform simulation of system according to flow chart of value chain
Way to eliminate bottleneck for sustained flow:
Once you identify the root cause of your bottleneck, try one or more of these ideas to improve workflow:
· Increase quality of input.-zero defect feed at bottlenecks
· Reorganize workflow
· Assign your best teams & technology at bottlenecks
· Add capacity.
· Accept partial delivery
References
http://www.qmpls.org/KnowledgeCentre/Newsletter/CurrentIssue/tabid/88/entryid/153/Default.aspx
http://www.slideshare.net/dutconsult/eliminating-the-production-bottlenecks
Describe the importance of process mapping in a supply chain flow? How would you use process maps? How do you know what to focus on when creating a process map?
Answer
Supply chain mapping allows a company to identify bottlenecks by:
· providing visibility into how processes are carried out;
· identifying where the processes are executed;
· identifying who is doing what within the processes;
· revealing how processes affect other processes;
· determining why a process is being executed
· identify activities within a process that are not adding value;
Process maps are also known as flowcharts, flow diagrams, relationship maps or blueprints. Process maps can be used to create virtual model of system and performing simulations. Which will be used for analyzing bottlenecks in system , Rework pattern, Time consumption for at rework, cycle time, Inventory at various stages of system. They Can also be used to train new employees or to brief consultant about business process. Process maps can be created by Microsoft visio and eVSM add in for manufacturing unit.
When creating process maps:
· Identify All Value adding process/ activities
· Identifies value add points
· Identifies value.
Similar to Information Processing_Implications for Adult Education Practices by Jane White (19)
Describe how you would identify bottlenecks in your value stream .docx
Information Processing_Implications for Adult Education Practices by Jane White
1. The seminar paper entitled
Information Processing: Implications for Adult Education Practices
Is approved by Tom Lo Guidice on February 29, 2012
2. 2
Information Processing: Implications
For Adult Education Practices
__________________
A Seminar Paper
Presented to
The Graduate Faculty
University of Wisconsin-Platteville
__________________
In Partial Fulfillment of the
Requirement for the Degree
Masters of Science
in
Education
__________________
by
Jane White
February 2012
3. 3
Abstract
Jane White
Under the Supervision of Tom LoGuidice, PhD, Professor Emeritus
Human cognitive architecture, responsible for learning, is part of a powerful and complex
communications network. Learning occurs as information is processed by cognitive structures in
a series of steps with critical points and parameters. Research suggests that understanding
cognitive structures and how they function can improve instructional design and, in turn,
enhance learning. This paper presents a review of research literature to determine the
relationship between cognitive architecture, information processing systems and learning. In
addition, implications and recommendations for instructional design strategies are presented.
Cognition has been the focus of cognitive research and led to numerous learning theories.
Information processing is one learning theory that correlates instructional strategies to each step
of the three part memory system; sensory, working, and long-term memory.
Cognitive Load Theory (CLT) is a learning theory focused on working memory and defines
information processing in terms of load and capacity limitations. Evidence based instructional
design strategies are suggested for each step of the memory process, as well as, for managing
cognitive load.
Students pursuing careers in healthcare must acquire vast amounts of information. It is not
uncommon for students to feel overwhelmed and experience information overload. Research
suggests poor instructional design may interfere, obstruct, and overload the information
processing system. When this happens, learning fails. The review of literature will show that
designing instruction to work with cognitive architecture has proven implications adult education
practices.
