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In the context of e-learning systems, instructors are not confronted with their learners to supervise them. They do not have enough information about what are learners doing, and whether they have problems during the learning process or not. Consequently, instructors cannot take the appropriate decisions in time to help learners (Graf & Liu, 2010; Martinez et al., 2015; Pardo, 2014).
To deal with these issues, several works have been proposed to analyse learner behaviours. This analysis aims to acquire knowledge about the learning process and to understand the learner behaviours in the e-learning system. This knowledge can be used by different stakeholders such as learners, instructors, designers and pedagogues. For example, learners could get self-awareness about their learning progress. The Instructors could supervise their learners. Additionally, course designers and/or pedagogues can adapt and personalise the learning process (Bourguignon, 2007; Romero et al., 2013; Pardo, 2014; Mahajan, 2014; Martínez et al., 2015; Cereso et al., 2016).
Actually, the analysis of learner interactions serves as a mechanism to understand their behaviours in the e-learning system. This analysis is achieved by using log files-based processing (Bousbia et al., 2010). Log files save all events that can occur during the learning process. However, these events could not be exploited directly by different stakeholders because they are low-level, noisy, incomplete, and huge data. For this, it is difficult to extract knowledge directly from these log files.
Among the techniques used in the field of e-learning, to extract useful knowledge from log files, the authors find Educational Data Mining (EDM) (Neuhauser, 2002; Romero & Ventura, 2010; Siemens & Long, 2011; Agosti et al., 2012; Greller & Drachsler, 2012; Stater et al., 2016; Bakhshinategh et al., 2018). This technique is used to well-understand learner interactions and to discover their behaviours (Cereso et al., 2016). EDM focuses on developing and applying computerised methods to detect patterns in large collections of educational data, which would otherwise be difficult or even impossible to analyse (Romero & Ventura, 2010; Peña-Ayala, 2013). These methods include data mining, machine learning, statistics, data visualization, and modelling methods (Baker & Siemens, 2014; Bakhshinategh et al., 2018).