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  1. The Keys to Learning: Unlocking Your Brain's Potential II

    Online Presentations | 09 Feb 2024 | Contributor(s):: Michael Melloch

    There are many things that influence learning that will be discussed. The first is how memory works and the best ways to put things permanently in memory. The role of spacing, interleaving, where you study, and sleep in memory formation are presented.

  2. The Keys to Learning: Unlocking Your Brain's Potential I

    Online Presentations | 22 Jan 2024 | Contributor(s):: Michael Melloch

    There are many things that influence learning that will be discussed. The first is how memory works and the best ways to put things permanently in memory. The role of spacing, interleaving, where you study, and sleep in memory formation are presented.

  3. How to Learn I

    Online Presentations | 21 Oct 2021 | Contributor(s):: Michael Melloch

    Learning is the process of developing mental models. A mental model is a mental representation of some external reality. These mental models should become progressively more complex as we deepen our understanding with study....

  4. Berna Çorak

    https://nanohub.org/members/299993

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

    Presentation Materials | 25 Jun 2015 | 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 scaffolding student-developed simulations in an open-ended learning environment.  The full paper...

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

    Papers | 21 Jul 2014 | 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 concepts. This study is approaching learning with simulations from a different perspective. We are...

  7. Sorin Adam Matei

    https://nanohub.org/members/93108

  8. [Illinois] MCB 493 Lecture 10: Time Series Learning and Nonlinear Signal Processing

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

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

    Online Presentations | 30 Oct 2013 | 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.

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

    Online Presentations | 30 Oct 2013 | 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.

  11. [Illinois] MCB 493 Lecture 11: Temporal-Difference Learning and Reward Prediction

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

    Temporal-difference learning can train neural networks to estimate the future value of a current state and simulate the responses of neurons involved in reward processing.

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

    Online Presentations | 29 Oct 2013 | 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.

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

    Online Presentations | 29 Oct 2013 | 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.

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

    Online Presentations | 29 Oct 2013 | 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.

  15. David Hallman

    https://nanohub.org/members/82815

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

    Online Presentations | 26 Feb 2013 | 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 following research question: What were the intended learning outcomes that guided the instructors'...

  17. Renato Regis

    123

    https://nanohub.org/members/69393

  18. Learning with nanoHUB

    Online Presentations | 01 Aug 2012 | 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 technology into their existing curricula, we developed a short seminar designed to bridge the gap between...

  19. Kamalakkannan Gothandam

    unique preference.

    https://nanohub.org/members/69077

  20. Glenn Carlo Dones Clavel

    Knowledge is proud that he has learned so much; Wisdom is humble that he knows no more. - William Cowper

    https://nanohub.org/members/68728