scholarly journals Teaching through urban sensorium: urban spatiality as a smart learning environment

2022 ◽  
Vol 9 (1) ◽  
Author(s):  
Gaana Jayagopalan ◽  
Sweta Mukherjee
Keyword(s):  
Learning Environments ◽  
Urban Studies ◽  
Real Life ◽  
Urban Spaces ◽  
Built Environments ◽  
Smart Learning Environment ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Bangalore City ◽  
Sensory Learning

AbstractThis paper qualitatively analyses the implication of urban sensorium as a pedagogic mode in the teaching of Urban Studies. Underpinned by the frames of smart learning environments, the paper reiterates experiencing urban ontologies as spatial learning environments. By drawing from a range of transdisciplinary and experiential modes of learning, this paper maps how an undergraduate course on Bangalore city in India served learners to critically engage with and experience spatial urban ontologies both digitally, and in real-world experiences of learning, furthering learner autonomy and reflection. The methodological prisms of this paper are autoethnography and critical reflection. It is organised around enabling learners recognize the experiential, embodied urban spaces through the urban sensorium via real-life engagements with urban spaces, and creation of digital portfolios that map this learning. Findings from the learners’ knowledge of sensory learning, the city’s intersectional aspects, and the student’s embodied and emplaced self in built environments and digital spaces are analysed via cognitive and affective-reflection levels; the course instructor's reflection is analysed via a process-reflection level. These reflections hold implications for the pedagogy of urban studies in undergraduate classrooms by foregrounding spatiality and urban sensorium as significant critical and affective pedagogic tools. The paper has also accommodated critical engagement with an external faculty member as a co-author, in order to manage any bias or researcher subjectivity in the design.

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.

scholarly journals Blockchain Technology Adoption in Smart Learning Environments

2021 ◽  
Vol 13 (4) ◽  
pp. 1801 ◽  
Author(s):  
Nazir Ullah ◽  
Waleed Mugahed Al-Rahmi ◽  
Ahmed Ibrahim Alzahrani ◽  
Osama Alfarraj ◽  
Fahad Mohammed Alblehai
Keyword(s):  
Technology Acceptance ◽  
Learning Environments ◽  
Technology Acceptance Model ◽  
Online Survey ◽  
Equation Modeling ◽  
Smart Learning Environment ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Blockchain Technology ◽  
Acceptance Model

The conventional education system in developing countries has been enhanced recently by implementing the latest technology of distributed ledger. Disruptive technology is a fundamental requirement for greater accountability and visibility. We explored the key factors affecting the intentions of educational institutions to use blockchain technology for e-learning. This study proposed an expanded model of Technology Acceptance Model by integrating the diffusion of innovation theory. Based on an online survey, the conceptual model was tested and validated using structural equation modeling. The results showed that compatibility had a significant impact on blockchain use in smart learning environments. Other significant effects were also found on adoption of blockchain technology. This study offers an expanded Technology Acceptance Model for implementing blockchain that could assist decision makers in building a smart learning environment for the educational institutes for the emerging economies.

scholarly journals Scientific production and thematic breakthroughs in smart learning environments: a bibliometric analysis

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Friday Joseph Agbo ◽  
Solomon Sunday Oyelere ◽  
Jarkko Suhonen ◽  
Markku Tukiainen
Keyword(s):  
Learning Environments ◽  
Bibliometric Analysis ◽  
Adaptive Learning ◽  
The United States ◽  
Scientific Production ◽  
Comprehensive Overview ◽  
Scientific Publications ◽  
Smart Learning Environment ◽  
Smart Learning ◽  
Smart Learning Environments

