## 5 Discussion  
Clearly, it is practically impossible for all students to retain knowledge for a year without forgetting anything (i.e., to achieve 100% accuracy rate in Figure 6). In spite of this unrealistic expectation, both Figure 6 and Table 3 show that the percentage of retained knowledge varies from one year to another: between 2015-2018 there are hardly any differences, but there is an increase in 2019. The results from 2015-2018 are based on the use of earlier VRLEs designed several years ago (Figures 1 and 3), whereas the results obtained from 2019 are related to the use of updated VRLEs designed more recently (Figures 2 and 4). During the period 2015-2018 the accuracy rate varies between 45% and 51% (Figure 6), while in 2019 the mean value rises to almost 65%, thereby increasing approximately 30% over previous years. On the other hand, the standard deviation is quite similar in the period 2015-2018, indicating a non-significant variation from year to year (extending the number of right answers from 11 to 20 out of 30). However, the higher standard deviation in 2019 suggests a greater dispersion of results, emphasizing that the new design process (step-by-step protocol) is quite effective for some students but, at the same time, it is not too effective for others. Nevertheless, taking into account that the range \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Students’ Marks** & **2015** & **2016** & **2017** & **2018** & **2019** \\ \hline Mean (\%) & 45.33 & 51.33 & 48.41 & 49.84 & 64.76 \\ Standard deviation (\%) & 6.93 & 7.60 & 7.50 & 7.11 & 11.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical results of students’ marks after using VRLE one year prior. Figure 6: Accuracy rate (students’ marks) of survey questions provided by students, who participated a year earlier in class sessions covering fundamental concepts in material science and engineering (MSE) through VRLEs. of data in 2019 is between 13 and 24, it is possible to ensure that the new design favors a higher level of meaningful learning. Several factors could influence these results: (i) the teacher; (ii) the contents given during class in the different years; (iii) the methodological process used during classes; (iv) the survey questions; (v) the academic level of the students; (vi) the software used to create the VRLEs; and (vii) the design process used in the VRLEs. Given that the first four factors have been the same during all years (2015-2019)--the teacher was the same during such a period, the contents did not vary from year to year, the methodological process was identical in all the academic courses, as shown in Figure 5, and the survey questions were the same in all cases)--and that the students' scores were similar as well, the key variables that may have had significant influence on the improved results in 2019 are: (i) the software used to create the VRLEs (i.e., newer, more powerful, and more versatile in 2019 than in 2015-2018, consequently, the VRLEs are more appealing and engaging for students [24]); (ii) the new design process used in the VRLEs in 2019 (Figure 4) with an enhanced step-by-step protocol. It should be noted that updating a VRLE with a better realism helps the student to be more motivated, thereby being more engaged and focused on the contents of the VRLE (which likely leads to a higher level of meaningful learning). However, based on the authors' own experience in designing different VRLEs for several years, the more relevant aspect influencing the meaningful learning experience is the step-by-step protocol. In previous studies [4; 6; 9; 10; 11; 15], the authors have verified that, in general, the motivation is always high when using this type of TEL. However, this aspect (higher level of motivation by updating the software) cannot be the key factor that has favored the increase reflected in Figure 6 of approximately 30% in the knowledge retained one year after.