Assessment of Long-Term Learning Through Item Response Theory in Moodle and H5P Virtual Environments: A Case Study of Leveling Students at the Escuela Politécnica Nacional

This study integrated Item Response Theory (IRT) models with ordinal survey instruments to assess academic performance trajectories and identify multidimensional factors associated with academic achievement among first-semester leveling students (N=1,558 pre-test; N=1,676 post-test) at the Escuela Politécnica Nacional, Ecuador. A dual-component methodology was employed: (1) an 80-item ordinal survey measuring eight latent constructs (socioeconomic, academic, motivational, vocational, social integration, psychological/emotional, institutional, and biological/health factors), validated through Confirmatory Factor Analysis (CFI > 0.95, RMSEA < 0.06); and (2) structured diagnostic assessments in mathematics, physics, chemistry, geometry, and language, calibrated using three-parameter logistic (3PL) IRT models via Expected A Posteriori (EAP) estimation. Results demonstrated high internal consistency (r = 0.93 between IRT and raw scores), with mean IRT-scaled ability θ ̅ = 10.45 (SD = 3.51) on a 1–20 scale. Item parameters indicated adequate discrimination a ̅ = 1.92) and centered difficulty (b ̅ = 0.05), though 13.75% of items exhibited poor model fit (S-X² p < 0.01), concentrated in physics and chemistry domains. Factorial scores and performance outcomes were statistically contrasted against 24 categorical demographic variables, revealing differential performance patterns across student subgroups. This research provides validated psychometric instruments, reproducible IRT-LMS integration protocols, and empirical evidence supporting targeted interventions to strengthen university transition in resource-constrained contexts.