scholarly journals Smart Learning Environment (SLE) in the Fourth Industrial Revolution (IR 4.0)

2022 ◽  
Vol 13 (2) ◽  
pp. 1-23
Author(s):  
Dedi Mulyadi ◽  
Miftachul Huda ◽  
Islah Gusmian
Keyword(s):  
Online Learning ◽  
Industrial Revolution ◽  
Digital Learning ◽  
Smart Learning Environment ◽  
Smart Learning ◽  
Framework Model ◽  
Main Basis ◽  
Online Learning Resources ◽  
Normal Trend ◽  
Search Program

This paper is attempted to examine the explanatory approach in dealing with SLE by advancing online learning sources. The systematic approach of searching for the relevant articles on SLE in IR 4.0 has been widely identified through two electronic databases, Scopus and Web of Sciences. Through adopting such digitally systematic search program, identification was made on the various elements in terms of online learning resources (OLR). This attempts to propose the SLE framework model with an innovative approach in enhancing the learning through incorporating IR 4.0 platform to utilize the variety of information sources together with knowledge attribution in the higher education (HE). The contribution provides theoretical framework with the guideline of well-adapted performance in the educational activities as the new normal trend. In achieving this attainment, the readiness of both instruction facilities and accessibility procedure is significantly the main basis in ensuring the process flow in enlarging the digital learning.

scholarly journals IMPROVING ADAPTIVE LEARNING IN A SMART LEARNING ENVIRONMENT

2021 ◽  
Vol 2 ◽  
pp. 93-99
Author(s):  
Gilberto Marzano ◽  
Anda Abuze ◽  
Yeliz Nur
Keyword(s):  
Learning Environment ◽  
Adaptive Learning ◽  
Low Cost ◽  
Educational Interventions ◽  
Digital Learning ◽  
Smart Learning Environment ◽  
Smart Learning ◽  
Unit Structure ◽  
Teaching Learning ◽  
Near Future

It has been broadly argued that, in the near future, the demand for skilled labor will increase whilst that for routine activities will decrease. In this regard, the need for making greater investments in education to re-skill workers and support continuous learning has been invoked as an essential requirement for preserving people’s employability.Digital technology is deemed increasingly necessary to sustain the educational endeavor, for the possibilities it offers to make more accessible and low-cost educational interventions. It allows for the creation of personalized learning paths and customized digital learning solutions, for courses to be available to a large attendance of learners, and for teaching-learning activities to be offered at significantly reduced cost.In this article, a learning unit structure designed to improve adaptive learning is proposed, and mechanisms for adaptive learning in a smart learning environment are discussed.The implemented teaching-learning solution is also illustrated. This is a preliminary application based on an approach that combines the teacher experience with learning analytics. 

scholarly journals Teaching System of Smart Learning Environment for Aerobics Course

2018 ◽  
Vol 13 (05) ◽  
pp. 165 ◽  
Author(s):  
Yanan Yu ◽  
Aili Qi
Keyword(s):  
Learning Environment ◽  
University Education ◽  
Independent Learning ◽  
Fuzzy Cognitive Map ◽  
Digital Learning ◽  
Smart Learning Environment ◽  
Teaching System ◽  
Smart Learning ◽  
University Course ◽  
The University

In the past digital learning environment, we required the multimedia facilities excessively and ignored the individual needs of learners, and the teaching resources existed depending on the equipment. The emergence of smart learning environment can meet people's independent learning, customized learning, smart learning and other requirements. Meanwhile, the “intelligence" of smart learning environment conforms to the teaching features of aerobics and other body-shaping courses, which can cultivate the learners’ innovation abilities and further optimize the teaching effect. In this paper, on the basis of the theory construction of smart learning environment, we designed a teaching system of smart learning environment for the university course Aerobics based on the Fuzzy Cognitive Map (FCM), conducted the application practice of the smart learning environment in the teaching of the university course Aerobics by the controlled experiment method, analyzed the difference of teaching effect in and out of a smart learning environment, and finally drew a conclusion, in order to provide some theoretical and data support for the application of smart learning environment in the teaching of physical education (PE) majors and other university education.

scholarly journals Activity Monitoring in the Digital Learning Environment of a Modern University: Technological Tasks of Psychological Support

2021 ◽  
Vol 23 (1) ◽  
pp. 156-165
Author(s):  
E. V. Bredun ◽  
Т. A. Vaulina ◽  
V. A. Shamakov ◽  
E. A. Shcheglova
Keyword(s):  
Learning Environment ◽  
Activity Monitoring ◽  
Psychological Support ◽  
Digital Learning ◽  
Smart Learning Environment ◽  
Research Results ◽  
Digital Learning Environment ◽  
Smart Learning ◽  
Distant Learning ◽  
Digital Footprints

The paper reviews the existing approaches to using digital footprints in the digital learning environment. Monitoring digital footprints of university students can help to design smart learning environment and predict models of interaction between this environment and the user. The article covers the main analysis tools that can be applied to activity monitoring in LMS Moodle, including datasets as a convenient resource for distant learning. The authors studied authentication techniques that are based not on one’s knowledge but on the confirmation of one’s digital profile. The research results revealed some personal styles and patterns of cognitive behavior that reflect students’ work in the digital learning environment. The research results can be used to develop new psychological support of activity monitoring of the digital university environment, as well as to create new effective cognitive user-friendly interfaces.

