# Identifying Engineering Undergraduates' Learning Style Profiles Using Machine Learning Techniques
## 1 Introduction  
The COVID-19 pandemic has posed several challenges to higher education in teaching, learning, research collaboration and institutional governance [1]. Since it has been stated that the contexts in which learning occurs are crucial [2], one central research focus is examining and analysing learning styles in this new setting. This effort is even more critical given that the first reports about education in this new context indicate that the most frequent barrier is the difficulty in adjusting learning styles [3].  
There are countless ways people process information from the environment, and individuals exhibit specific behaviours that allow them to learn efficiently [4]. They prefer interaction, assimilation, and information processing methods. This natural disposition or preference of the individual to learn and study is known as a learning style [5]. Since there are many learning styles among different individuals, it is a challenging task to determine and predict the learning style of an individual student. According to these ideas, adopting a standard pedagogy method is not appropriate to improve learning for all students. Therefore, it is essential to devise and adopt different pedagogies for different types of learners.  
Universities in a pandemic face a hybrid teaching environment. The literature suggests that hybrid learning offers many benefits to students and faculties [6]. Nevertheless, this environment regularly changes scenarios and actors. Thus, the rapid identification of the learning styles of participants appears to be fundamental to achieving complementarity between traditional teaching and the application of new technologies. However, the extension of the existing learning style measurement instruments in the literature limits this rapid detection. In this context, is it possible to quickly detect the learning styles of university students of engineering? This question establishes the research problem for this study.  
This study proposes developing a predictive model that identifies learning styles using analytical techniques. In the vein of a study that applied machine learning to reducedata capture times [7], our research is in line with works that reduce scales of learning styles [8; 9; 10]; however, we have advanced towards predicting a student profile associated with a learning style. The main practical contribution of this study to teaching in higher education is to allow administrators and educators to adopt different pedagogies for different groups of students quickly, improving students' academic results. Additionally, obtaining data on the learning styles of university students in the context of the pandemic in a developing country contributes to the generation of knowledge about this phenomenon for organisations and academics interested in higher education in the world.  
In particular, the objective of this study is to generate a predictive model to detect undergraduates' learning style profiles quickly for making recommendations to teachers and managers to improve learning outcomes.  
To achieve this objective, we proceed as follows. First, the following section describes the study background, including student learning profiles, student learning styles, and predictive analysis with decision trees. In the next section, the materials and methods are defined. Then, the results of the analyses are shown. Finally, the findings are discussed, limitations and future research lines are given, and conclusions are presented.