<![CDATA[Purpose: This study aims to evaluate multivariate statistical sensitivity in detecting academic performance differences across three instructional methods: Differentiated Instruction, Group Learning, and Individual Learning in Mathematics, Chemistry, and Physics. Methods: A structured synthetic dataset containing 100 simulated student records was generated using a stratified Monte Carlo distribution to reflect balanced instructional groups (Differentiated Instruction: , Group Learning: , Individual Learning: ). Data analysis employed a one-way Multivariate Analysis of Variance (MANOVA) to assess simultaneous score variations across the three subjects. Findings: The overall multivariate model revealed significant differences across instructional groups, Pillai's Trace . Univariate tests demonstrated significant effects for Chemistry, , and Physics, , whereas Mathematics showed no significant effect, . Games-Howell post-hoc tests confirmed that Group Learning yielded higher simulated scores than Individual Learning in Chemistry () and Physics (). Research Implications: These simulation patterns demonstrate how subject-specific characteristics interact with group-based learning modalities, highlighting the necessity for future empirical classroom trials to validate these statistical trends. Originality: This study provides a controlled simulation framework illustrating that the theoretical efficacy of instructional techniques varies according to the domain-specific demands of STEM subjects.]]>
Copyrights © 2026
