Quantifying the Theory Vs. Programming Disparity using Spectral Bipartivity Analysis and Principal Component Analysis

Some students in the Computer Science and related majors excel very well programming-related assignments, but not equally theoretical assignments (that are programming-based) vice-versa. We refer to this as "Theory vs. Programming Disparity (TPD)". In paper, we propose a spectral bipartivity analysis-based approach quantify TPD metric for any student course based on percentage scores (considered decimal values range of 0 1) involves both programming-based assignments). also principal component analysis (PCA)-based entire class (in scale 100) programming assignments. The partitions set two disjoint sets whose constituents closer each other within relatively more different from across sets. is computed basis Euclidean distance between tuples representing actual numbers vis-a-vis number PCA-based identifies dominating components computes weighted average correlation coefficients these