Open AccessFrom Big Data to Learning Analytics for a personalized learning experience
Author(s) -
Anna Dipace,
Fedela Feldia Loperfido,
Alessia Scarinci
Publication year - 2018
Publication title -
rem
Language(s) - English
Resource type - Journals
eISSN - 2037-0849
pISSN - 2037-0830
DOI - 10.1515/rem-2018-0009
Subject(s) - formative assessment , learning analytics , computer science , personalized learning , consistency (knowledge bases) , curriculum , set (abstract data type) , analytics , knowledge management , data science , teaching method , open learning , mathematics education , artificial intelligence , cooperative learning , psychology , pedagogy , programming language
This article describes Learning Analytics (LA) as a predictive and formative approach that enables the planning of educational scenarios in line with students’ needs and languages in order to set a priori and in progress systems of control and inspection of the following: consistency, relevance and effectiveness of training objectives, curriculum paths, students’ needs and learning outcomes. Thanks to LA, it is possible to understand how students learn. Training courses are designed to include the definition of those learning outcomes that respond effectively to students’ needs in terms of contents, methodologies, tools and teaching resources. The present article aims to describe and discuss, after reviewing the relevant literature, in what way LA represents a valid support not only in designing student-centred training courses, which assess outcomes, but also in carrying out a formative assessment considering the learning experience as a whole. The analysis of some case studies was a good opportunity to reflect and define the bridge existing between the use of LA for assessment purposes and personalized learning paths.