Testing, Evaluation and Performance of Optimization and Learning Systems

2002 ◽  
pp. 27-39 ◽  
Author(s):  
D. Whitley ◽  
J. P. Watson ◽  
A. Howe ◽  
L. Barbulescu
Keyword(s):  
Learning Systems ◽  
And Performance

scholarly journals A Model for e-Learning Systems Success: Systems, Determinants, and Performance

2017 ◽  
Vol 12 (10) ◽  
pp. 4 ◽  
Author(s):  
Ismail M. Romi
Keyword(s):  
Information And Communication Technologies ◽  
Learning Systems ◽  
Learning System ◽  
Learning Context ◽  
Learner Performance ◽  
Learning Success ◽  
Proposed Model ◽  
System Components ◽  
E Learning ◽  
And Performance

E-learning is used by higher education institutions and corporate training institutes as a means of solving performance problems, and the accessibility to educational technology which considered as vital for acquisition and dissemination of knowledge to students, as well as interaction between instructors and students. To determine technological solutions for those institutions, an analysis to the literature, and related theories have been conducted depending on the context impact to e-learning system, as well as the interrelationship between e-learning system components and its impact on learner performance. The main findings show that e-learning system is composed of four components, mainly; the instructor, learner, course, and information and communication technologies (ICT), in addition to the context determinants of e-learning system success. The current study, proposed a model for e-learning success, which incorporates eight factors, mainly; e-learning context that include individual, institutional, and environmental determinants to e-learning success. In addition to e-learning components which include instructor, learner, course, and ICT. As well as the learner performance, that aims to measure e-learning success. The proposed model was designed to integrate prior research in the area of e-learning, where it adds set of determinants to e-learning systems success, and find out the best fit of e-learning system components. Moreover, educational institutions can use this proposed model.

scholarly journals Formación y desarrollo de expertos: estrategias para el diseño de sistemas de aprendizaje y apoyo al desempeño / Expert training and development: strategies for learning systems design and performance support

2014 ◽  
Vol 10 (20) ◽  
pp. 17-24
Author(s):  
Lisette G. Reyes Paulino
Keyword(s):  
Professional Practice ◽  
Learning Theory ◽  
Cultural Context ◽  
Support Systems ◽  
Systems Design ◽  
Training And Development ◽  
Learning Systems ◽  
Performance Support ◽  
And Performance ◽  
Current Stage

Este artículo hace una revisión de la literatura existente sobre el desarrollo de la experticia humana, especialmente aplicada a la pedagogía universitaria y su relación con la teoría sociocognitiva del aprendizaje como fuente de estrategias para el diseño de sistemas de aprendizajes y apoyo al desempeño. Las estrategias identificadas pueden adaptarse a cualquier disciplina siempre y cuando se considere el contexto social y cultural específico en el que se realiza la práctica profesional o actividad, la naturaleza de la misma y la comprensión de la etapa actual de formación y desarrollo en el que se encuentra la persona.AbstractThis article reviews the existing literature on the development of human expertise, especially applied to university pedagogy and its relationship to the sociocognitive learning theory as a source of strategies for designing learning and performance support systems. This identified strategies can be adapted to any discipline as long as the specific social and cultural context, in which professional practice or activity is performed, is considered, the nature of it, and understanding of the current stage of training and development in which the person is.

The Corporate Learning Environment

2005 ◽  
pp. 1-38 ◽  
Author(s):  
Jerry Klein ◽  
Deniz Eseryel
Keyword(s):  
Learning Environment ◽  
Learning Environments ◽  
Rate Of Change ◽  
Learning Systems ◽  
Document Management ◽  
Multiple Systems ◽  
Corporate Learning ◽  
Making Sense ◽  
Knowledge Intensive ◽  
And Performance

Emerging technology has changed the focus of corporate learning systems from task-based, procedural training to knowledge-intensive problem-solving with deep conceptual learning. In addition, the deployment of open systems and distributing processing are adding new stresses to learning systems that can barely keep pace with the current rate of change. Learning environments to address these challenges a reviewed within a framework of the conventional learning curve, in which different learning elements are required to support different levels of expertise. An adaptive development model for creating and sustaining a learning environment is proposed that consists of the iterative application of three phases: (1) analysis and reflection, (2) architecture inception and revision, and (3) alignment. The model relies on the notion that analysis deals as much with synthesis and learning as it does with decomposition. We conclude that the concept of a “learning environment” provides a viable construct for making sense of the array of systems designed to support knowledge management, document management, e-learning, and performance support. A learning environment with a well-defined architecture can guide the convergence of multiple systems into a seamless environment providing access to content, multimedia learning modules, collaborative workspaces, and other forms of learning support. Finally, we see future learning environments consisting of networks of databases housing content objects, elegant access to the content, ubiquitous virtual spaces, and authoring tools that enable content vendors, guilds, and universities to rapidly develop and deliver a wide range of learning artifacts.

