Active Learning vs Traditional Lecturing in Introductory Mechanics: A Pooled Pass-Rate Benchmark Under Common Departmental Assessments from a Latin American Institutional Change Initiative

Improving student success in introductory physics remains a persistent challenge despite substantial progress from research-based instructional practices. Evidence from the Latin American context remains limited, where resources for instructional change are often constrained. This study reports a transparent benchmark of student passing outcomes in $\mathit{\text{Elementary}}\mathit{\text{Mechanics}}\mathit{\text{I}}$ at a large public university in México, comparing sections using Active Learning (AL) with those using Traditional Lecturing (TL). The labels AL and TL are operational, referring to section-level implementations by individual instructors rather than standardized protocols. Using aggregated counts from coordinator reports and common departmental assessments – written by a committee independent of instructional modality – we estimated pooled student-level pass probabilities for the first and second midterm exams, the global exam, and the final mark. Modality differences are summarized primarily by the risk difference, $R{D}_a=p_{\mathrm{A}\mathrm{L},a}-p_{\mathrm{T}\mathrm{L},a}$ (percentage points), with uncertainty quantified using Wilson confidence intervals and a Bayesian reference analysis with Jeffreys priors for binomial proportions. Across assessments, pooled pass rates were higher under AL than under TL, with the strongest separation observed for the global exam and the final mark. For these outcomes, the $95\mathrm{\%}$ confidence intervals excluded zero, including under a random-intercept Bayesian model. We emphasize a constrained interpretation: the results provide a student-weighted benchmark of AL as implemented'' versus TL as implemented’’ in this setting, without isolating the causal effect of individual instructional techniques. Implications are discussed for departmental decision-making and feasible next steps in evaluation, including improved student data collection and more robust qualitative analysis.