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Framework for Evaluating Simulations: Analysis of Student Developed Interactive Computer Tool
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...
Framework for Evaluating Simulations: Analysis of Student Developed Interactive Computer Tools
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...
Sorin Adam Matei
[Illinois] MCB 493 Lecture 6: Supervised Learning and Non-Uniform Representations
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.
[Illinois] MCB 493 Lecture 7: Reinforcement Learning and Associative Conditioning
Reinforcement learning algorithms can simulate certain types of associative conditioning and train neural networks to form non-uniform distributed representations.
[Illinois] MCB 493 Lecture 4: Covariation Learning and Auto-Associative Memory
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.
[Illinois] MCB 493 Lecture 5: Unsupervised Learning and Distributed Representations
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.
[Illinois] MCB 493 Lecture 8: Information Transmission and Unsupervised Learning
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.
Engineering and Science Instructors' Intended Learning Outcomes with Computational Simulations as Learning Tools
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'...
Learning with nanoHUB
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...
Glenn Carlo Dones Clavel
Overview of How People Learn Framework to Support Instructional Design
19 Apr 2010 | | 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 on human cognition that has informed the development of pedagogical methods that lead to learning. The...
nanoHUB: Impact On Education
nanoHUB’s Impact on Education
The contributions of nanoHUB.org to learning have been significant. Since its inception nanoHUB has been used in 379 graduate and undergraduate courses taught...