Educational admission systems, particularly in developing countries, often suffer from opaque decision processes, unstructured data, and limited analytic insight. This study proposes a novel methodology that integrates structural causal models (SCMs), ontological modeling, and machine learning to uncover and apply interpretable knowledge from an admission database. Using a dataset of 12,043 records from Benue State Polytechnic, Nigeria, we demonstrate this approach as a proof of concept by constructing a domain-specific SCM ontology, validate it using conditional independence testing (CIT), and extract features for predictive modeling. Five classifiers, Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM) were evaluated using stratified 10-fold cross-validation. SVM and KNN achieved the highest classification accuracy (92%), with precision and recall scores exceeding 95% and 100%, respectively. Feature importance analysis revealed ‘mode of entry’ and ‘current qualification’ as key causal factors influencing admission decisions. This framework provides a reproducible pipeline that combines semantic representation and empirical validation, offering actionable insights for institutional decision-makers. Comparative benchmarking, ethical considerations, and model calibration are integrated to enhance methodological transparency. Limitations, including reliance on single-institution data, are acknowledged, and directions for generalizability and explainable AI are proposed.

The review of literature reveals cybercrime as one of the fastest-growing global security threats, and in emerging economies like Nigeria, the evolution is intricately configured to the rapid growth of internet accessibility, weak regulatory policies, and socio-economic factors. This review analyses the problem of cybercrime in Nigeria with empirical evidence drawn from a case study that attempts to illustrate the complicated growth pattern of cyberspace in Nigeria. The analysis covers three distinct evolution phases: the initial phase of internet penetration through cybercafés (2000 to 2001) and the proliferation of mobile and GSM technology (2001 to 2011), both precursors to the broadband-enabled digital era, which presumably spans 2011 and onwards. These shifts correspond to changes in the level of sophistication of cybercrime which evolved from simple email hoaxes to identity theft and advanced fraud schemes. While adopting these technologies, other issues such as lack of adequate public awareness, regulatory policies, and limited capacity of cybersecurity approaches are equally pressing. Based on these findings, this research introduced the PRAP (Proactive, Reactive, Active, Persuasive)Framework, which can better contextualize Nigeria's cybersecurity needs. The holistic PRAP framework addresses prevention, incident response, threat assessment, prosecution and adapts to Nigeria's social and technical context. This policy is crucial for public and private sector stakeholders concerned with the fight against cybercrime in developing countries.