scholarly journals Determinants of Active Online Learning in the Smart Learning Environment: An Empirical Study with PLS-SEM

2021 ◽  
Vol 13 (17) ◽  
pp. 9923
Author(s):  
Shaofeng Wang ◽  
Gaojun Shi ◽  
Mingjie Lu ◽  
Ruyi Lin ◽  
Junfeng Yang
Keyword(s):  
Online Learning ◽  
Learning Environment ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Smart Learning Environment ◽  
Smart Learning ◽  
Intelligent Interaction ◽  
Acceptance Model

A smart learning environment, featuring personalization, real-time feedback, and intelligent interaction, provides the primary conditions for actively participating in online education. Identifying the factors that influence active online learning in a smart learning environment is critical for proposing targeted improvement strategies and enhancing their active online learning effectiveness. This study constructs the research framework of active online learning with theories of learning satisfaction, the Technology Acceptance Model (TAM), and a smart learning environment. We hypothesize that the following factors will influence active online learning: Typical characteristics of a smart learning environment, perceived usefulness and ease of use, social isolation, learning expectations, and complaints. A total of 528 valid questionnaires were collected through online platforms. The partial least squares structural equation modeling (PLS-SEM) analysis using SmartPLS 3 found that: (1) The personalization, intelligent interaction, and real-time feedback of the smart learning environment all have a positive impact on active online learning; (2) the perceived ease of use and perceived usefulness in the technology acceptance model (TAM) positively affect active online learning; (3) innovatively discovered some new variables that affect active online learning: Learning expectations positively impact active online learning, while learning complaints and social isolation negatively affect active online learning. Based on the results, this study proposes the online smart teaching model and discusses how to promote active online learning in a smart environment.

scholarly journals EduCOR: An Educational and Career-Oriented Recommendation Ontology

2021 ◽  
pp. 546-562
Author(s):  
Eleni Ilkou ◽  
Hasan Abu-Rasheed ◽  
Mohammadreza Tavakoli ◽  
Sherzod Hakimov ◽  
Gábor Kismihók ◽  
...  
Keyword(s):  
Online Learning ◽  
Labour Market ◽  
Real Life ◽  
Learning Goals ◽  
Learning Material ◽  
Learning Resources ◽  
Digital Learning ◽  
Personalised Learning ◽  
Learning Path ◽  
Online Learning Resources

AbstractWith the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user’s learning goals and preferences, academic and psychological parameters, and labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations.Resource: https://tibonto.github.io/educor/.

Big Data Cognitive Storage: An Optimum Way of handling the Educational Data in Indian Scenario

2017 ◽  
Vol SED2017 (01) ◽  
pp. 5-7
Author(s):  
Ruchi Jain ◽  
Neelesh Kumar Jain
Keyword(s):  
Big Data ◽  
Online Learning ◽  
Online Education ◽  
Data Protection ◽  
Teaching And Learning ◽  
Relevant Information ◽  
Indian Society ◽  
Digital Learning ◽  
Modern Education ◽  
Indian Scenario

The concept of big data has been incorporated in majority of areas. The educational sector has plethora of data especially in online education which plays a vital in modern education. Moreover digital learning which comprises of data and analytics contributes significantly to enhance teaching and learning. The key challenge for handling such data can be a costly affair. IBM has introduced the technology "Cognitive Storage" which ensures that the most relevant information is always on hand. This technology governs the incoming data, stores the data in definite media, application of levels of data protection, policies for the lifecycle and retention of different classes of data. This technology can be very beneficial for online learning in Indian scenario. This technology will be very beneficial in Indian society so as to store more information for the upliftment of the students’ knowledge.

scholarly journals COVID-19 pandemic’s disruption on university teaching and learning and competence cultivation: Student evaluation of online learning experiences in Hong Kong

2021 ◽  
Vol 10 (1) ◽  
pp. 221258682110070
Author(s):  
Ka Ho Mok ◽  
Weiyan Xiong ◽  
Hamzah Nor Bin Aedy Rahman
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Hong Kong ◽  
Social Presence ◽  
Online Teaching ◽  
Teaching And Learning ◽  
Student Evaluation ◽  
Learning Experiences ◽  
University Teaching ◽  
Digital Learning

The COVID-19 pandemic outbreak has forced online teaching and learning to be the primary instruction format in higher education globally. One of the worrying concerns about online learning is whether this method is effective, specifically when compared to face-to-face classes. This descriptive quantitative study investigates how students in higher education institutions in Hong Kong evaluated their online learning experiences during the pandemic, including the factors influencing their digital learning experiences. By analysing the survey responses from 1,227 university students in Hong Kong, this study found that most of the respondents felt dissatisfied with their online learning experiences and effectiveness. Meanwhile, this study confirms that respondents’ household income level and information technology literacy affected their online learning effectiveness. Moreover, this study highlights the significant contributions of the community of inquiry, which places social presence on the promotion of a whole person development that could not be achieved when relying mainly on online learning. Findings encourage university leaders and instructors to search for multiple course delivery modes to nurture students to become caring leaders with the 21st century skills and knowledge set.

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