## 5 Survey Results, Analysis and Discussion
Furthermore, all female respondents affirmed that the software was well structured and helped in the identification, diagnosis, and rectification of errors in their exercises. Among male respondents, these percentages were slightly lower, with 80.2% perceiving it as easy to use and 86.5% finding it easy to learn. Moreover, 74% of male participants reported its effectiveness in terms of identifying errors. These findings offer valuable insights into the gender-based differences in opinions regarding MaGraDa, with the female cohort exhibiting a more favourable outlook across several key dimensions of assessment. Mann-Whitney U and Kruskal-Wallis H tests were also employed to conduct comparisons based on the other characteristics of the surveyed population. The aim of this analysis was to determine whether there were differences in opinions based on the highest university year of the students, and whether it was the first time they had attended the DM course. In neither case were significant differences observed in the distribution of responses. To analyse the effect of students' socio-demographic profiles (described in Table 14) on the four dimensions obtained using CATPCA in Section 4.3, we also conducted a stepwise MLR analysis. The decision to use the dimensions obtained through CATPCA was based on its ability to identify the dimensions more clearly than EFA and PCA from the set of Likert scale variables used for the survey. Moreover, CATPCA explained a higher percentage of variance (71.179%) with 4 components, compared to FA and PCA. Since MLR treats independent variables as numerical, dummy variables were generated to represent the categorical variables. The gender variable accounted for approximately 3.6% of the variation in students' opinions about the utility of MaGraDa for learning (dimension 1). With a \(p\)-value of 0.047, this variable emerged as the sole significant socio-demographic contributor to this dimension. As shown in Table 18, for similar profiles, female students rated the utility of MaGraDa for learning 0.556 points higher than male students. Furthermore, the categorised age of the students explained 4.9% of the variation in dimension 4, with students aged 20 to 29 rating the content quality of MaGraDa 0.472 points higher than the rest of students. The socio-demographic profile did not influence the students' opinions about the usability and user-friendliness of the MaGraDa software (dimension 2) or its impact on academic performance (dimension 3). The final grades of students for the laboratory practice sessions were also compared with their opinions on each survey question. A preliminary statistical analysis to test the normality of the final grades was carried out using both the Shapiro-Wilk and Kolmogorov-Smirnov (with the Lilliefors correction) tests, which showed that there was no evidence to reject the assumption of normality in any group. The ANOVA (analysis of variance) test and the independent-samples \(t\)-test were therefore used to analyse the mean grades. Significant differences were observed in the mean grades achieved by students who expressed overall satisfaction with the MaGraDa software compared with those who were dissatisfied (see also Figure 20). Students who expressed satisfaction with MaGraDa achieved a mean grade of 6.87 and a median of 7, while their dissatisfied peers had a mean grade of 5.21 and a median of 5.5. These results indicated the influence of satisfaction with MaGraDa on academic performance. This was mainly because most of the satisfied students were able to use MaGraDa efficiently to learn about graphs and to identify, diagnose, and correct errors in their exercises, thus obtaining better grades for the laboratory sessions.