4. 4
Table of Contents
Page
Approval Page………………………………...………………….……...1
Title page………………………………………………………….…..…2
Abstract…………………………………………………………………..3
Table of Contents…………………………….…………………………..4
Chapter I Introduction...…………………………………..……...…5
Introduction
Statement of the Problem
Definitions of Terms
Delimitations of the Research
Method of Approach
Chapter II Review of Literature……………………………….……..9
Human cognitive architecture
Cognitive learning theory
Cognitive load theory
Implications for adult education
Chapter III Conclusions and Recommendations…………………..….20
References……………………………………….……………………….22
Appendices..………………………………………………………….......24
Appendix A: Using the information processing approach in the classroom
Appendix B: Design principles and strategies recommended by cognitive load
theory
5. 5
Chapter I Introduction
In health science courses, it is not uncommon for students to feel overwhelmed and
experience information overload. Students pursuing careers in healthcare must acquire vast
amounts of information, technical clinical skills, and ethical attitudes of the profession. Students
must learn complex body system structures and their functions in conditions of health and
disease. Moreover, health professionals are required to learn the medical language which has
been compared to learning a non-native language. According to research, health science
coursework places heavy demands upon cognitive structures responsible for learning
(van Merrienboer & Sweller, 2010). The study of how our minds work, how we remember, and
ultimately, how we learn is the focus of cognitive research (Connor, 2007). Therefore, an
examination of cognitive research and the implications for adult education is warranted.
Human cognitive architecture responsible for learning is part of a powerful and complex
communications network. Understanding cognitive structures and how they function can
improve the way we teach (Connor, 2007). Although the human brain has been studied for
thousands of years, scientist have yet to unravel the all the mysteries of intelligence, personality,
preferences, imagination, learning and memory (Huitt, 2003). However, recent advances in
cognitive research and brain imaging technology have added significantly to our understanding
of how information is processed and learning occurs (Brain facts, 2008). Brain structures
communicate through a vast network of neural connections and perform complex functions that
control and direct all of life’s vital functions, critical thinking, actions and reactions, emotions
and learning (Brain facts, 2008; Longenbaker, 2011). Cognitive brain structures carry out the
essential processing of information that leads to learning (Brain facts, 2008).
6. 6
Cognitive learning theory, which gained popularity in the 1960’s, is based upon how the
brain processes information. Cognitive psychologists likened the human brain to a computer
processor (Martinez, 2010). Cognitive structures, called schema, were shown to provide the
mental framework in which information was programmed, formatted, reformatted and saved
(Connor, 2007; Martinez, 2010). However, unlike a computer, the human memory system is
selective, has processing limitations, and unlimited space for storing information (Connor, 2007;
Martinez, 2010). Information processing theory, a model of learning based upon the mental
processes of cognition, has yielded numerous instructional design strategies (Huitt, 2003).
Cognitive Load Theory (CLT) is rooted in and expands upon cognitive learning theory and
information processing theory. CLT explains learning in terms of the load imposed by new
information and the limited capacity of working memory (van Merrienboer & Sweller, 2010).
Managing the relationship between cognitive load and cognitive resource capacity for optimal
learning is the goal of CLT (van Merrienboer & Sweller, 2010).
Instructional design strategies based on cognitive learning theory are grounded in authentic
research and shown to improve the design, delivery, and retention of instruction (Connor, 2007).
According to van Merrienboer and Sweller (2010), CLT design principles are extremely useful
for teaching the health sciences.
7. 7
Statement of the Problem
Learning occurs as information is processed by cognitive structures in a series of steps with
critical points and parameters. Information processing models of learning aim to design
instruction to work with human cognitive architecture. The problem to be addressed is what are
the implications of information processing on adult education practices?
Definition of Terms
1. Sensory memory is the mental spaces that receive sensory information (van Merrienboer
& Sweller, 2010).
2. Working memory are mental workspaces that process, assimilate and organize
information (van Merrienboer & Sweller, 2010).
3. Long term memory is the mental structures that store information for long periods of time
(van Merrienboer & Sweller, 2010).
4. Intrinsic cognitive load refers to the inherent complexity of information to be learned and
the corresponding amount of working memory capacity required for processing
(van Merrienboer & Sweller, 2010).
5. Extraneous cognitive load refers to information that requires processing in working
memory but doesn’t contribute to learning (van Merrienboer & Sweller, 2010).
6. Germane cognitive load refers to working memory capacity used for making sense of
information and the actual learning (van Merrienboer & Sweller, 2010).
