Resumo:
The Brazilian National Curriculum Guidelines (DCN) of the Undergraduate Engineering
Courses of 2019 presented new pedagogical demands, one of them being the application of
Active Learning (AL). Scientific studies on AL applied to Engineering courses has grown
significantly in recent years and its results uncovered challenges and opportunities for future
research. Classroom observation instruments designed for AL environments have emerged and
have supported research to objectively assess the behaviors and attitudes that characterize such
environments. However, there was a lack of predictability for Engineering Higher Education
Institutions (EHEIs) regarding learning gains when applying AL techniques within a
challenging process of changing teaching practices. In this context, the objective of this thesis
was to propose a predictive mathematical model that demonstrates the relationship between the
students' degree of learning and the application or not of AL techniques in the classroom
(measured by the level of activity captured by an observation protocol). To achieve this
objective, a strict and systematic methodological process was established, using controlled
experimental research in an EHEI over three years, in two dimensions of analysis. The first one,
intraclass, used a repeated measures experimental design to demonstrate the probable causeeffect relationship in a two-level one-factor approach. In addition, it allowed a qualitative
analysis of AA application in individual courses. The second, interclass, involved independent
class samples in subsequent semesters and used Partial Least Squares Structural Equation
Modeling to test and identify the best predictive model for learning based on the application of
AL. The intraclass results demonstrated, in a positive cause-effect relationship, that the global
average academic performance was 14% better in the post-AL assessment, compared to the
first, without the application of AL techniques, representing 40% of the standard deviation of
the grades. In addition, the individual analysis of performance in each of the courses revealed
the most and least successful strategies and allowed to recommend those most viable AL
strategies for specific groups of courses. In the interclass dimension, the improvement was 10%
and the PLS-SEM predictive model was positively validated by several performance indexes,
demonstrating a significant and non-linear positive relationship between the latent constructs,
with a moderate to high relevance for learning prediction (𝑄2 > 0.344). In the demonstration of
the predictive relevance, the best-fitting curve of the relationship allowed, from an average
score between 0 and 35.97 in the level of AL adherence (NAA) to predict an average Learning
score (AP) between 45.89 and 74.90 on the scale of performance degrees. β coefficients were
positive and significant, with p values < 0.01. The systematic methodological design and the
results obtained are intended to be the main contributions of this research to the literature and
to the latent discussion of the effectiveness of active learning methods in Engineering
Education.