Learning analytics in distance and mobile learning for designing personalised software

Abstract

Distance Learning, in its synchronous and asynchronous form, has gained an increasing interest over the last decades, both because of the realization of the “digital era” and also due to the reachability and accessibility it offered to human education. Lately, mobile learning has also been gaining a lot of interest due to the widespread popularity of Smartphones. In order to improve human educational interaction with Personal Computers and Smartphones, collecting learning analytics data and utilizing them is considered as a valuable requirement. Distance and mobile learning analytics may improve and assist the entire learning process by providing personalized software solutions. This paper focuses on the collection and the combination of the learning analytics data offered by different modalities in Personal Computers and modern Smartphones. For this combination two different Multi-Criteria Decision making theories are used, namely the Analytical Hierarchy Process and the Simple Additive Weighting model.