8. 8
Delimitations of Research
The references used for the review of literature were collected over a period of 60 days
using the resources of the Karmann Library at the University of Wisconsin-Platteville. The
several search engines provided by EBSCOHOST were used. Key search terms were
“information processing”, “working memory”, “neuroscience”, “cognitive load” and “health
science education”.
Method of Approach
A brief review of literature on the human cognitive architecture and the history of cognitive
learning theory were conducted. A comprehensive literature review on the information
processing and cognitive load learning theories were conducted. A review of literature regarding
instructional design and cognitive learning theory and anecdotal evidence of adult students in
health science courses was conducted. The findings were synthesized, summarized and
recommendations made.
9. 9
Chapter II Review of Literature
Overview of Human Cognitive Architecture
Brain structures of human cognition, also called knowledge structures, are the essential
component parts for processing information that account for learning (Ifenthaler, Masduki, &
Seel, 2009). As shown in Figure 1, major cognitive structures include the cerebrum, cerebral
cortex, thalamus, hypothalamus, hippocampus, cerebellum, and amygdala (Brain facts, 2008;
Longenbaker, 2011). The cerebrum is the largest part of the brain and functions to communicate
with and coordinate all the activities of the rest of the brain. Higher level thinking processes
required for learning and memory is directed by the cerebrum (Brain facts, 2008; Longenbaker,
2011). The cerebral cortex is the deeply convoluted outer covering of the cerebrum and is
divided into four sections called lobes. Cortical lobes play key roles in processing sensory
information, attention, emotion, thinking,
planning, and language (Brain facts, 2008).
The medial temporal lobe is richly connected
to widespread areas of the cerebral cortex
and important for forming, organizing,
consolidating, and retrieving information
(Brain facts, 2008).
Acting as a relay station, the thalamus
receives and directs sensory input from the
sensory organs of touch, taste, smell, sight
and sound (Brain facts, 2008).
Figure 1. Major cognitive structures. This figure
shows two views of the brain and identifies the
location of cognitive structures. (Brain facts, 2008)
10. 10
The hippocampus, a seahorse shaped organ below the thalamus, is vital to the processing of
episodic memories: personal experience and events (Brain facts, 2008). The cerebellum assists
in the learning of new motor skills, controls movement and coordination (Brain facts, 2008;
Longenbaker, 2011). Lastly, the amygdala plays an important role in the emotional aspects of
memory by attaching emotional significance to otherwise neutral information and events (Brain
facts, 2008). Together, these hidden cognitive structures make up the powerful and complex
information processing system that ultimately lead to learning (Brain facts, 2008).
11. 11
Overview of Cognitive Theory
Cognition simply means knowing. Cognitive theory is the study of cognition and focuses on
how one comes to know. Over his lifetime, Jean Piaget, (1896-1980) made significant
contributions to the understanding of cognition through research focused on human cognitive
structures and functions in acquiring and maintaining knowledge (Huitt, 2003). Other important
cognitive theorists are Ausubel, Bruner, Gagne, Miller and Sweller (Conner, 2007; Huitt, 2003).
Within the framework of cognitive theory, a number of theoretical models with implications
for education have emerged. Information processing theory is one focused primarily on memory
and how information is received, stored and retrieved (Huitt, 2003; Terrell, 2006). “Memory is
one of the most important concepts in learning: if things are not remembered, no learning can
take place” (Kearsley, 2007).
As shown in Figure 2, memory is formed in a 3-step process as information moves from
sensory memory (SM) to working memory (WM), also called short term memory, and on into
long term memory (LTM) (Terrell, 2006). Step one involves the initial process of sorting out
important information items from the steady stream of messages being sent from sensory organs.
Sensory memory holds information briefly, lasting about .25 seconds for visual images and up to
3 seconds for auditory input (Huitt, 2003; Mayer 2010; Terrell, 2006). At this point, it is
absolutely critical that the learner attend to the information to-be-learned (Joyce et al., 2008).
