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Tags: learning

Resources (1-10 of 10)

  1. Framework for Evaluating Simulations: Analysis of Student Developed Interactive Computer Tool

    25 Jun 2015 | Presentation Materials | Contributor(s): Kelsey Joy Rodgers, Heidi A Diefes-Dux, Yi Kong, Krishna Madhavan

    This is the presentation for a paper presented at the 2015 annual American Society of Engineering Education (ASEE) conference. The paper discusses a developed framework for evaluating and...

    https://nanohub.org/resources/22482

  2. Framework for Evaluating Simulations: Analysis of Student Developed Interactive Computer Tools

    21 Jul 2014 | Papers | Contributor(s): Kelsey Joy Rodgers, Heidi A Diefes-Dux, Krishna Madhavan

    Computer simulations are discussed in the learning environment from two major perspectives: 1) teaching students how to build simulations and 2) developing simulations to teach students targeted...

    https://nanohub.org/resources/21266

  3. [Illinois] MCB 493 Lecture 6: Supervised Learning and Non-Uniform Representations

    30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    Supervised learning algorithms can train neural networks to associate patterns and simulate the non-uniform distributed representations found in many brain regions.

    https://nanohub.org/resources/17022

  4. [Illinois] MCB 493 Lecture 7: Reinforcement Learning and Associative Conditioning

    30 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    Reinforcement learning algorithms can simulate certain types of associative conditioning and train neural networks to form non-uniform distributed representations.

    https://nanohub.org/resources/18832

  5. [Illinois] MCB 493 Lecture 4: Covariation Learning and Auto-Associative Memory

    29 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    Networks with recurrent connection weights that reflect the covariation between pattern elements can dynamically recall patterns and simulate certain forms of memory.

    https://nanohub.org/resources/16950

  6. [Illinois] MCB 493 Lecture 5: Unsupervised Learning and Distributed Representations

    29 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    Unsupervised learning algorithms, given only a set of input patterns, can train neural networks to form distributed representations of those patterns that resemble brain maps.

    https://nanohub.org/resources/16951

  7. [Illinois] MCB 493 Lecture 8: Information Transmission and Unsupervised Learning

    29 Oct 2013 | Online Presentations | Contributor(s): Thomas J. Anastasio

    Unsupervised learning algorithms can train neural networks to increase the amount of information they contain about their inputs and simulate the properties of sensory neurons.

    https://nanohub.org/resources/18833

  8. Engineering and Science Instructors' Intended Learning Outcomes with Computational Simulations as Learning Tools

    26 Feb 2013 | Online Presentations | Contributor(s): Alejandra J. Magana

    This presentation describes the results of a study aiming to identify how 14 instructors incorporated into their classrooms computational simulations as learning tools. The study was based on the...

    https://nanohub.org/resources/16592

  9. Learning with nanoHUB

    01 Aug 2012 | Online Presentations | Contributor(s): Quincy Leon Williams

    Interactive media is the most valuable tool for engaging the younger generations of students and future researchers. Since, few instructors have the skills required to incorporate such new...

    https://nanohub.org/resources/14886

  10. Overview of How People Learn Framework to Support Instructional Design

    19 Apr 2010 | Online Presentations | Contributor(s): Sean Brophy

    The National Academy of Sciences commissioned a report on How People Learn which is now being used by a wide range of educators and researchers. The report provides a review of critical research...

    https://nanohub.org/resources/8874

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