# Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories
## 7 Conclusions  
This work proposes a method to deeply analyze student's behavior during laboratory sessions. It relies on data collected in the context of a B.S. degree course on database design and management. The collected data describe the main activities performed by the participants during a computer laboratory session. _Confidence_ and _Autonomy_ are strongly correlated with each other, as shown in diagram (a). Specifically, 68% of students who have _High Confidence_ also have _High Autonomy_, whereas 74% of the students who have _Low Confidence_ also have _Low Autonomy_. This evidence confirms what previously emerged in the analysis of the most frequent profiles (see Section 6.1), and it is explained by the fact that, commonly, when students asked for help (_Confidence_) they received it (_Autonomy_).  
_Concentration_ and _Autonomy_, on the other hand, are independent: 47% of students who have _High Concentration_ have _High Autonomy_ as well, and 53% have _Low Autonomy_. The general level of _Concentration_ is _High_ (see Figure 2), but _Autonomy_ is a characteristic of the students that is mainly influenced by self-confidence rather than by the capability to focus on a given task.  
_Autonomy_ and _Understanding_ are also independent, as shown in diagram (b). Specifically, 44% of students who have _High Understanding_ also have _High Autonomy_ and 44% of them have _Low Autonomy_, while 41% of students who have _Low Understanding_ have _High Autonomy_ and 48% of them have _Low Autonomy_. This shows that help interventions, while they are generally sufficient to solve the specific task for what they were requested, are not always effective for providing a more comprehensive level of understanding applicable to all the tasks. Furthermore, they show that the perceived need for external support is very personal and not always related to the actual need.  
Most students have _High Concentration_ and _High Reflection_ (as shown in Figure 2), and they are correlated with each other: 87% of students who have _High Concentration_ also have _High Reflection_, and only 9% of them also have _Low Reflection_. This is understandable, because the capability to focus on a task influences the attitude to apply a more reflective approach in problem solving.  
_Confidence_ positively influences _Reflection_, as shown in diagram (c). Specifically, 69% of students who have _High Confidence_ also have _High Reflection_. Conversely, only 18% of the students have _Low Reflection_, and 84% of students who have _High_ or _Medium Confidence_ also have _High Reflection_. This is justifiable because self-confidence helps students to rely on their own capability and to address problems with a reflective approach (compared to a trial-and-error one).  
The implication that _Reflection_ positively influences _Understanding_ clearly emerges from the performed analyses, as shown in diagram (d). Specifically, 71% of students with _High Reflection_ have _High_ or _Medium Understanding_, whereas only 29% have _Low Understanding_, and only 28% of students who have _High Understanding_ have _Low Reflection_. The attitude to face a problem in a more reflective way has a positive influence to apply what has been learned in the following problems. The sequence of exercises was proposed by the teacher with this goal in mind, to progressively build competence and skills in the specific subject.  
No specific correlation was found between _Persistence_ and the other dimensions, possibly because the persistence level was high for almost all the students: the laboratory was not compulsory so the participating students were mainly committed to it, with a good level of behavioral engagement. If the laboratory would be compulsory, the results would probably have been different, with a variable level of behavioral engagement that could have influenced cognitive and affective engagement aspects.