For, only information items that are attended to are forwarded onto the second step of the
process: working memory (Joyce et al., 2008).
Step two involves the process of assimilating and encoding information to fit LTM and lasts
slightly longer, from 15 to 30 seconds (Huitt, 2003). At this step, keeping the information active
is essential for proper processing (Terrell, 2006). Two key features of WM are limited duration
12. 12
Figure 2. Cognitive architecture. This figure illustrates the three-step process of the human
memory system based on information processing theory. (Terrell, 2006)
and limited capacity for the number of “new” items it can process at any one time (Huitt, 2003).
Miller (1956) first presented the concept of “chunks” as meaningful units of information and that
the working memory could manage no more than 5-9 chunks at any one time (Huitt, 2003;
Terrell, 2006). More recent research suggests the number may be even less: 3-7 chunks of
information (Huitt, 2003). This limitation can create a “bottleneck” in the memory system
process and obstruct learning (Huitt, 2003).
Information processed by working memory is then stored in LTM, step three. LTM has
unlimited storage capacity and can last a lifetime (Huitt, 2003; Mayer, 2010). Information held
in LTM is organized into structures, collectively called schema, that categorize and store
information into a meaningful system (Huitt, 2003). Schemas are created as new information is
structured into new schema and changeable as new information is assimilated into existing
schema, or existing schemas are restructured (Huitt, 2003; Terrell, 2006).
13. 13
Cognitive Load Theory
Cognitive load theory (CLT) is rooted in and expands upon cognitive learning theory with
an interest in optimizing cognitive processing capacities through understanding the demands
learning places on cognitive resources. Cognitive load research aims to provide meaningful
measures of working memory capacity and strategies to fully utilize those capacities to improve
instruction and learning (Terrell, 2006). CLT has become a leading instructional theory
recognized around the world by researchers and educators alike (UNSW, 2011). John Sweller,
Emeritus Professor of the School of Education at the University New South Wales (UNSW),
Sydney Australia, pioneered the original research in the 1980’s and continues to lead the study of
this theory. Other principal CLT researchers include Ayres, Jin, Kalyuga, and Low (UNSW,
2011). According to experts in the field, “a major strength of CLT research is that it has been
carried out in ways that mirror real world complex learning environments” (Kirschner, Ayers, &
Chandler, 2011).
CLT makes the following assumptions about human cognitive architecture. Working
memory has limited capacity that can hold no more than five to seven information items at one
time and actively process no more than two to four information items at one time (van
Merrienboer & Sweller, 2010). Long-term memory has unlimited capacity and stores
information in schema, also called knowledge structures (van Merrienboer & Sweller, 2010).
CLT identifies three types of cognitive load that use up cognitive processing resources:
intrinsic load, also called essential cognitive processing, extraneous load, and germane load,
also called generative cognitive processing (Mayer, 2010; van Merrienboer & Sweller, 2010).
Intrinsic load represents essential information and the inherent complexity of the information to-
be-learned (Mayer, 2010; van Merrienboer & Sweller, 2010). Extraneous load represents non-
14. 14
a)
b)
c)
essential information that does not contribute to learning and is caused by poor instructional
design (Mayer, 2010; van Merrienboer & Sweller, 2010). Germane load represents working
memory resources used to organize, assimilate and encode intrinsic load, which leads to learning
(Mayer, 2010; van Merrienboer & Sweller, 2010).
Not everyone agrees that there ought to be three types of load. Kalyuga (2011), a colleague
of Sweller, suggests a dual model of intrinsic and extraneous load is sufficient. Kalyuga
suggests intrinsic and germane load are essentially the same and thus, redundant.
As shown in Figure 3, total cognitive load is the sum of the different types of load and
represents the amount of cognitive activity taking place in working memory at any moment in
time. If the sum total cognitive load exceeds working memory capacity, then learning fails.
The goal of CLT is to design instruction that minimize extraneous load, manage intrinsic load
and maximize germane load to optimize learning (van Merrienboer and Sweller, 2010).
