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Beyond Topical Similarities: Semantic Differences and the Challenge of Interdisciplinary Course Integration in Business Analytics Education

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Title: Beyond Topical Similarities: Semantic Differences and the Challenge of Interdisciplinary Course Integration in Business Analytics Education
Language: English
Authors: Rick L. Brattin (ORCID 0000-0003-3451-0140), James R. Grabowski (ORCID 0009-0003-6468-9388), Randall S. Sexton
Source: Journal of Applied Research in Higher Education. 2026 18(5):1503-1516.
Availability: Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight
Peer Reviewed: Y
Page Count: 14
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Business Education, Interdisciplinary Approach, Semantics, Integrated Curriculum, College Curriculum, Data Analysis, Graduate Study, Differences
DOI: 10.1108/JARHE-02-2025-0152
ISSN: 2050-7003
1758-1184
Abstract: Purpose: Business analytics education sits at the intersection of business decision-making, statistical modeling and computational techniques. While accreditation bodies encourage analytics integration into curricula, they provide little guidance on course structuring. Many institutions borrow non-business courses, but their fit within business curricula remains unclear. This study examines whether business and non-business graduate analytics curricula differ beyond topical content and explores implications for interdisciplinary course integration. Design/methodology/approach: This study applies natural language processing (NLP) and machine learning to analyze graduate-level course descriptions from business and non-business analytics programs. BERTopic identifies latent topical structures, while a neural network-based classifier Neural Network Simultaneous Optimization Algorithm (NNSOA) assesses semantic distinctions. Statistical tests determine whether topics disproportionately represent one domain. Findings: Results indicate that business and non-business analytics curricula are semantically distinct, even for topics shared across disciplines, challenging the assumption that non-business courses can easily integrate into business analytics curricula. These distinctions suggest that framing, emphasis and structure shape learner value, making them essential considerations for curriculum design and instructional strategies. The findings underscore challenges in interdisciplinary integration, emphasizing the need for curricular frameworks that maintain both analytical rigor and business relevance. Practical implications: Findings highlight the risks of borrowing courses from non-business disciplines without careful evaluation, emphasizing the need for intentional curricular design to ensure alignment with business education priorities and accreditation expectations. Originality/value: This study is among the first to apply machine learning and NLP to examine structural and semantic differences across data analytics disciplines. Beyond topic-based comparisons, it demonstrates that business analytics education differs in how analytics is framed and taught.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1507665
Database: ERIC
FullText Text:
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  Data: Beyond Topical Similarities: Semantic Differences and the Challenge of Interdisciplinary Course Integration in Business Analytics Education
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  Data: <searchLink fieldCode="AR" term="%22Rick+L%2E+Brattin%22">Rick L. Brattin</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3451-0140">0000-0003-3451-0140</externalLink>)<br /><searchLink fieldCode="AR" term="%22James+R%2E+Grabowski%22">James R. Grabowski</searchLink> (ORCID <externalLink term="https://orcid.org/0009-0003-6468-9388">0009-0003-6468-9388</externalLink>)<br /><searchLink fieldCode="AR" term="%22Randall+S%2E+Sexton%22">Randall S. Sexton</searchLink>
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  Data: Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: emerald@emeraldinsight.com; Web site: http://www.emerald.com/insight
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  Data: Purpose: Business analytics education sits at the intersection of business decision-making, statistical modeling and computational techniques. While accreditation bodies encourage analytics integration into curricula, they provide little guidance on course structuring. Many institutions borrow non-business courses, but their fit within business curricula remains unclear. This study examines whether business and non-business graduate analytics curricula differ beyond topical content and explores implications for interdisciplinary course integration. Design/methodology/approach: This study applies natural language processing (NLP) and machine learning to analyze graduate-level course descriptions from business and non-business analytics programs. BERTopic identifies latent topical structures, while a neural network-based classifier Neural Network Simultaneous Optimization Algorithm (NNSOA) assesses semantic distinctions. Statistical tests determine whether topics disproportionately represent one domain. Findings: Results indicate that business and non-business analytics curricula are semantically distinct, even for topics shared across disciplines, challenging the assumption that non-business courses can easily integrate into business analytics curricula. These distinctions suggest that framing, emphasis and structure shape learner value, making them essential considerations for curriculum design and instructional strategies. The findings underscore challenges in interdisciplinary integration, emphasizing the need for curricular frameworks that maintain both analytical rigor and business relevance. Practical implications: Findings highlight the risks of borrowing courses from non-business disciplines without careful evaluation, emphasizing the need for intentional curricular design to ensure alignment with business education priorities and accreditation expectations. Originality/value: This study is among the first to apply machine learning and NLP to examine structural and semantic differences across data analytics disciplines. Beyond topic-based comparisons, it demonstrates that business analytics education differs in how analytics is framed and taught.
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