A Data Science Enhanced Framework for Applied and Computational Math

Kirby McMaster, Samuel Sambasivam, Brian Rague, Stuart L Wolthuis
Issues in Informing Science and Information Technology  •  Volume 15  •  2018  •  pp. 191-206
Aim/Purpose: The primary objective of this research is to build an enhanced framework for Applied and Computational Math. This framework allows a variety of applied math concepts to be organized into a meaningful whole.

Background: The framework can help students grasp new mathematical applications by comparing them to a common reference model.

Methodology: In this research, we measure the most frequent words used in a sample of Math and Computer Science books. We combine these words with those obtained in an earlier study, from which we constructed our original Computational Math scale.

Contribution: The enhanced framework improves the Computational Math scale by integrating selected concepts from the field of Data Science.

Findings: The resulting enhanced framework better explains how abstract mathematical models and algorithms are tied to real world applications and computer implementations.

Future Research: We want to empirically test our enhanced Applied and Computational Math framework in a classroom setting. Our goal is to measure how effective the use of this framework is in improving students’ understanding of newly introduced Math concepts.
framework, applied math, computational math, data science, concordance
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