# Data-Driven Analysis of Student Engagement in Time-Limited Computer Laboratories
## 2 Literature Review  
The use of laboratories in computer science education is well established; several studies (e.g., [1; 2; 16; 17]) have highlighted the advantages of having a practical approach to learning, describing facilities and suggesting best practices. The research community has stressed the importance of cooperation while working in laboratories. Laboratories are not simply considered as places where a single student interacts with a Personal Computer: their use is primarily concerned with the interaction between students [16; 18]. Therefore, studying learners' interactivity inside a lab is particularly useful for improving the effectiveness of learning practices. The Structured Query Language (SQL) is the most widespread declarative programming language to query relational databases. Due to the overwhelming diffusion of relational Database Management Systems, in software engineering and computer science education, Structured Query Language (SQL) skills are deemed to be fundamental. A systematic review of SQL education is given by [14]. In the early 2000s, most research works related to SQL education were focused on proposing ad hoc tools to support laboratory sessions on SQL query writing (e.g., [19; 20; 21]). Later on, with the growth of Learning Analytics (LA) technologies, the attention of the community has shifted towards the development of smart solutions to acquire, collect, and analyze learner-generated data within SQL laboratories. For example, an established LA challenge is to predict students' performance early [22]. Under this umbrella, the works presented in [23; 24; 25] proposed recording students' activities in SQL laboratories in order to obtain inferences related to the upcoming students' performance. More recently, the research community has paid more and more attention to innovative SQL learning paradigms, e.g., blended learning [26; 27], game-based learning [28], and flipped classrooms activities [29]. The present paper positions itself as a new learning analytics study in higher education [30], with particular reference to SQL laboratory activities. Unlike [23; 24; 25], the focus of the present work is not on predicting students' performance. Conversely, it investigates the use of exploratory data-mining techniques, i.e., sequence pattern mining [31], to characterize and profile learners' activities during SQL laboratory sessions and to describe the cognitive, behavioral, and affective dimensions of student engagement. In recent years, the parallel issue of fostering student engagement through educational technologies in secondary and higher education has received increasing attention [1; 4; 8; 32]. For example, the authors in [32] analyzed the behavioral engagement of MOOC participants based on both the timing of resource accesses and on the type of explored resources, i.e., video, Self Regulated Learning (SRL) support video, discussion, quiz, assignment, reading. In [8], the authors analyzed click-stream log data related to 89 students of a Freshman English course. They classified students as surface, deep, or strategic according to their engagement level measured in terms of time spent on the Web pages and number of actions made on that pages (detected from reading logs). Some attempts to facilitate students' engagement in secondary education through flipped learning approaches have also been made [4]. An extensive overview of the existing educational technology applications to enhance student engagement in higher education can be found in [1]. Similar to [8; 32], in this study we analyze click-stream data in order to monitor students' engagement levels. Unlike [8] we consider a different context of application (i.e., a higher education course on databases), and we apply a different methodology for exploring data.