A Simulation-Based MANOVA of Mathematics, Chemistry, and Physics Scores Across Differentiated, Group, and Individual Learning Methods

Zul Jalali Wal Ikram; Dadang Juandi; Hastri Rosiyanti; Bambang Avip Priatna Martadiputra

doi:10.61255/jupiter.v4i4.1017

Authors

Zul Jalali Wal Ikram Universitas Pendidikan Indonesia
Dadang Juandi Universitas Pendidikan Indonesia
Hastri Rosiyanti Universitas Muhammadiyah Jakarta
Bambang Avip Priatna Martadiputra Universitas Pendidikan Indonesia

DOI:

https://doi.org/10.61255/jupiter.v4i4.1017

Keywords:

Differentiated Instruction, Group Learning, Individual Learning, Academic Performance

Abstract

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.

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