Analyzing and Validating Employability Factors and Predictive Models for Computer Science Graduates: A Scientometric and Systematic Review
Keywords:
CS graduate, prime factors, factor identification, factor validation, employability prediction, scientometric analysis, SLRAbstract
Promoting student employability stands as a central objective for educational institutions, often serving as a barometer of their effectiveness. However, the landscape of the job market is undergoing rapid transformation, driven by forces such as globalization, automation, and the rise of artificial intelligence (AI). In this study, we use scientometric analysis and a systematic literature review (SLR) to delve into recent trends and future trajectories within the realm of identifying, validating, and constructing predictive models for employability factors about computer science (CS) graduates. Our research encompasses 592 pertinent studies published between 2010 and 2023, sourced from Scopus, a pivotal academic database. Our SLR offers invaluable insights into the prevailing validation and predictive models for employability among CS graduates. Guided by our SLR, we propose that forthcoming research should explore the potential of innovative AI techniques to pinpoint key factors and elevate the precision of predictive models geared toward computer science graduates' employability.
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