Blended Learning in Smart Learning Environments

2019 ◽  
pp. 62-67 ◽  
Author(s):  
Peter Mikulecky
Keyword(s):  
Blended Learning ◽  
Learning Environments ◽  
Smart Learning ◽  
Smart Learning Environments

scholarly journals A conceptual framework for designing smart learning environments

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Sirkka Freigang ◽  
Lars Schlenker ◽  
Thomas Köhler
Keyword(s):  
Conceptual Framework ◽  
Learning Environments ◽  
Smart Learning ◽  
Smart Learning Environments

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.

scholarly journals Technology-enhanced assessment visualization for smart learning environments

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Hiran Ferreira ◽  
Guilherme P. de Oliveira ◽  
Rafael Araújo ◽  
Fabiano Dorça ◽  
Renan Cattelan
Keyword(s):  
Learning Environments ◽  
Behavioral Changes ◽  
Educational Systems ◽  
Smart Learning Environment ◽  
Pedagogical Strategies ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Related Information ◽  
Student Models ◽  
Level Of Knowledge

AbstractIn Smart Learning Environments, students need to be aware of their academic performance so they can self-regulate their learning process. Likewise, the teaching process can also be improved if instructors are able to supervise the progress of students, both individually and globally, and anticipate proper pedagogical strategies. Thus, effective Student Models, capable of identifying and predicting the level of knowledge of students, are a key requirement in modern educational systems. In this article, we revisit OSM-V, an Open Student Model with Information Visualization capabilities that allow students and instructors to assess performance-related information in educational systems. We detail its architecture and how it was integrated into Classroom eXperience, a Smart Learning Environment with multimedia capture capabilities. We also present extended results from experiments that evaluate both the perception of utility and behavioral changes in students who used OSM-V, showing that it can positively impact students’ learning and positively influence their study habits.

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