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Research Scholar, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, India
Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
Placements at campus level are one of the most crucial events for engineering and computing students; however, institutes use simplistic cutoffs and subjective judgment methods to determine the placement status of their students. In this study, a classification system is proposed by introducing new operational cognitive readiness criteria which include four major components: Memory, Intelligence, Learning Ability, and Soft Skills. An ANN (Artificial Neural Network) is developed using the Kaggle dataset, containing details of 10,000 students including CGPA, Aptitude Scores, Soft Skills Scores, Internship, Project, Training, and Placement Status. ANN is chosen because it can model the non-linear relationship among these cognitive factors. The initial assessment was conducted using the unfiltered data set where the ANN provided 78.00% accuracy and ROC–AUC score of 0.86, which proves that cognitive factors play a partial role in explaining the historical placement outcomes. In order to evaluate the alignment, cognitive readiness filtering is done where historically placed students are excluded whose total cognitive score is below 70 on a 100 scale. The filtering process reduced the number of inconsistent records related to positive placement outcomes with respect to the operational cognitive paradigm to 21.59%. On the filtered ready-to-go cohort, the ANN was able to provide 94.58% accuracy, 0.90 F1 score, and 0.98 ROC-AUC value. Crucially, while the ANN achieved the highest nominal accuracy among tested models, corrected statistical tests confirmed that its performance remains statistically competitive with rather than demonstrably superior to simpler classical machine learning baselines. Kernel SHAP method was applied to interpret the model prediction and revealed Learning Ability and Intelligence as the key features, then Soft Skills and Memory. Overall, the proposed framework provides an interpretable decision-support approach that aligns with institutional placement evaluation processes.
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