Paper Info
Title
Mathematical Modelling Based Learning Strategy
Abstract
We present the Mathematical Modelling Learning strategy in which students create a model that will predict behaviour of existing phenomena using real data. In our implementation students create models from atmospheric data and solve them to determine which weather conditions favour high levels of pollutants in the atmosphere of Monterrey metropolitan area in Mexico. To carry out the strategy we structure course topics around this single comprehensive and integrative project. Students follow a procedure consisting of 4 stages. In the first stage they do statistical analysis of the data. In the second stage, students interpolate missing data and project component data to a 2D map of the metro area. In the third stage students create the mathematical models by carrying out curve fitting through least squares technique. In the third stage, students solve the models by finding roots, solving systems of equations, solving differential equations or integrating. The final deliverable is to determine under which weather conditions there can be an environmental situation that put people's health in danger. Analysis of the strategy is presented as well as statistical results.
Year
DOI
Venue
2015
10.1016/j.procs.2015.05.307
INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE, ICCS 2015 COMPUTATIONAL SCIENCE AT THE GATES OF NATURE
Keywords
Field
DocType
Mathematical modelling, Metacognition, Collaborative learning, Kolb's learning cycle
Least squares,Data mining,Collaborative learning,Curve fitting,Computer science,Interpolation,Artificial intelligence,Missing data,Mathematical model,Operations research,Metacognition,Deliverable,Machine learning
Conference
Volume
ISSN
Citations 
51
1877-0509
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
raul ramirezvelarde1224.26
Nia Alexandrov2114.36
Raul Perez-Cazares381.17
Carlos Barba-Jimenez4121.92