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  • 标题:A Data Science Enhanced Framework for Applied and Computational Math
  • 本地全文:下载
  • 作者:Kirby McMaster ; Samuel Sambasivam ; Brian Rague
  • 期刊名称:Issues in Informing Science and Information Technology
  • 印刷版ISSN:1547-5840
  • 电子版ISSN:1547-5867
  • 出版年度:2018
  • 卷号:15
  • 页码:191-206
  • DOI:10.28945/4032
  • 语种:English
  • 出版社:Informing Science Institute
  • 摘要: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.
  • 关键词:framework; applied math; computational math; data science; concordance
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