. MR instruction was able to facilitate an interactive,collaborative, problem-based approach to learning in courses. Implications for Engineeringeducation, grounded in the original literature-based theory, are described.Key words: mixed reality, virtual reality, holograms, digital twins, active learning, educationaltechnology, remote learning, chemical engineering, electrical engineering, computer science,laboratory equipment, laboratory instruction, formative assessment.1. IntroductionDuring the COVID-19 pandemic, when remote instruction was mandated by institutions of highereducation, laboratory experiences, which are traditionally a practical, in-person activity, wereoffered virtually. There were many ways in which different institutions
from the program. However, it is challenging to keep students engaged and attentive inthis age and time using traditional teaching mediums such as boards and slide decks.1 Theabstract and sometimes intimidating nature of engineering concepts—such as thermodynamics,material balances, or reaction kinetics—requires teaching approaches that are accessible,impactful, engaging, and frankly exciting.In this context, student engagement is more than a matter of participation; it directly correlateswith how well students internalize and apply the material. Traditional lecture methods canstruggle to keep students actively involved, especially in large classes where individualinteraction is limited.2,3 Without opportunities for hands-on exploration or
learning. These foundational topics provide students with a basic level oftheoretical knowledge needed to effectively apply ML tools and techniques in practicalscenarios.An example of one of the foundational topics is where students download wine quality data fromthe University of California Irvine’s Machine Learning Repository (Cortez, 2009). UsingMATLAB’s Regression Learner Toolbox, they employ supervised learning techniques, such asregression, to predict wine quality. A key feature of MATLAB is its ability to simultaneouslycompare multiple models, enabling users to select the most effective one (see Figure 1).Figure 1: Screenshot of Matlab Regression Learner Toolbox (The MathWorks Inc., 2023).Following this introduction, the majority of the
students. Students often express astrong sense of pride in becoming an engineer and believe that their hard work, perseverance,and ability to overcome rigorous challenges are a testament to their capability and worthiness inthe field.[1] However, the intense, high-pressure culture within engineering programs often takesa serious toll on students' mental health. Engineering students, even before the COVID pandemichit, were reporting higher levels of stress, anxiety, and depression than the general studentpopulation, but are less likely to seek help.[2-4] Their well-being is connected to whether theyfeel like the academic environment is supportive, hostile, or something in between.Culture change is a gradual process, requiring time, commitment, and