NotesFAQContact Us
Collection
Advanced
Search Tips
Back to results
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1239705
Record Type: Journal
Publication Date: 2018-Dec
Pages: 7
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-2037-0849
EISSN: N/A
From Big Data to Learning Analytics for a Personalized Learning Experience
Dipace, Anna; Loperfido, F. Feldia; Scarinci, Alessia
Research on Education and Media, v10 n2 p3-9 Dec 2018
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.
Sciendo, a company of De Gruyter Poland. 32 Zuga Street., 01-811 Warsaw, Poland. Tel:+48-22-701-5015; e-mail: info@sciendo.com; Web site: https://www.sciendo.com
Publication Type: Journal Articles; Reports - Evaluative
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A