## 6 Discussion
Therefore, alternative methodologies should be considered to comprehensively study learning styles. However, within the current context of this study, learning styles may be utilized to understand how students engage with the learning materials. Teachers can \begin{table}
\begin{tabular}{l|c c c c} \hline
**CHEA** & \multicolumn{2}{c}{**Score**} & \multicolumn{2}{c}{**Level**} \\ \hline
**Grade** & **Score** & **Level** & **Score** & **Level** \\ \hline ExpBio-Bioche-2223 & 0.538 & **0.619** & 0.451 & 0.577 \\ ExpBio-Bioche-2324 & 0.280 & 0.229 & **0.369** & 0.318 \\ PlaNut-Bioche-2223 & **0.170** & 0.081 & 0.120 & 0.050 \\ StrFun-Cell-2223 & **0.104** & 0.059 & 0.097 & 0.090 \\ Healthp-Speech-2223 & **0.083** & 0.048 & 0.041 & 0.060 \\ ChiDis-Speech-2223 & 0.142 & 0.209 & **0.236** & 0.229 \\ ComLan-Primary-2324 & 0.296 & **0.310** & 0.238 & 0.202 \\ Intsys-Health-2223 & 0.282 & **0.294** & 0.255 & 0.253 \\ ComTee-Software-2223 & **0.148** & 0.111 & 0.110 & 0.099 \\ Intsys-Computer-2223 & **0.460** & 0.366 & 0.318 & 0.267 \\ TheAut-Computer-2223 & **0.024** & 0.022 & 0.021 & 0.015 \\ TheAut-Computer-2324 & 0.181 & 0.248 & 0.192 & **0.292** \\ ProPla-Computer-2223 & 0.136 & **0.260** & 0.079 & 0.246 \\ \hline Mean & 0.219 & **0.220** & 0.194 & 0.208 \\ Standard deviation & 0.149 & 0.164 & **0.132** & 0.151 \\ Median & 0.17 & **0.229** & 0.192 & **0.229** \\ Wins & **6** & 4 & 2 & 1 \\ \hline \end{tabular}
\end{table} 
Table 10: Maximum \(R^{2}\) performances of the linear regression models for each configuration of grades and CHAEA marks when distributed using scores or levels. Figure 6: Average classification of the CHAEA results for courses from a BSc in Speech Therapy (**left**) and a BSc in Computer Science Engineering (**right**). use this information to comprehend why students might interact with the same materials differently and guide them on how to approach these materials effectively. It must be highlighted that our primary goal is to facilitate a tool with the potential to be used in courses with a considerable amount of students involved. Furthermore, this study presents several difficulties that could distort the results, including the following:  
* Student participation rate. In order to extract more solid conclusions, a broad representation of the classroom should be used to complete the survey. However, as pointed out before, students are increasingly less involved in these types of altruistic or voluntary tasks. * Teachers with different subjects in each course. Educators involved in this study can teach particular subjects in a specific academic year; in the following year, they can teach other completely different materials. This can increase the number of subjects to be analyzed, but it complicates the tracking of any particular subject. * CHAEA nature. The unequal distribution of questions among the considered learning styles to reach the same level may disadvantage some styles while benefiting others. Some possible actions to be assumed in order to avoid or alleviate these difficulties and draw more robust conclusions include the following:  
* A higher number of courses: Although increasing the number of courses under study does not increase the participation rate, it will provide more fulfilled questionnaires. * Send reminders: Reach out to those who have not responded to motivate them to participate. * Incentives: Provide rewards to encourage participation, such as offering extra credit, gift cards, a raffle, or even extra grade points. * Simplify the questionnaire: Confirm the survey is concise and easy to fulfill in order to reduce the burden on participants. * Use an additional learning traits questionnaire: Ensure the average learning style profile of the class. * Evaluate motivation: Use a different type of voluntary or altruistic questionnaire to verify and evaluate the level of encouragement. * Imputation: Use imputation techniques to estimate the responses of non-respondents based on the data from participants.