Author: David L

  • Privacy Policy

    Your privacy is very important to us. This privacy statement provides information about the personal information that Learning-Theories.com collects, and the ways in which we use that personal information.

    Collection of Personal Information

    Learning-Theories.com may collect and use the following kinds of personal information: information about your use of this website, including usage statistics, geographic location, demographic details; information that you provide using for the purpose of registering with the website (including your name, email address, and other supplied information); information about transactions carried out over this website (including your credit card and other financial details); information that you provide for the purpose of subscribing to the website services;

    Using Personal Information

    Learning-Theories.com may use your personal information to administer this website; personalize the website for you and create a more customized experience; enable your access to and use of the website services; send to you products that you purchase; supply to you services that you purchase; send to you statements and invoices; collect payments from you; and send you marketing communications.

    Where Learning-Theories.com discloses your personal information to its agents or sub-contractors for these purposes, the agent or sub-contractor in question will be obligated to use that personal information in accordance with the terms of this privacy statement.

    In addition to the disclosures reasonably necessary for the purposes identified elsewhere above, Learning-Theories.com may disclose your personal information to the extent that it is required to do so by law, in connection with any legal proceedings or prospective legal proceedings, and in order to establish, exercise or defend its legal rights.

    Securing Your Data

    We assert that Learning-Theories.com will take reasonable technical and organizational precautions to prevent the loss, misuse or alteration of your personal information.  We will store all the personal information you provide on secure servers; information relating to electronic transactions entered into via this website will be protected by encryption technology.

    Updates

    At any time, Learning-Theories.com may update this privacy policy by posting a new version on this website. You should check this page occasionally to ensure you are familiar with any changes.

    Other Websites

    This website contains links to other websites that are not affiliated with Learning-Theories.com.  Learning-Theories.com is not responsible for the privacy policies or practices of any third party.

     

     

  • Terms of Service

    Terms of Service 

    This website is provided “as is” without any representations or warranties, express or implied.  Learning-Theories.com makes no representations or warranties in relation to this website or the information and materials provided on this website.

    Without prejudice to the generality of the foregoing paragraph, Learning-Theories.com does not warrant that:

    • this website will be constantly available, or available at all; or
    • the information on this website is complete, true, accurate or non-misleading.

    No content on this website constitutes, or is meant to constitute, advice of any kind.

    Limitations of liability

    Learning-Theories.com will not be liable to you (whether under the law of contract, the law of torts or otherwise) in relation to the contents of, or use of, or otherwise in connection with, this website or any associated content.

    • for any indirect, special or consequential loss; or
    • for any business losses, loss of revenue, income, profits or anticipated savings, loss of contracts or business relationships, loss of reputation or goodwill, or loss or corruption of information or data.

    These limitations of liability apply even if Learning-Theories.com has been expressly advised of the potential loss.

     

    Reasonableness

    By using this website, you agree that the exclusions and limitations of liability set out in this website disclaimer are reasonable.  If you do not think they are reasonable, you must not use this website.

    Unenforceable provisions

    If any provision of this website disclaimer is, or is found to be, unenforceable under applicable law, that will not affect the enforceability of the other provisions of this website disclaimer.

    Copyrighted material

    All material on this website are copyrighted material and may not be reproduced, sold, modified or used for commercial purposes without the expressed written consent of Learning-Theories.com.  Our website occasionally accepts submissions from individuals.  Learning-Theories.com is not held responsible for the viewpoints or content of these published submissions.

    Learning-Theories.com reserves the right to add, delete, change, or modify these Terms and Conditions at any time, without any warning.

    By using this website, you agree to the above terms of conditions in this page.

  • Game Reward Systems

    Game Reward Systems

    Summary: The phrase game reward systems describes the structure of rewards and incentives in a game that inspire intrinsic motivation in the player while also offering extrinsic rewards. Game reward systems can be modeled in non-game environments, including personal and business environments, to provide positive motivation for individuals to change their behavior.

