A Complete Validated Learning Analytics Framework: Designing Issues from Data Preparation Perspective

2. Literature Review

LA is rooted in data science, artificial intelligence, practices of recommender systems, online marketing and business intelligence. Siemens (2010) defined LA as “the use of intelligent data, learner-produced data, and analysis models to discover information and social connections, and to predict and advise on learning.” Fournier, Kop and Sitlia (2011) considered LA as “the measurement, collection, analysis, and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.” Van Barneveld, Arnold and Campbell (2012) defined LA as “the use of analytic techniques to help target instructional, curricular, and support resources to support the achievement of specific learning goals.” LA approaches typically rely on data obtained from learners’ interactions with Information and Communication Technologies (ICTs), such as learning management systems and social media (Gašević, Dawson, Rogers & Gasevic, 2016). Powell & MacNeill (2012) identified five potential purposes of LA which are: (1) Provide learners feedback about their learning progress compared to their colleagues; (2) Identify at risk students; (3) Help instructors to plan interventions when needed; (4) Enhance the designed courses; and, (5) Support decision making when it comes to administrative tasks. Furthermore, while the most used method to model learners is questionnaires, Tlili, Essalmi, Jemni, Kinshuk and Chen (2016) proposed a new approach which uses LA to implicitly model learners in computer based learning environments.