An interactive simulation tool for student centred concept exploration

2002 ◽  
Author(s):  
J.G. Weir ◽  
A.E. Samuel
Keyword(s):  
Interactive Simulation ◽  
Simulation Tool ◽  
Concept Exploration

An Interactive Simulation Tool for the Study of Multirate Sampled Data Systems

2003 ◽  
Vol 36 (10) ◽  
pp. 29-34
Author(s):  
J. Salt ◽  
P. Albertost ◽  
A. Cuenca ◽  
S. Dormid
Keyword(s):  
Interactive Simulation ◽  
Simulation Tool ◽  
Data Systems ◽  
Sampled Data

scholarly journals Application of an Online Interactive Simulation Tool to Teach Engineering Concepts Using 3D Spatial Structures

2016 ◽  
pp. 788-806
Author(s):  
Brett D. Jones ◽  
Mehdi Setareh ◽  
Nicholas F. Polys ◽  
Felipe Bacim
Keyword(s):  
Effective Means ◽  
Interactive Simulation ◽  
Simulation Tool ◽  
Spatial Structures ◽  
Learning Tools ◽  
Powerful Learning ◽  
Inquiry Approach ◽  
Based Instruction ◽  
The Impact ◽  
Interactive Simulations

Simulations can be powerful learning tools that allow students to explore and understand concepts in ways that are not possible in typical classroom settings. However, research is lacking as to how to use simulations most effectively in different types of learning environments. To address this need, we designed a study to examine the impact of using online interactive simulations on the learning and motivation of 109 undergraduate architecture students from two large public universities. The simulation tool allowed students to create models of spatial structures and analyze the effects of loads on structural member forces and deflections. The authors incorporated the simulations into our instructional design using an inquiry approach because it was consistent with our goals of teaching students concepts and the process of deriving the concepts. They documented that online interactive simulations delivered through inquiry-based instruction can be an effective means to help students learn and apply concepts.

An Interactive Simulation Tool for the study of Multirate Systems

2013 ◽  
Vol 46 (17) ◽  
pp. 49-54
Author(s):  
J. Salt ◽  
S. Dormido ◽  
A. Cuenca ◽  
F. Palau
Keyword(s):  
Interactive Simulation ◽  
Simulation Tool ◽  
Multirate Systems

scholarly journals Learning in an Interactive Simulation Tool against Landslide Risks: The Role of Amount and Availability of Experiential Feedback

2017 ◽  
Author(s):  
Pratik Chaturvedi ◽  
Akshit Arora ◽  
Varun Dutt
Keyword(s):  
Decision Making ◽  
Risk Communication ◽  
Present Condition ◽  
Landslide Risk ◽  
Interactive Simulation ◽  
Simulation Tool ◽  
Simulation Tools

Abstract. To investigate how differing amounts of experiential feedback and feedback’s availability in an interactive simulation tool influences people’s decision-making against landslide risks. Feedback via simulation tools is likely to help people improve their decisions against disasters; however, currently little is known on how differing amounts of experiential feedback and feedback’s availability in simulation tools influences people’s decisions against landslides. We tested the influence of differing amounts of experiential feedback and feedback’s availability on people’s decisions against landslide risks in an Interactive Landslide Simulation (ILS) tool. In an experiment, in high-damage conditions, the probabilities of damages to life and property due to landslides were 10-times higher than those in the low-damage conditions. In feedback-present condition, experiential feedback was provided in numeric, text, and graphical formats in ILS. In feedback-absent conditions, the probabilities of damages were described; however, there was no experiential feedback present. Investments were greater in conditions where experiential feedback was present and damages were high compared to conditions where experiential feedback was absent and damages were low. Furthermore, only high-damage feedback produced learning in ILS. Experience gained in ILS enables people to improve their decision-making against landslide risks. Simulation tools seem appropriate for landslide risk communication and for performing what-if analyses.

scholarly journals A Multirate Control Strategy to the Slow Sensors Problem: An Interactive Simulation Tool for Controller Assisted Design

Sensors ◽  
2014 ◽  
Vol 14 (3) ◽  
pp. 4086-4110 ◽  
Author(s):  
Julián Salt ◽  
Ángel Cuenca ◽  
Francisco Palau ◽  
Sebastián Dormido
Keyword(s):  
Control Strategy ◽  
Interactive Simulation ◽  
Simulation Tool ◽  
Multirate Control

A Bayesian Belief Network Approach for Modeling Complex Domains

2007 ◽  
pp. 13-41 ◽  
Author(s):  
Ben K. Daniel ◽  
Juan-Diego Zapata-Rivera ◽  
Gordon I. McCalla
Keyword(s):  
Graphical Models ◽  
Bayesian Model ◽  
Computational Models ◽  
Knowledge Engineering ◽  
Social Systems ◽  
Bayesian Belief Network ◽  
Bayesian Belief Networks ◽  
Interactive Simulation ◽  
Simulation Tool ◽  
Engineering Effort

Bayesian belief networks (BBNs) are increasingly used for understanding and simulating computational models in many domains. Though BBN techniques are elegant ways of capturing uncertainties, knowledge engineering effort required to create and initialize the network has prevented many researchers from using them. Even though the structure of the network and its conditional & initial probabilities could be learned from data, data is not always available or it is too costly to obtain. In addition, current algorithms that can be used to learn relationships among variables, initial and conditional probabilities from data are often complex and cumbersome to employ. Qualitative-based approaches applied to the creation of graphical models can be used to create initial computational models that can help researchers analyze complex problems and provide guidance and support for decision-making. Initial BBN models can be refined once appropriate data is obtained. This chapter extends the use of BBNs to help experts make sense of complex social systems (e.g., social capital in virtual learning communities) using a Bayesian model as an interactive simulation tool. Scenarios are used to find out whether the model is consistent with the expert’s beliefs. The sensitivity analysis was conducted to help explain how the model reacted to different sets of evidence. Currently, we are in the process of refining the initial probability values presented in the model using empirical data and developing more authentic scenarios to further validate the model.

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