A Predictive Framework for Adaptive Resources Allocation and Risk-Adjusted Performance in Engineering Programs
Keywords:
Predictive analytics, Engineering education, Machine learning, Student performance, Academic risk, Resource allocation, Retention improvement, Equity in education, Learning analytics, educational data miningAbstract
In this paper, the main purpose is to discuss the adaptive resource allocation considerations, predictive frameworks of the adaptive resource allocation, and risk-adjusted performance frameworks in the context of engineering education. The analysis of the results of ten scholarly research studies with secondary data shows the determinant effects of previous performance and socio-economic risk factors of academic outcomes. Machine learning models and real-time data are proven to be accurate in predicting student success and identifying at-risk learners. With predictive analytics, adaptive systems result in quantifiable enhancements in the GPA, retention, and rates of institutional resource utilization. This incorporation of identity, motivational and behavioral indicators provide an equitable and individualized intervention. Risk-informed planning helps overcome achievement gaps that disadvantaged groups could face, and predictive dashboards facilitate academic decision-making. As demonstrated by the study, not only predictive frameworks contribute to improved outcomes of individual learning but also to the improved operational planning within engineering programs. Furthermore, these models enable proactive academic counseling, targeted faculty mentoring, and more efficient budget allocation for student services.
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