A major factor contributing to cognitive load is the number of new items that need to be
processed (van Merrienboer and Sweller, 2010). New information lacks organization and each
item must be handled as a separate unit (van Merrienboer and Sweller, 2010). In contrast,
information retrieved from LTM is organized in schema and can be highly detailed and complex.
(van Merrienboer and Sweller, 2010). Schemas are treated as one single item in WM which
greatly reduces cognitive load (van Merrienboer & Sweller, 2010).
Figure 3. Cognitive Loads. This figure illustrates the additive nature of intrinsic and extraneous
load: (a) overload; (b) preventing overload by decreasing extraneous load, and (c) optimizing
germane load by increasing intrinsic load. (van Merrienboer and Sweller, 2010)
15. 15
Implications for Adult Education
The goal of cognitive learning theory is to design instruction that works with human
cognitive architecture. Understanding cognitive structures and how they function can help guide
the design, delivery, and retention of instruction from orientation through final exams. The
information processing models of learning provide a framework of mental processes from which
instruction is designed to focus attention, keep information active and facilitate the construction
and automation of schema. Due to the large amount of information and inherent complexity of
health science coursework, information processing models of instruction are highly relevant.
Scientific evidence supports the following instructional design strategies and practices.
The first section provides an overview of strategies based on each step of the information
processing model. Appendix A, Table 1 summarizes instructional design based on information
processing theory. The second section explains best educational practices based on CLT.
Appendix B, Table 2 summarizes instructional design based on CLT. (van Merrienboer &
Sweller, 2010)
Information Processing Model.
Attend: To-be-learned information must be attended to in order for it to be moved from sensory
memory onto working memory. Two key strategies for stimulating attention are to introduce
information with an interesting feature and use a known pattern that triggers retrieval of some
prior learning. (Huitt, 2003; MacLeod, 2010)
Active: Strategies that keep information active in working memory are important to extend
processing time. Rehearsal is one strategy that can extend processing time up to 20 minutes
(Joyce, et al.; 2008; Huitt, 2003). During rehearsal, retrieval cues are developed that facilitate
and strengthen recall (Joyce, et al., 2008; MacLeod, 2010). Miller’s concept of chunking, also
16. 16
called segmenting, has become an important organizational strategy for getting and keeping
information in working memory (Huitt, 2003). Chunking breaks apart large lesson plans into
smaller more manageable units of information (Mayer, 2010; Huitt, 2003).
Archive: Encoding is the processing and transfer of information from WM to LTM (MacLeod,
2010). Four powerful learning strategies for effective encoding are imagery, elaboration,
generation, and production (MacLeod, 2010; Huitt, 2003). Imagery is the ability to store visual
images and facilitates recall (MacLeod, 2010). Imagery is also associated with sensory motor
skills as one is able to mentally picture themselves performing a skill (Kearsley, 2007).
Elaboration expands the learner’s knowledge base by connecting new information to the things
the learner already knows (van Merrienboer and Sweller, 2010; Huitt, 2003; MacLeod, 2010).
Generation effect, also called the testing effect, requires the learner to retrieve information from
LTM which improves retention more than rereading or relearning the information (MacLeod,
2010; Huitt, 2003; Kirschner, et al., 2011). Furthermore, being unable to retrieve information
identifies areas needing more study and therefore is also beneficial to learning (Kirschner, et al.,
2011).
Lastly, the production effect, suggests words read out load enhances remembering by giving the
words a distinctive quality (MacLeod, 2010). “The basic idea of distinctiveness as an
explanatory mechanism is that information which is made to stand out from other information at
the time of encoding will show enhanced memory.” (MacLeod, 2010, p. 233) According to
MacLeod, 2010, mouthing the word or even imagining saying the word out loud is equally
effective for improved retention.
17. 17
Instructional design principles based on CLT have been grouped into three sections based on
these three goals: (a) reduce extraneous load, (b) manage intrinsic load, and (c) optimize
germane load.
