# Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories
## 5 Results
### Discussion  
The extracted patterns can be used to gain insights into the students' learning experience during the SQL laboratory sessions. Very few students completed all the assigned exercises: most of them completed only the first six exercises. The results confirm that the proposed practice was too long for a 90-min session. The teachers' objective, in fact, was to challenge the students with more exercises than were strictly required in order to encourage them to complete the practice at home.  
_Access patterns_ show that as exercise number increases, the number of students accessing it decreases, because most of them are struggling on the previous ones, whereas _Successful patterns_ and _Error patterns_ show that few students who solved exercise 4 pass all of the first four exercises; these findings reveal the general difficulty in solving the first part of the lab session.  
In a time interval of 5 min after access to the exercises (see Table 7), a significant number of students could solve only exercises 2, 4, 3, and 6. Exercises 1, 3, and 5 were the ones where students had more problems (see Table 8). Furthermore, Table 9 shows  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Id** & **Pattern** & **Students** & **Students (\%)** \\ \hline T21 & 1��1�97 & 45.1 \\ \hline T22 & 3�3�3 & 81 & 37.7 \\ \hline T23 & 5�5�5 & 74 & 34.4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Patterns for interval 10–15 min.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Id** & **Pattern** & **Students** & **Students (\%)** \\ \hline L1 & 1�2�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�3�33�3�3�33�3�33�33�3�33�3�33�3�33�33�33�3�33�33�33�3�33�33�333�3 that approximately 16% of students spent more than 30 min on exercise 1 before accessing exercise 2, and that approximately 3 out of 4 students spent at least 30 min before accessing exercise 5 after having accessed exercise 1. A difficulty disparity between exercises 2, 3, and 4 and exercises 1 and 5 is therefore evident. Regarding exercise 1, this is understandable because most students were using the learning environment for the first time, and this was also the first time they had practiced with SQL. Exercise 5 caused many problems for most of the students because it introduced new SQL language structures.  
_Assistance patterns_ show that the requests for help and the assistants interventions were usually useful for solving the exercises, and that the students succeeded in most cases after being helped. Students were used to asking for help after a few minutes from the exercise accesses, and often many students asked for assistance simultaneously; this caused a waiting time of up to 10 min before being assisted. In addition, they rarely required assistance twice for the same exercise. The assistants usually intervened after 10 min, due to the high number of assistance requests. In addition to the startup delay, there are some specific exercises (especially number 5) that required a long time to be solved. Some of the students solved the exercise before the assistant interventions (especially for exercise 1).  
In general, students submitted several wrong queries before the correct one, showing a trial-and-error approach that is typical for a laboratory session in computer science courses.  
Through sequential pattern analysis, teachers could reinforce the lab experience by considering the discovered issues. First of all, an introduction of the lab environment could be suitable for limiting the startup problems; some exercises could be solved step-by-step by the assistants to prepare the students for the autonomous work. The sequence of the proposed exercises could also be modified to better reflect the students' perceived difficulties.