    Originators and Key Contributors: Many theories on intrinsic motivation, sense of satisfaction, and other reward concepts have been developed that form the foundation for current thinking about game reward systems. In the 1930s, B. F. Skinner explored reward schedules with pigeons, and his findings have influenced the design of reward mechanisms both inside and outside of the field of game mechanics. In their paper Game Reward Systems: Gaming Experiences and Social Meanings (2011), Hao Wang and Chuen-Tsai Sun analyze the main structural features of reward systems within videogames that have relevance outside videogames as well[1].

    Keywords: game, variable ratio, fixed ratio, reward, intrinsic motivation, extrinsic motivation

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  • Cognitive Dissonance (Festinger)

    Cognitive Dissonance (Festinger)

    Summary: Cognitive dissonance is the negative feeling that results from conflicting beliefs and behaviors.

    Originator: Leon Festinger (1919-1989), American social psychologist

    Keywords: social psychology, forced compliance, decision-making, error justification

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  • Expertise Theory (Ericsson, Gladwell)

    Expertise Theory (Ericsson, Gladwell)

    Expertise theory specifies how talent develops across specified fields or domains, focusing on cognitive task analysis (to map the domain), instruction and practice, and clearly specified learning outcomes against which one can objectively measure the development of expertise.

    Anders Ericsson, a professor at Florida State University, is the leading figure in the field of expertise theory. However, many others are associated with it as well: Robert Sternberg (Cornell University), Richard Clark (University of Southern California), Benjamin Bloom (late of the University of Chicago), Herbert Simon (late of Carnegie Mellon University), and Mihaly Csikszentmihalyi (Claremont Graduate University). Another notable figure is Malcolm Gladwell, whose work has served to popularize the theory.

    Keywords: expertise, practice, instruction, cognitive task analysis

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  • Cognitive Tools Theory (Egan)

    Cognitive Tools Theory (Egan)

    Summary: There exist five kinds of understanding (or cognitive tools) that individuals usually master in a particular order during the course of their development; these have important educational implications.

    Originator: Kieran Egan, a Professor at Simon Fraser University, proposed his theory of cognitive tools as part of a sustained program of writing and research on the role of imagination in learning, teaching, and curriculum.

    Keywords: Cognitive, Stages, Imagination, Ironic, Literacy, Memes

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  • Self-Perception Theory (Bem)

    Self-Perception Theory (Bem)

    Summary: Self-perception theory describes the process in which people, lacking initial attitudes or emotional responses, develop them by observing their own behavior and coming to conclusions as to what attitudes must have driven that behavior.

    Originators and Key Contributors:  Psychologist Daryl Bem originally developed this theory of attitude formation in the late 1960’s and early 1970’s.

    Keywords: identity, perception, behavior, attitude, marketing, therapy

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  • Online Collaborative Learning Theory (Harasim)

    Online Collaborative Learning Theory (Harasim)

    Summary: Online collaborative learning theory, or OCL, is a form of constructivist teaching that takes the form of instructor-led group learning online. In OCL, students are encouraged to collaboratively solve problems through discourse instead of memorizing correct answers. The teacher plays a crucial role as a facilitator as well as a member of the knowledge community under study.

    Originators and Key Contributors:

    Linda Harasim, professor at the School of Communication at Simon Fraser University in Vancouver, developed online collaborative learning theory (OCL) in 2012[1]from a theory originally called computer-mediated communication (CMC), or networked learning[2][3][4].

    Keywords: collaborative learning, internet, virtual classroom, e-learning, discourse, constructivism

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  • E-Learning Theory (Mayer, Sweller, Moreno)

    E-Learning Theory (Mayer, Sweller, Moreno)

    E-learning theory consists of cognitive science principles that describe how electronic educational technology can be used and designed to promote effective learning.

    History

    The researchers started from an understanding of cognitive load theory to establish the set of principles that compose e-learning theory. Cognitive load theory refers to the amount of mental effort involved in working memory, and these amounts are categorized into three categories: germane, intrinsic, and extraneous[1].

