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

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

Ahmed Tlili (Smart Learning Institute of Beijing Normal University, Beijing, China), Fathi Essalmi (Research Laboratory of Technologies of Information and Communication & Electrical Engineering (LaTICE), Tunis Higher School of Engineering (ENSIT), University of TUNIS, Tunis, Tunisia), Mohamed Jemni (Research Laboratory of Technologies of Information and Communication & Electrical Engineering (LaTICE), Tunis Higher School of Engineering (ENSIT), University of TUNIS, Tunis, Tunisia), Professor Kinshuk (University of North Texas, Denton, USA) and Nian-Shing Chen (Department of Applied Foreign Languages, National Yunlin University of Science and Technology, Douliou, Taiwan)
DOI: 10.4018/IJICTE.2019070104

Abstract

Advances in technology have given the learning analytics (LA) area further potential to enhance the learning process by using methods and techniques that harness educational data. However, the lack of guidelines on what should be taken into considerations during application of LA hinders its full adoption. Therefore, this article investigates the issues that should be considered during the design of LA experience from the data use perspective. The results obtained present a validated LA framework which is composed of eighteen validated key issues that should be considered by various stakeholders in their contexts to enhance designing LA experiences. This framework can also be used by researchers and practitioners to learn more about LA and its designing issues.
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2. Literature Review

Learning analytics is rooted in data science, artificial intelligence, practices of recommender systems and business intelligence. Cooper (2012) suggested that a single definition of LA is impossible because of the broad range of perspectives and motivations involved. For instance, 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.”

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