# Monitoring Students at the University: Design and Application of a Moodle Plugin
## 3 Results
### Contrasting Objectives
#### 3.1.2 Objective 2  
Regarding objective 2 (\"To test the effectiveness of the 'eOrientation' plugin on university students\"), the \"eOrientation\" Moodle plugin was available to 279 health science students following two degree courses: Nursing and Occupational Therapy. The logs were extracted from the type of accesses indicated in the section on instruments for the design of subjects (2.2) (\"feedback\", \"quizzes\", \"theoretical information\", \"practices\", \"supplementary material\", and \"information on PBL\") in order to check the effectiveness of the tool. Automatic learning techniques for prediction (MRA and DT) and clustering (k-means) were used for the analysis of these data, all of which were computed with the statistical package SPSS v.24. Firstly, we studied whether the \"degree type\" variable predicted student learning behaviours when accessing the following aspects: \"feedback\", \"quizzes\", \"theoretical information\", \"practices\", \"supplementary material\", and \"information on PBL\". The \"degree type\" variable was found to predict 59.2%, \(R^{2}=0.592\), of student behaviour patterns on the platform for these activities. Specifically, the partial correlations showed significant differences for accesses to \"feedback\" (\(t=4.11\), \(p=0.000\)),  
Figure 6: Bar graph of student participation in teacher feedback on different activities. Figure 7: Functionality of sending a message to facilitate the orientation of the at-risk student. quizzes (\(t=-6.51\), \(p=0.000\)), and \"practices\" (\(t=6.35\), \(p=0.000\)). The Tolerance (\(T\)) values were found in an interval between 0.753 and 0.942 (the accepted \(T\) value was less than 1, so these data were within the expected patterns). For the Variance Inflation Factor (_VIF_) values, the values found were in the range of 1.06 to 1.26 (the accepted value of _VIF_ was within the range of 1 to 10). It was therefore concluded that the distribution lay within the parameters of normality and that parametric statistics could be applied. Regarding the prediction of the variables, it was found that the variable with the greatest predictive value among the two groups of degree students was the \"frequency of access to the quizzes\". Specifically, three groups could be identified, the third of which had a higher percentage of nursing students (85.7%) compared to occupational therapy students (14.3%), and that ratio was reversed in the first group (30.7%/69.3%), as shown in Figure 8.  
The clustering technique was also applied, and three clusters were found for the distribution of \"frequency of student access\" to resources on the platform (see Table 3). Likewise, significant differences between clusters were found under \"feedback\". To sum up, a contingency table was calculated to check the student distributions within the two degrees within the three clusters (see Table 4). It was found that there was a higher percentage of Occupational Therapy degree students within Cluster 2 (60.56%) and a higher percentage of Nursing degree students within Cluster 1 and Cluster 3 (49.63% and 24.09%, respectively). Finally, Orange software-based data visualization tools were used. Specifically, aspects of prediction and classification were displayed. In Figure 9, we can see the different frequency of student access (Nursing degree vs. Occupational Therapy degree) in the variables, in which significant differences were found for the three clusters. In the blue coloured spots, which represent nursing student groups, the results show greater evenness for all types of access, except for access to quizzes. The yellow coloured spots that represent the groups of occupational therapy students reflect a greater dispersion of access to \"feedback\", \"theoretical information\", and \"practices\". Similarly, in Figure 10, the distribution of students in the three clusters can be seen.