# Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories
## 5 Results
### Access Patterns  
These patterns describe the timing of the students' accesses to the proposed exercises during the SQL laboratory session. A sample of the extracted sequences is reported in Table 3, with the relative support value (percentage of students that satisfy the specific sequence). Based on the sequences belonging to this pattern type, students can be clustered into two groups based on their profile of accesses to the proposed exercises:  
* _Students using sequential patterns_: this cluster consists of the students who accessed the exercises in the proposed sequence (from exercise 1 to 13).
* _Students using out-of-order patterns_: this groups the students who follow a non-sequential order in accessing the assigned exercises.  
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Key Engagement Indicator** & **Pattern Type** & **Comments** \\ \hline Understanding & Time-constrained patterns & These patterns highlight the timing of a student working on an exercise. They provide useful hints to answer questions such as: (i) What is the (overall) average time spent by students on each exercise? (ii) What is the average time spent on an exercise prior to submitting a query? (iii) What is the average time needed to resubmit a new solution after a failure? (iv) What is the average time needed to solve an exercise? & Assistance intervention patterns highlight the effect of an intervention by a teaching assistant on the solution of the current exercise and of the following ones. Together with the successful patterns, they differentiate students who are able to solve exercises autonomously and those who need extra explanations. \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._Sequential patterns reveal that most students consecutively accessed the first five exercises. However, as the exercise number increases, the pattern support decreases. For example, it decreases by 4% from A1 to A2 and by 9% (179 students) from A2 to A3. Furthermore, the frequency count halves from A3 to A4. This result reflects the actual complexity of the proposed exercises: teaching assistants confirmed that the perceived complexity of exercise 5 was higher than expected. It should be noted that the application used by the students during the laboratory allowed them to access a specific exercise only after all previous ones are accessed. This is the reason why skipped exercises never occurred in these patterns (An exercise is considered as skipped when the student did not access it).  
Out-of-order patterns reveal the students who came back to a previous exercise. In [49], the authors highlighted the usefulness of \"design by copying\" practice, whereas in [50] the authors paid attention to the \"we do as we did in the previous exercise\" thinking in learning practice. These behaviors also occur in this learning context and explain why the students are used to coming back to the previous exercises; most of students are facing the SQL language practice for the first time and they are not yet familiar with the subject.  
Table 3 shows that _out-of-order_ patterns are almost equally spread over the first six exercises; in fact, the support value does not show any significant variation, as happened for the _sequential_ sequences. Conversely, it slightly varies between 16.7% (36 students) and 20% (43 students).  
The differences between sequential and out-of order sequences are likely to be related to the \"_Persistence\"_ indicator of behavioral engagement. This aspect will be discussed later on (see Section 6).