Reduce extraneous load.
The goal-free principle is when conventional problem solving tasks with specific end goals are
replaced with goal-free tasks with non-specific end goals (van Merrienboer & Sweller, 2010).
Essentially, the learner takes what information is given and applies it where ever possible. Goal-
free tasks prompt a forward thinking cognitive process: a more advanced method of problem
solving that reduces cognitive load and facilitates learning (van Merrienboer & Sweller, 2010).
The worked example principle is when problems with worked out solutions are given for the
learner to study the problem solving process from beginning to end (van Merrienboer & Sweller,
2010). Worked examples provide a framework for solving the problem and guide the learner to
the solution. This principle eliminates the guesswork and reduces cognitive load caused by poor
problem solving skills of new learners (Paas, Gog, & Sweller, 2010). In a recent study, students
were given worked example problems and to-be-solved problems, both with varying ratios of
steps (Kirschner, et al., 2011). Worked examples reduced extraneous load whereas to-be-solved
problems increased extraneous load no matter the ratio of steps (Kirschner, et al., 2011). Closely
related to the worked example principle is the completion principle. Extraneous load is reduced
by having the learner finish finding the solution to partially worked out problems (van
Merrienboer & Sweller, 2010).
The split attention principle is when learning materials from different resources are combined
into one source and/or presented at the same time (van Merrienboer & Sweller, 2010). This
principle eliminates the mental energy it takes to integrate information from different places or
18. 18
that would have been presented at different times (van Merrienboer & Sweller, 2010). In the
same way, the redundancy principle is when needless repetition of information is eliminated (van
Merrienboer & Sweller, 2010).
The modality principle is when a spoken rather than written explanation is given with a visual
source of information (Mayer, 2010). This combination makes use of the separate channels for
processing auditory and visual information which reduces the load on working memory (Mayer,
2010).
Manage intrinsic load.
The simple-to-complex strategy is when a series of conventional tasks is replaced with tasks that
begin with singular elements and gradually adding elements until the full complexity of the task
is learned (van Merrienboer & Sweller, 2010). The low- to high-fidelity strategy is when
learning a task increasingly becomes more and more like that of real world practice (van
Merrienboer & Sweller, 2010). The learner progresses from performed in a low-fidelity
environment such as a simulation, and then progress to higher-fidelity environments that
resemble real world practice (van Merrienboer & Sweller, 2010).
Optimize Germane Load.
The variability principle “Replace a series of tasks with similar surface features with a series of
tasks that differ from one another on all dimensions on which tasks differ in the real world.”
(van Merrienboer & Sweller, 2010) This principle requires the learner to think carefully about
some quality or characteristic of a problem that is capable of changing. This thinking process
encourages schema construction as information is reorganized and solutions formulated.
Similarly, the contextual interference principle changes the order of tasks so that knowledge and
19. 19
skill sets required to complete the task are random rather than being grouped together (van
Merrienboer & Sweller, 2010).
The self-explanation principle is when the learner is asked to explain what they have learned
(van Merrienboer & Sweller, 2010). This principle requires the learner to assimilate prior
learning with new learning and promotes schema construction.
20. 20
Chapter III Conclusions and Recommendations
From the review of research literature, substantial evidence is found in support of instruction
designed to work with human cognitive architecture. Structures of cognition are connected
through a vast and changeable network of neurons that transmit process and store the volumes of
information learned over a lifetime. Cognitive learning theory has a solid base of empirical
studies and ongoing contemporary research which have made it a leading instructional design
theory.
Information processing theory provides a mental model of the pathways and processes that
lead to learning. Learning occurs as information is attended to in sensory memory, assimilated
and understood in working memory and stored in the appropriate location and format in long
term memory schema. Accordingly, strategies that focus attention and keep information active
in working memory should be incorporated into instruction. Recall of prior learning and
rehearsal are two such strategies. In addition, research suggests that imagery, elaboration,
generation, and production are four powerful strategies for encoding information into LTM.
