LA – Learning Analytics

4th  International Symposium Learning Analytics

Learning Analytics refers to the measurement, collection, analysis and reporting of data on the progress of learners and the micro and macro contexts of learning environment. It uses the data collected from the digital footprint of the learner and applies bigdata concepts to understand the influencing factors that affects learners whether it is educational policy and strategy, or it relates to the micro level module contents and its delivery. This has potential to support designing data and fact-based policy both for campus based and online delivery.

Through Learning Analytics, it is possible to provide tailor-made learning and teaching for a specific group. It can lead to reduce disparity of standard within different systems and optimise the resources required to achieve required standards. Learning Analytics can support the prediction of outcome and guide early intervention to boosting retention rates. It will make significant contribution for quality assurance.

When combined with refined visualization tools and suitable interfaces in an e-learning system, the use of Learning Analytics fosters a student-centred approach, supports self-regulated learning, and eventually helps learners (and teachers) along the road to educational success. This research area is witnessing swift developments since several years, and this Symposium aims to gather contributions to allow for exchange and further advancements. Technology Enhanced Learning advocates are invited to submit their original research work involving the use of Learning Analytics in education. The topics of interests include but are not limited to:

Learning & Teaching tracking

– Temporal Analysis of Learning Data
– Tool for quality assurance and quality improvement
– Tool for boosting retention rate
– Tool for adaptive learning
– Tool to improve quality of teaching

Technical infrastructure

 – E-Learning
– M-Learning
– Game Based Learning
– Personalized and Adaptive Learning
– Learning analytics warehouse
– Learning analytics processor
– Alert and intervention system
– Dashboards, and a student app
– Machine & Deep-learning Analytics

Issues in Data-Based Educational Theories and models

 – Educational Data Model
– Data-based methodology for research in education
– Ethics and privacy in learning analytic

Please check the submission procedures @ the submission page.

General enquiries and submissions should be addressed to the Conference Co-ordinator

Symposium enquiries specific should be addressed to:

Marco Temperini, Sapienza University, Rome, IT
marte (@)

Filippo Sciarrone, Sapienza University, Rome, IT
sciarro (@)

Minoru Nakayama, Information and Communications Engineering, Tokyo Institute of Technology, Japan, nakayama (@)

Tania Di Mascio, University of L’Aquila, IT
tania.dimascio (@)