Exploring Engineering Employment Trends: A Decade-long Deep Dive into Skills and Competences Included in Job Advertisements

Abstract

My dissertation explores how Natural Language Processing (NLP) can support job advertisement discipline classification to help workforce researchers in analyzing labor market trends and relate it back to higher education. In particular, this study investigates how NLP can be used to identify discipline-specific and education-level skill demands from pre-classified large-scale online job advertisements form Burning Glass Technologies. Although engineering education has made long steps in preparing students with foundational knowledge, employers continue to report a misalignment between the skills students acquire in school and the skills needed in practice. A key challenge in addressing this issue is the effective interpretation of semi-structured labor market data such as online job postings, which contain rich but inconsistently labeled skill information. To address this, I developed an NLP classification system that applies pattern-based text classification and flexible regular expression (regex) matching to identify relevant engineering job postings across Civil (CE), Electrical (EE), and Mechanical (ME) Engineering. The classification framework leverages a dictionary of O*NET job title terms and engineering-specific vocabulary to refine the labeling of jobs originally mapped using Standard Occupational Classification (SOC) codes. To validate the classification accuracy, I evaluated results using confusion matrix metrics (accuracy, precision, recall, F1-score) and performed manual spot-checking of 100 job ads from each discipline. The final classification system achieved high F1-scores across CE (94.2%), EE (91.7%), and ME (93.0%), showing strong alignment with human-judged classifications. This step was essential to ensure accurate discipline-specific labeling for subsequent skill demand analysis. Guided by the SABER-Workforce Development (SABER-WfD) framework, the study then addresses two additional research questions. The second research question examines how skill demands differ by engineering discipline and by degree level (bachelor's, master's, doctoral). Using skill mention proportions and statistical analyses such as ANOVA and Cohen's d, the study reveals that foundational technical skills like Drafting and Engineering Design, CAD, and Microsoft Office tools are dominant across all three disciplines at the bachelor's level. At the graduate level, postings increasingly emphasize management-oriented competencies such as Project Management, Budgeting, and Scheduling, particularly in civil and mechanical engineering. EE showed a higher graduate-level demand for specialized tools like MATLAB, Python, and Simulation. The third research question explores how skill requirements have changed over time from 2010 to 2022. Longitudinal analysis shows a growing emphasis on digital and programming tools (e.g., Python, MATLAB) across all disciplines, especially at the graduate level. Simultaneously, demand for traditional skills such as Drafting, Project Management, and Engineering Design has remained steady or increased, signaling that core engineering competencies remain essential. These time-based trends highlight the dual importance of technical depth and managerial fluency in modern engineering roles. This study demonstrates the potential of NLP-based classification and analysis techniques to extract meaningful trends from complex labor market datasets. In doing so, my dissertation contributes to ongoing discussions about curriculum reform by providing a replicable framework for aligning engineering education with workforce needs. The methodology introduced in this study also offers guidance for researchers and institutional stakeholders aiming to apply NLP in large-scale skill demand analysis, thereby expanding access to labor market insights that support engineering workforce development.