AbstractThis study examines the research landscape of smart learning environments by conducting a comprehensive bibliometric analysis of the field over the years. The study focused on the research trends, scholar’s productivity, and thematic focus of scientific publications in the field of smart learning environments. A total of 1081 data consisting of peer-reviewed articles were retrieved from the Scopus database. A bibliometric approach was applied to analyse the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of smart learning environments. The result from this bibliometric analysis indicates that the first paper on smart learning environments was published in 2002; implying the beginning of the field. Among other sources, “Computers & Education,” “Smart Learning Environments,” and “Computers in Human Behaviour” are the most relevant outlets publishing articles associated with smart learning environments. The work of Kinshuk et al., published in 2016, stands out as the most cited work among the analysed documents. The United States has the highest number of scientific productions and remained the most relevant country in the smart learning environment field. Besides, the results also showed names of prolific scholars and most relevant institutions in the field. Keywords such as “learning analytics,” “adaptive learning,” “personalized learning,” “blockchain,” and “deep learning” remain the trending keywords. Furthermore, thematic analysis shows that “digital storytelling” and its associated components such as “virtual reality,” “critical thinking,” and “serious games” are the emerging themes of the smart learning environments but need to be further developed to establish more ties with “smart learning”. The study provides useful contribution to the field by clearly presenting a comprehensive overview and research hotspots, thematic focus, and future direction of the field. These findings can guide scholars, especially the young ones in field of smart learning environments in defining their research focus and what aspect of smart leaning can be explored.

scholarly journals Smart learning environments in a contemporary museum: a case study

2020 ◽  
Vol 11 (2) ◽  
pp. 353-375
Author(s):  
Judita Kasperiuniene ◽  
Ilona Tandzegolskiene
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Time Management ◽  
Personal Experience ◽  
Historical Narrative ◽  
Smart Learning Environment ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
The Creation

Aim. The modern museum becomes an attractive learning place and space where the visitor, depending on age and competence, develops personal experience, and constructs the learning process based on personalized goals. The article aims to reveal how spaces in museums are exploited, in what ways visitors are involved in a narrative that connects the present and the past. Concept. The research uses a case-study method to investigate the POLIN Museum of the History of Polish Jews (Poland), Ruhr Museum (Germany), and Vienna Technical Museum (Austria). Within the smart learning environment context, this study explains how to encourage museum visitors to learn and seek answers. Results and conclusion. Four main directions are emphasized: the construction of a narrative through the creation of spaces and places, the creation of a historical narrative through simulacra, the educational effect of smart solutions, and the edutainment. The findings show that change in the museum by combining design solutions, historical narrative, time experience, and smart technologies leads to cognitive, engaging learning, touching, feeling, and experiencing different emotions, encouraging a return to the museum, inviting to learn, and shaping one's personal experience. Cognitive value. Contemporary museums invite visitors to a new experience combining artistic space design, storytelling, individual time management, and the use of smart learning environments. These challenges are shifting museum narratives and influencing non-formal learning programs. Authors raise a discussion of how, by exploiting museum spaces, the visitors are involved in the stories, and how the smart learning environment is created in a modern museum.

Diverse Applications of the Elements of Smart Learning Environments

2019 ◽  
pp. 118-141
Author(s):  
Edward Robeck ◽  
Shriram Raghunathan ◽  
Abtar Darshan Singh ◽  
Bibhya Sharma
Keyword(s):  
Learning Environments ◽  
Internet Technology ◽  
Learning Approaches ◽  
Smart Learning Environment ◽  
Effective Learning ◽  
Smart Learning ◽  
Smart Learning Environments ◽  
Design And Implementation ◽  
Design Of Learning Environments ◽  
New Generation

The design of learning environments has greatly influenced learning approaches and strategies, and has traditionally been considered to exist within the physical walls of a learning institution. In recent years, learning environments have evolved alongside advances in the internet, technology, and mobile devices and have given rise to smart learning environments to better accommodate a new generation of learners and learning behaviors. This chapter presents an exploration of the possibilities of smart learning environments in distinct and diverse environments, across varying learner locations, profiles, and demographics. The authors explore and analyze technology and pedagogy elements that make up an effective smart learning environment, through different cases and viewpoints of the contributing authors of this book. Based on the findings, they propose a framework for the design and implementation of smart learning environments that will effectively create engaging, personalized, and effective learning moments for individual learners.

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