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

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

Ahmed Tlili (Research Laboratory of Technologies of Information and Communication and Electrical Engineering (LaTICE), Tunis higher school of engineering (ENSIT), Tunisia), Fathi Essalmi (Research Laboratory of Technologies of Information and Communication and Electrical Engineering (LaTICE), Tunis Higher School of Engineering (ENSIT), Tunisia), Mohamed Jemni (Research laboratory of Technologies of Information and Communication and Electrical Engineering (LaTICE), Tunis higher school of engineering (ENSIT), Tunisia), Kinshuk (University of North Texas, Denton, USA) and Nian-Shing Chen (Department of Information Management, National Sun Yat-sen University, Taiwan)
DOI: 10.4018/IJICTE.2018040101

Abstract

With the rapid growth of online education in recent years, Learning Analytics (LA) has gained increasing attention from researchers and educational institutions as an area which can improve the overall effectiveness of learning experiences. However, the lack of guidelines on what should be taken into consideration during application of LA hinders its full adoption. Therefore, this article investigates the issues that should be considered when approaching the design of LA experiences from the data preparation perspective. The obtained results highlight a validated LA framework of twenty-two designing issues that should be considered by various stakeholders in different contexts as well as a set of guidelines which can enhance designing LA experiences.
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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.

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