Structural Modeling and Measuring Impact of Active Learning Methods in Engineering Education

Abstract

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> This article presents a novel approach that demonstrates how students’ learning can be improved by increasing classroom adherence to active learning (AL) application, in a typical engineering education (EE) environment. It does that by using classroom observation protocol data and student assessment grades analyzed by a statistical tool, representing seven engineering programs. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Background:</i> AL applications in EE have been growing in recent years and there have been relevant discussions about their effect on students learning. This research is grounded on a 2.5-year-long data collection process through objective measures, following a strict experimental design project. It differs from other studies where surveys are used for data collection. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Research Question:</i> Can differences in students’ learning between traditional teaching and AL methods be distinguished by means of classroom observation protocol measures coupled with partial least-square structural equation modeling? <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Methodology:</i> An experimental research design was conducted in an Engineering Higher Education Institution, by taking independent measures of latent constructs’ indicators, such as student grades and AL classroom adherence levels. The data were subject to a partial least-squares structural equation modeling (PLS-SEM) approach for modeling, analysis, and validation. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Findings:</i> The results suggested a nonlinear positive cause-and-effect relationship between AL adherence and learning, validated by several performance indexes, such as a learning prediction relevance <inline-formula> <tex-math notation="LaTeX">$Q^{2}~\mathrm{predict}$</tex-math> </inline-formula> <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.453 and goodness of fit <inline-formula> <tex-math notation="LaTeX">$=$</tex-math> </inline-formula> 0.588. The model demonstrated a learning score improvement from 45.89 to 74.90 as an effect of the adherence to AL score increase from 0 to 35.97.