# Identifying Engineering Undergraduates' Learning Style Profiles Using Machine Learning Techniques
## 5 Discussion  
This paper generated a model to detect undergraduates' profiles based on swift learning styles. A machine learning process founded on a C4.5 algorithm generates a predictive model to classify students into two profiles. The two student learning style profiles were previously obtained from k-means clustering analysis. The result of two distinct learning style profiles is a remarkable finding: both tend towards the sensing, visual, and active poles. This fact is consistent with the literature of both engineering students [34] and students of other disciplines [35; 36]. In a sample of Chilean engineering students, [23] found that most of the students were oriented to the sensing (84 per cent), visual (76 per cent), and active (70 per cent) poles. In a sample from a university in Mexico, [37] reported that engineering students tended towards the sensing (82 per cent), visual (90 per cent), and active (67 per cent) poles. As well, in a sample of manufacturing engineering students in Ireland, [38] found a bias towards the sensing (78 per cent), visual (per cent), and active (70 per cent) poles. In undergraduate business students, [35] reported that these learners tended toward the sensory (70 per cent), visual (68 per cent), and active (64 per cent) poles. Finally, a study by [39] discovered among industrial engineering students in Brazil the trend towards sensing (70 per cent), visual (73 per cent), and active (66 per cent) poles. Although there is the recent proposal of [32], we do not find student cluster reports associated with their learning styles based on the ILS, hence the importance of these results.  
Although our study is in the line of works to reduce the scale of learning styles [8; 9; 10], these research findings distinguish an engineering student profile using a few questions of the ILS. Furthermore, the results suggest that attributes such as gender, age, academic performance, previous education, behaviour in social networks, perception of preferences to learn, or psychological traits are not appropriate to predict the student's profile. The exception is agreeableness; this trait discriminates between the clusters.  
In particular, the results indicate that the model's predictive capacity is good (82.93%). Additionally, we highlight the rapidity of application due to this being essential in a hybrid environment: the model classifies 65% of students with no more than five questions, respectively. This decision tree model is the basis for the design of a computerised survey system for rapid discrimination.  
Two limitations of this study should be stated. First, the analysis was conducted on a relatively small sample in one unit, which does not directly extrapolate the results. Second, a limited set of student attributes was used to predict their learning style profile, and, as a result, other attributes may predict this profile with greater accuracy.  
Future studies should go three ways. First, to extend the analysis to a more significant sample. Second, to explore in other samples of engineering students in emerging economies the two profiles discovered in this investigation. Third, using new student attributes to predict their learning style profile, including personal values.