Learning Analytics for Smart Learning Environments: A Meta-Analysis of Empirical Research Results from 2009 to 2015

2016 ◽  
pp. 1-23 ◽  
Author(s):  
Zacharoula Papamitsiou ◽  
Anastasios A. Economides
Keyword(s):  
Learning Environments ◽  
Empirical Research ◽  
Learning Analytics ◽  
Meta Analysis ◽  
Research Results ◽  
Smart Learning ◽  
Smart Learning Environments

scholarly journals The PERLA Framework: Blending Personalization and Learning Analytics

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Mohamed Amine Chatti ◽  
Arham Muslim
Keyword(s):  
Learning Environments ◽  
Learning Analytics ◽  
Theoretical Foundation ◽  
Personalized Learning ◽  
Systematic Design ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Learning Contexts ◽  
Customized Learning ◽  
Opening Up

Personalization is crucial for achieving smart learning environments in different lifelong learning contexts. There is a need to shift from one-size-fits-all systems to personalized learning environments that give control to the learners. Recently, learning analytics (LA) is opening up new opportunities for promoting personalization by providing insights and understanding into how learners learn and supporting customized learning experiences that meet their goals and needs. This paper discusses the Personalization and Learning Analytics (PERLA) framework which represents the convergence of personalization and learning analytics and provides a theoretical foundation for effective analytics-enhanced personalized learning. The main aim of the PERLA framework is to guide the systematic design and development of effective indicators for personalized learning.

Open Learner Models in Smart Learning Environments

2019 ◽  
pp. 346-368 ◽  
Author(s):  
Angeliki Leonardou ◽  
Maria Rigou ◽  
John D. Garofalakis
Keyword(s):  
Learning Environments ◽  
Adaptive Learning ◽  
Real Life ◽  
Self Reflection ◽  
Learner Model ◽  
Self Regulated Learning ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Navigation Support ◽  
Adaptive Learning Systems

Smart learning environments (SLEs), like all adaptive learning systems, are built around the learner model and use it to support a variety of interventions such as mastery learning, scaffolding, adaptive sequencing, and adaptive navigation support. Open learner models (OLMs) “expose” the learner data to users through easily perceivable visual representations aiming to improve student self-reflection and self-regulated learning and also increase user motivation and even foster collaboration. This chapter presents the evolution and current state of OLMs, summarizes related research in the field emphasizing on OLM types, locus of control between the system and the user and visualizations categorized on the basis of quantized/continuous and structured/unstructured representations. OLM cases implementing typical SLEs features are described, along with representative real-life scenarios of incorporating OLMs in SLEs. Moreover, the chapter provides guidelines for designing effective OLMs and discusses current research trends in this active scientific field.

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