# Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories
## 4 Materials and Methods
### Computation and Analysis of Engagement Key Indicators  
Teachers explore the sequential patterns extracted at the previous step to gain insights into students' engagement in the SQL computer laboratories. The student-related events considered in this study (see Section 4.2) are exploited to analyze student involvement, motivation, and willingness to comprehend the fundamentals behind the SQL language. Specifically, the aim is to analyze the sequence database in order to characterize the behavioral, cognitive, and affective engagement levels of the students who participated to the laboratories. The occurrence of single events (e.g., the access to a specific exercise) is not relevant enough to profile students according to their engagement level because it is likely to be related to the occurrence of other events that occurred in the past, potentially regarding different event types and exercises. Hence, the present analysis relies on the extraction of sequential patterns, which represent the most significant temporal correlation between the occurrences of multiple events. The idea behind this is to capture the most interesting temporal relationships between correlated events and obtain actionable knowledge about student activities, involvement, and motivation. Based on the characteristics of the contained events, the extracted sequential patterns can be classified as follows:  
* Access patterns: This type of pattern comprises all the sequences whose elements are exclusively composed of events of type access to exercise. Since students (i) are provided with an ordered list of exercises, (ii) have no time limits to solve an exercise, (iii) and can move back-and-forth in the exercise list according to their preferences, exploring access patterns allows teachers to understand the way students deal with the laboratory exercises as well as to analyze the time spent on each exercise. * Successful patterns: This pattern category includes all the sequences whose elements comprise both access and successful attempts for the same exercise. They are deemed as relevant to explore both the level of complexity of the provided exercises and the level of competence of the students. * Assistance request patterns: This type of pattern includes all the sequences that comprehend a request for assistance. * Assistance intervention patterns: This type of pattern consists of all the sequences that comprehend an intervention of the teaching assistant. Together with the assistance request patterns, they provide interesting insights into the ability of the students to work in autonomy. They allow us also to identify the most common situationswhen students ask for help, and to study the impact of the intervention of a teaching assistant on the development of the current and following exercises. * Error patterns: This pattern type comprises all the sequences whose elements include events of type wrong submitted query for a given exercise. They can be exploited to identify the exercises generating major difficulties and to cluster students based on their level of competence, as well as to monitor the progresses of the students across the practice (e.g., to understand whether the trial-and-error approach actually works or not). * Time-constrained patterns: This class of pattern consists of all the sequences extracted by enforcing either a minimum/maximum gap between each element of the sequence or a maximum sequence duration (i.e., the elapsed time for the occurrence of the first element and those of the last one). Unlike all the previous pattern types, they give more insights into the timing of specific event. They can be exploited to analyze the timing of the activities and the responsiveness of a student (e.g., the time needed to submit the first query, the time needed to resubmit a query after a failure, and the overall time spent in solving an exercise). As detailed in Table 2, the above-mentioned pattern categories are mapped to the engagement key indicators reported in Table 1.