Cog Sci 1 + Ed Tech (Spring 2014)Weekly Annotations

January 27, 2013 (#1)Foundation and Assumptions of Cognitive Science

Overview of Information Cognitive ProcessingHuman CognitionIntroduction to Instructional DesignFoundations of Instructional Design

February 3, 2014 (#2)Human Cognitive Architecture: Biological Bases of Learning and Memory; Sensory, Short-Term, Working Memory Models

Information ProcessingBiological Basis of Learning and MemorySchema Acquisition and Sources of Cognitive Load.Working MemoryThe 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 LearningLong-term MemoryReturn of the mental image: Are there really pictures in the brain?

February 18 (#4)Meaningful Learning, Schema Theory, Situated Cognition

Meaningful LearningSituated CognitionCognition in the Wild (Optional)

February 25, 2014 (#5)Cognitive Load Theory

Implications of Cognitive Load Theory for Multimedia LearningThe Split-Attention Principle in Multimedia LearningThe Redundancy Principle in Multimedia LearningCognitive Load Theory, Learning Difficulty, and Instructional DesignDirect 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 LoadPrior Knowledge Principle in Multimedia LearningLearning Styles: Concepts and EvidenceAnalyzing 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 TheoryIn Defense of Advance Organizers: A Reply to Critics (Optional)

March 31, 2014 (#9)Reducing Extraneous Processing in Multimedia Learning

Modality PrincipleWorked-Out Examples PrincipleStructuring 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 MicroworldsInteractivity in Multimedia Learning: An integrated ModelRole of Guidance, Reflection, and Interactivity in an Agent-Based Multimedia GameEnhancing 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 LearningThe Broaden-and-Build-Theory of Positive EmotionsEmotional Design in Multimedia LearningSocial 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 RealityDesign Factors for Educationally Effective Animations and SimulationsDesign Factors for Effective Science Simulations: Representation of InformationInstructional Animation Versus Static Pictures: A Meta- Analysis

April 28, 2014Cognitive Development: Stages of Development (Piaget), Interactionist Theories (Bruner, Vygotsky)

Cognitive and Knowledge DevelopmentInteractional 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 DesignSmith & 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 DesignSmith & 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, logical-

mathematical 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 dual-task 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 long-term 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 TheoryClark, 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 two-dimensional 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 LearningDriscoll, 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 split-attention 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 split-attention 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 DesignSweller, 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 sensory-modality

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 long-

term 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 picture-

text-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 dual-

coding 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 self-regulation 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 self-regulations 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 LearningMayer, 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, “high-knowledge 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 low-structured 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 problem-solving 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 on-screen 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 PrincipleMayer, 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 subgoal-oriented 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 FeedbackMultimedia 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 ModelDomagk, 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 GameMoreno, 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 long-term 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 broaden-and-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 broaden-and-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 SimulationsPlass, 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 controlled-pacing, 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 dynamicvisualizations” (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 InformationPlass, 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’ self-efficacy, 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 meta-analysis. 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 cross-species 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.