Cognitive load theory offers practical design strategies aimed at effective information
management in order to prevent cognitive overload and facilitate optimum load. The limitations
of working memory require instructors to identify and reduce extraneous elements that do not
contribute to learning. Using worked examples and integrating sources of information are two
strategies that reduce extraneous load. Material that is inherently complex can also surpass WM
capacity and must be managed to prevent intrinsic overload. Breaking apart the material into
smaller chunks, and building from simple to complex elements are two strategies to manage
intrinsic load. CLT also suggests strategies that increase germane load into order to utilize the
full capacity of WM which will lead to optimal learning. The variability principle and contextual
21. 21
interference principle require information learned to be applied in new ways which increases
germane load and promotes schema construction and connections. Similarly, the self-
explanation principle requires the learner to construct and communicate the meaning of newly
acquired information. Although there is debate over the naming of two or three types of
cognitive load, the implications for adult education practices remain.
In conclusion, information processing has significant implications for adult education.
Future cognitive research will likely increase our understanding of cognition. Translating the
research into instructional design strategies will continue to improve adult education, including
health science coursework.
22. 22
References
Conner, M. L. (2007). A Primer on educational psychology. Retrieved from
http://agelesslearner.com/intros/edpsych.html
Huitt, W. (2003). The information processing approach to cognition. Retrieved from
http//www.edpsychinteractive.org/topics/cogsys/infoproc.html
Iftenthaler, D., Masduki, I., & Seel, N. M. (2011). The mystery of cognitive structure and how
we can detect it: Tracking the development of cognitive structures over time.
Instructional Science, 39 (1), 41-61. doi: 10.1007/s11251-009-9097-6
Joyce, B., Weil, M., & Calhoun, E. (2008). Models of teaching. (8th
ed.). Pearson Education,
Inc., Boston.
Kalyuga, S. (2011). Cognitive load theory: How many types of load does it really need?
Education Psychology Review, 23, 1-19. doi: 10.1007/s10648-010-9150-7
Kearsley, G. (2007). Memory. Retrieved from
http://home.sprynet.com/~gkearsley/tip/memory.html
Kearsley, G. (2007). Imagery. Retrieved from
http://home.sprynet.com/~gkearsley/tip/imagery.html
Kirschner, P. A., Ayers, P., & Chandler, P. (2011). Contemporary cognitive load theory research:
The good, the bad and the ugly. Computers in Human Behavior, 27, 99-105.
doi:10.1016/j.chb.2010.03.025
Longenbaker, S. N. (2011). The nervous system. Mader’s understanding human anatomy and
physiology. (7th
ed.). (pp. 155-179). New York, McGraw-Hill.
MacLeod, C. M. (2010). When learning met memory. Canadian Journal of Experimental
Psychology, 64 (4), 227-240. doi: 10.1037/a0021699
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Martinez, M. E. (2010). Human memory: The basics. Phi Delta Kappan, 91 (8), 62-65.
Retrieved from http://issuu.com/shanedee11/docs/1_-_human_memory_-_the_basics
Mayer, R. E. (2010). Applying the science of learning to medical education. Medical Education,
44, 543–549ª. doi:10.1111/j.1365-2923.2010.03264.x
Paas, F., Van Gog, T., Sweller, J. (2010). Cognitive load theory: new conceptualizations,
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doi: 10.1007/s10648-010-9133-8
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25. 25
Appendix A
Using the Information Processing Approach in the Classroom
Principle Example
1. Gain the students’ attention. Use cues to signal when you are ready to begin.
Move around the room and use voice inflections.
2. Bring to mind relevant prior
learning.
Review previous day’s lesson.
Have discussion about previously covered content.
3. Point out important information Provide handouts.
Write on the board or use transparencies.
4. Present information in an
organized manner.
Show a logical sequence to concepts and skills.
Go from simple to complex presenting new material.
5. Show how to categorize (chunk)
related information.
Present information in categories.
Teach inductive reasoning.
