Journal of Enterprise and Business Intelligence


A Quantitative Analysis of Hard and Soft Academic Fields to Determine Cross Disciplinary Differences



Journal of Enterprise and Business Intelligence

Received On : 10 May 2023

Revised On : 10 July 2023

Accepted On : 25 August 2023

Published On : 05 April 2024

Volume 04, Issue 02

Pages : 061-072


Abstract


Disciplinary differences between academic fields refer to those differences in teaching approaches, course design, and instructional tools but often considered “hard” sciences like physics and engineering, and “soft” sciences such as social sciences and humanities. This study seeks to explore these differences through a quantitative analysis of how course design and teaching tools differ across hard and soft academic disciplines. The research employs a survey-based approach to obtaining data from 240 academics from a number of institutions and disciplines. Statistical tests such as t-tests and regression analysis were used to identity and test the difference between the disciplines. The results found that instructors of hard sciences favor the delivery of structured content, emphasize technical skills, standardized assessments, and the use of digital simulation tools. On the other hand, soft sciences integrate collaborative projects, discussion based learning, and experiential methods like case studies for the most part. The findings also showed a significant variation in how technology gets adopted; where hard fields prefer software and digital tools that help with precise measurements and quantitative data analysis, while soft fields tend to prefer platforms that allow for discussion and qualitative analysis.


Keywords


Business and Management Education, Hard/Soft Academic Fields, Cross-Disciplinary Differences, E-Learning Projects, Course Modes, Teaching Tools, Teaching or Pedagogical Methods.


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Cite this article


Dong Diane E. Davis, “A Quantitative Analysis of Hard and Soft Academic Fields to Determine Cross Disciplinary Differences”, Journal of Enterprise and Business Intelligence, vol.4, no.2, pp. 061-072, April 2024. doi: 10.53759/5181/JEBI202404007.


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© 2024 Dong Diane E. Davis. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.