    Germane cognitive load describes the effort involved in understanding a task and accessing it or storing it in long-term memory (for example, seeing an essay topic and understanding what you are being asked to write about). Intrinsic cognitive load refers to effort involved in performing the task itself (actually writing the essay). Extraneous cognitive load is any effort imposed by the way that the task is delivered (having to find the correct essay topic on a page full of essay topics).

    Key Concepts

    Mayer, Moreno, Sweller, and their colleagues established e-learning design principles that are focused on minimizing extraneous cognitive load and introducing germane and intrinsic loads at user-appropriate levels[2][3][4][5][6]. These include the following empirically established principles:

    Multimedia principle (also called the Multimedia Effect)

    Using any two out of the combination of audio, visuals, and text promote deeper learning than using just one or all three.

    Modality principle

    Learning is more effective when visuals are accompanied by audio narration versus onscreen text. There are exceptions for when the learner is familiar with the content, is not a native speaker of the narration language, or when printed words are the only things presented on screen. Another exception to this is when the learner needs to use the material as reference and will be going back to the presentation repeatedly.

    Coherence principle

    The less that learners know about the presentation content, the more they will be distracted by unrelated content. Irrelevant video, music, graphics, etc. should be cut out to reduce cognitive load that might happen through learning unnecessary content. Learners with some prior knowledge, however, might have increased motivation and interest with unrelated content.

    Contiguity principle

    Learning is more effective when relevant information is presented closely together. Relevant text should be placed close to graphics, and feedback and responses should come closely to any answers that the learner gives.

    Segmenting principle

    More effective learning happens when learning is segmented into smaller chunks. Breaking down long lessons and passages into shorter ones helps promote deeper learning.

    Signaling principle

    Using arrows or circles, highlighting, and pausing in speech are all effective methods of signaling important aspects of the lesson. It is also effective to end a lesson segment after releasing important information.

    Learner control principle

    For most learners, being able to control the rate at which they learn helps them learn more effectively. Having just play and pause buttons can help more than having an array of controls (back, forward, play, pause). Advanced learners may benefit from having the lesson play automatically with the ability to pause when they choose.

    Personalization principle

    A tone that is more informal and conversational, conveying more of a social presence, helps promote deeper learning. Beginning learners may benefit from a more polite tone of voice, while learners with prior knowledge may benefit from a more direct tone of voice. Computer characters can help reinforce content by narrating the lesson, pointing out important features, or illustrating examples for the learner.

    Pre-training principle

    Introducing key content concepts and vocabulary before the lesson can aid deeper learning. This principle seems to apply more to low prior knowledge learners versus high prior knowledge learners.

    Redundancy principle

    Having graphics explained by both audio narration and on-screen text creates redundancy. The most effective method is to use either audio narration or on-screen text to accompany visuals.

    Expertise effect

    Instructional methods that are helpful to low prior knowledge learners may not be helpful at all, or may even be detrimental, to high prior knowledge learners.

    Additional Resources and References

    Resources

    References

    1. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational psychologist, 38(1), 43-52.
    2. Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions?.Educational psychologist, 32(1), 1-19.
    3. Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309-326.
    4. Low, R., & Sweller, J. (2005). The modality principle in multimedia learning.The Cambridge handbook of multimedia learning, 147, 158.
    5. Mayer, R. E. (2003). Elements of a science of e-learning. Journal of Educational Computing Research, 29(3), 297-313.
    6. Clark, R. C., & Mayer, R. E. (2016). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. John Wiley & Sons.
  • Online Disinhibition Effect (Suler)

    Online Disinhibition Effect (Suler)

    Summary: The online disinhibition effect describes the loosening of social restrictions and inhibitions that are normally present in face-to-face interactions that takes place in interactions on the Internet.

    Originators and Key Contributors: In 2004, John Suler, professor of psychology at Rider University, published an article titled “The Online Disinhibition Effect,” which analyzed characteristics of internet interactions that contributed to this effect[1]. The term “online disinhibition effect” was already in use at the time.

    Keywords: online, internet, anonymity, invisibility, imagination, disinhibition

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