# Identifying Engineering Undergraduates' Learning Style Profiles Using Machine Learning Techniques
## 2 Background
### Student Learning Styles  
Researchers agree that understanding student learning styles is a keystone for tailoring the teaching process, improving the satisfaction of educational needs, and enhancing learning experiences, especially in learning environments [16]. From a general point of view, since learning style is a component of the broader concept of personality [2], it may be related to specific personality traits. The Five-Factor Model has been widely used in the literature to measure personality traits. The FFM proposes five traits that capture the core domains of personality: conscientiousness, agreeableness, extraversion, neuroticism, and openness. Some studies have reviewed the relationship between psychological traits and the teaching-learning process. For example, [17] studied the predictive capacity of personality traits for teacher teaching styles in the Republic of China. The results indicate that personality traits contributed to the teaching styles of teachers beyond their gender, level of education, and perception of the quality of their students. On the other hand, the relationship between learning style and learners' personality was examined by [18]. This study reported that extroverted students tend to have an accommodative learning style. Finally, the concomitance between learning styles and psychological traits to explain learning to read English by Iranian students has been reported [19]. From a narrow point of view, the study of learning styles has generated considerable interest over the past three decades, leading to various models of learning styles based on how the learners adapt to multiple dimensions related to information reception and processing [5]. A review described 71 taxonomies of learning styles proposed in the literature [20]. The Felder-Silverman model is well-recognised in education [5; 21]. In this model, learning styles refer to different strengths and preferences in acquiring and processing information [5]. We chose the Felder-Silverman model for this study due to two reasons. First, the model is widely accepted in engineering education [22]. Second, the measured scale of the model is reliable, valid, and suitable for engineering students [5; 22]. The Felder-Silverman model classifies students by responding to four questions: What type of information does the learner preferentially perceive: sensory or intuitive? What type of sensory information is most effectively perceived: visual or verbal? How does the learner prefer to process information: actively or reflectively? How is the learner progressing in terms of sequential or overall comprehension? According to the answers, the learners are classified into four dimensions:  
* D1--Perception: sensing (concrete thinker, practical, oriented towards facts and procedures) or intuitive (abstract thinker, innovative, oriented towards theories and underlying meanings). * D2--Input: visual (learners prefer visual representations of material presented, such as pictures, diagrams, and flow charts) or verbal (learners prefer written and spoken explanations). * D3--Processing: active (learners prefer to learn by trying things out, enjoy working in groups) or reflective (learners prefer to learn by thinking things through, such as working alone or with a single familiar partner). * D4--Understanding: sequential (learners prefer to learn using a linear thinking process, learn in small incremental steps) or global (learners prefer to learn using a holistic thinking process, learn in giant leaps). The Felder-Silverman model is operationalised by The Index of Learning Styles (ILS). The ILS is a 44-item questionnaire designed to evaluate preferences across the four dimensions of the Felder-Silverman model (active/reflective, sensing/intuitive, visual/verbal, and sequential/global). The Index of Learning Styles exposes an extensive application and formal validation [21].