Aligning Academic Curricula with Job Market Demands: A Data-Driven Analysis
The job market is changing rapidly, creating the need for an interface between industrial standards and an educational framework to allow graduates to be equipped with the basic skills necessary for assuming their roles in their careers. This study is aimed at aligning undergraduate curricula with needs for jobs in demand, related to Fourth Industrial Revolution (4IR) and emerging technologies. Based on the analysis of hiring trends data, such as job titles, required skills, and qualifications, this study emphasizes key electives and courses in Computer Science that may widen employed traits of graduates. Our analysis incorporated data extraction techniques to obtain job-listing information embedded in emails from job hiring platforms, followed by focused scraping of data from hyperlinks. The dataset spans four to six months of communication regarding hiring. Our analysis is based on job-hiring updates; whereas, the data on elective subjects enables further examination of the relationship of industry needs and courses offering. The study examines the extent to which academic courses are preparing students for deep technologies and 4IR demands, which are increasing alarmingly. This study thus establishes the rationale for the incorporation of the very cutting edge technologies like augmented reality, blockchain, quantum computing, AI, and machine learning in the curriculum. It recommends a flexible dynamic curriculum geared towards new market trends to ensure that academic programs do not fall to complacency and can accommodate the changing demands of the global labor market. Researchers would identify divergency issues between the extant school curriculum and the enormous needs for specialized skills in some emerging technologies and set out a host of recommendations for educators, employers, and policymakers in order to work toward better preparing educational programs in meeting the demands of the future workforce. The dataset is available from the conference website or by contacting researchers and formally acknowledging its use.
Authors
- Sanasam Surendra Singh (ORCID: https://orcid.org/0000-0002-3004-4499)
- Sumit Goswami (ORCID: https://orcid.org/0000-0002-3158-6505)
- Nishtha Singh
Institutions
- Defence Research and Development Organisation (IN)
- Institute of Management Technology (IN)
- Shobhit University (IN)
Publication Details
- Published
- 2025-02-21
- DOI
- https://doi.org/10.1109/mpseciceta64837.2025.11118466
- Citations
- 1
- Primary Topic
- Online Learning and Analytics
- Type
- article
- Field-Weighted Citation Impact
- 1.69