6. Provide opportunities for students
to elaborate on new information.
Connect new information to something already known.
Look for similarity and differences among concepts.
7. Show how to use coding when
memorizing lists.
Make up silly sentence with first letter of each word.
Use mental imagery techniques such as keyword method.
8. Provide for repetition of learning.
State important principles several times in different ways
during the presentation of information (STM)
Have items on each day’s lesson from previous lesson
(LTM)
Schedule periodic reviews of previously learned concepts
and skills (LTM)9. Provide opportunities for
overlearning of fundamentals.
Use daily drills for arithmetic facts.
Play form of trivial pursuit with content related to class.
Table 1. Information processing. This table provides principles and practical examples of the
information processing theory in education. (Huitt, 2003)
26. 26
Appendix B
Design principles and strategies recommended by cognitive load theory
Design
Guideline Description Applied to Health Education
Decreasing extraneous load
Goal-free
principle
Replace conventional tasks with goal-
free tasks that provide learners with a
non-specific goal.
Ask students to ‘Please come up with as many
illnesses as possible that could be related to
the observed symptoms’, rather than asking
‘Which illness is indicated by the symptoms?”
Worked
example
principle
Replace conventional tasks with
worked examples that provide full
solution learners must carefully study.
Let students criticize a ready-made treatment
plan, rather than having them independently
generate such a plan.
Completion
principle
Replace conventional tasks with
completion tasks that provide a partial
solution learners must finish.
Let medical interns closely observe a surgical
operation and only perform part of it, rather
than performing the whole operation.
Split
attention
principle
Replace multiple sources of
information, distributed in space
(spatial) or time (temporal), with one
integrated source of information.
Provide students with instructions for
operating a piece of medical equipment just in
time, precisely when they need it, rather than
providing information beforehand.
Modality
principle
Replace a written explanatory text and
another source of visual information
(unimodal) with a spoken explanatory
text and the visual source of
information (multimodal).
Give students spoken explanations when they
study a computer animation of the working of
the digestive tract, rather than giving them
written explanations on screen.
Redundancy
principle
Replace multiple sources of
information that are self-contained
(i.e. they can be understood on their
own) with one source of information.
When providing learners with a diagram of
the flow of blood in the heart, lungs and body,
do not include a verbal description of flow.
27. 27
Table 2. Instructional design principles and strategies. This table provides principles and
examples of cognitive load theory in health science education. (van Merrienboer and Sweller,
2010)
Managing intrinsic load
Simple-to-
complex
strategy
Replace a series of conventional tasks
with tasks that first present only
isolated elements (low element
interactivity) and gradually work up
to the tasks in their full complexity.
Give students tasks that require them to apply
basic physical principles of hydrodynamics,
such as pressure–volume and pressure–flow
relationships, before giving them tasks that
require them to apply a full model of how the
blood flows through the circulatory system.
Low- to
high-fidelity
strategy
Replace a series of conventional tasks
with tasks that are first performed in a
low-fidelity environment (decreased
element interactivity), and then in
increasingly higher-fidelity
environments.
When teaching students medical diagnosis,
start with textual case descriptions, continue
with computer-simulated patients or patients
played by peers, and end with real
patients in an internship in hospital.
Optimizing Germane Load
Variability
principle
Replace a series of tasks with similar
surface features with a series of tasks
that differ from one another on all
dimensions on which tasks differ in
the real world.
When describing a particular clinical
symptom, illustrate it using patients of
different sex, age, physique, medical history
etc.
Contextual
interference
principle
Replace a series of task variants with
low contextual interference with a
series with high contextual
interference.
If students practice different variants of a
particular surgical task, order these variants in
a random rather than a blocked order.
Self-
explanation
principle
Replace separate worked examples or
completion tasks with enriched ones
containing prompts, asking learners to
self-explain the given information.
For students learning to diagnose
malfunctions in the human cardiovascular
system, present an animation of how the heart
works and provide prompts that ask them to
self-explain the underlying mechanisms.