The Preliminary Investigation of the Factors that Influence the E-Learning Adoption in Higher Education Institutes

2010 ◽  
Vol 8 (4) ◽  
pp. 1-11 ◽  
Author(s):  
Maen Al-hawari ◽  
Sanaa Al-halabi
Keyword(s):  
High Performance ◽  
Preliminary Investigation ◽  
Learning Systems ◽  
Educational Institutions ◽  
Traditional Learning ◽  
Web Content ◽  
Analysis Software ◽  
E Learning ◽  
And Performance ◽  
Learning Factors

Creativity and high performance in learning processes are the main concerns of educational institutions. E-learning contributes to the creativity and performance of these institutions and reproduces a traditional learning model based primarily on knowledge transfer into more innovative models based on collaborative learning. In this paper, the authors focus on the preliminary investigation of factors that influence e-learning adoption in Jordan. As a pioneer country for e-learning systems in the Middle East, an investigation has been completed for one of Jordan’s universities that has implemented e-learning. Factors are defined through the analysis of unstructured interviews with developers and users of the e-learning systems, and Leximancer content analysis software is used to analyze the interview’s content. Main factors include Internet, legislations, human factors, and Web content.

Cloud-Based IoT Architecture for Green Buildings

2021 ◽  
pp. 73-87
Author(s):  
Rejo Rajan Mathew ◽  
Vikram Kulkarni
Keyword(s):  
Internet Of Things ◽  
Low Cost ◽  
Green Building ◽  
Learning Systems ◽  
Management Systems ◽  
Improve Design ◽  
Building Management ◽  
And Performance ◽  
Improved Learning ◽  
Building Management Systems

Green building (GB) is a game changer as the world is moving towards conserving its resources. Green building management systems available nowadays are too expensive and cannot cater to small or medium-sized buildings. Internet of things-based systems use simple, low-cost sensors, signal processing, and high-level learning methods. Studies on building occupancy and human activities help improve design and push the energy conservation levels. With huge amounts of data and improved learning systems, the impetus is to capture the information and use it well to improve design and justify the green building concept. Cloud-based architecture helps to monitor, capture, and process the data, which acts as input to intelligent learning systems, which in turn help to improve the design and performance of current green building management systems. This chapter discusses role of cloud-based internet of things architecture in improving design and performance of current building management systems.

Cloud-Based IoT Architecture for Green Buildings

2020 ◽  
pp. 61-75
Author(s):  
Rejo Rajan Mathew ◽  
Vikram Kulkarni
Keyword(s):  
Internet Of Things ◽  
Low Cost ◽  
Green Building ◽  
Learning Systems ◽  
Management Systems ◽  
Improve Design ◽  
Building Management ◽  
And Performance ◽  
Improved Learning ◽  
Building Management Systems

Green building (GB) is a game changer as the world is moving towards conserving its resources. Green building management systems available nowadays are too expensive and cannot cater to small or medium-sized buildings. Internet of things-based systems use simple, low-cost sensors, signal processing, and high-level learning methods. Studies on building occupancy and human activities help improve design and push the energy conservation levels. With huge amounts of data and improved learning systems, the impetus is to capture the information and use it well to improve design and justify the green building concept. Cloud-based architecture helps to monitor, capture, and process the data, which acts as input to intelligent learning systems, which in turn help to improve the design and performance of current green building management systems. This chapter discusses role of cloud-based internet of things architecture in improving design and performance of current building management systems.

The Preliminary Investigation of the Factors that Influence the E-Learning Adoption in Higher Education Institutes

2012 ◽  
pp. 64-74
Author(s):  
Maen Al-hawari ◽  
Sanaa Al-halabi
Keyword(s):  
High Performance ◽  
Preliminary Investigation ◽  
Learning Systems ◽  
Educational Institutions ◽  
Traditional Learning ◽  
Web Content ◽  
E Learning ◽  
Main Factors ◽  
And Performance ◽  
Learning Factors

Creativity and high performance in learning processes are the main concerns of educational institutions. E-learning contributes to the creativity and performance of these institutions and reproduces a traditional learning model based primarily on knowledge transfer into more innovative models based on collaborative learning. In this paper, the authors focus on the preliminary investigation of factors that influence e-learning adoption in Jordan. As a pioneer country for e-learning systems in the Middle East, an investigation has been completed for one of Jordan’s universities that has implemented e-learning. Factors are defined through the analysis of unstructured interviews with developers and users of the e-learning systems, and Leximancer content analysis software is used to analyze the interview’s content. Main factors include Internet, legislations, human factors, and Web content.

scholarly journals Introduction to Artificial Intelligence and Machine Learning for Pathology

2021 ◽  
Author(s):  
James H. Harrison ◽  
John R. Gilbertson ◽  
Matthew G. Hanna ◽  
Niels H. Olson ◽  
Jansen N. Seheult ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Monitoring ◽  
Model Development ◽  
Learning Systems ◽  
Professional Organizations ◽  
Current Status ◽  
Learning Tools ◽  
Recent Developments ◽  
And Performance ◽  
Additional Education

Context.— Recent developments in machine learning have stimulated intense interest in software that may augment or replace human experts. Machine learning may impact pathology practice by offering new capabilities in analysis, interpretation, and outcomes prediction using images and other data. The principles of operation and management of machine learning systems are unfamiliar to pathologists, who anticipate a need for additional education to be effective as expert users and managers of the new tools. Objective.— To provide a background on machine learning for practicing pathologists, including an overview of algorithms, model development, and performance evaluation; to examine the current status of machine learning in pathology and consider possible roles and requirements for pathologists in local deployment and management of machine learning systems; and to highlight existing challenges and gaps in deployment methodology and regulation. Data Sources.— Sources include the biomedical and engineering literature, white papers from professional organizations, government reports, electronic resources, and authors' experience in machine learning. References were chosen when possible for accessibility to practicing pathologists without specialized training in mathematics, statistics, or software development. Conclusions.— Machine learning offers an array of techniques that in recent published results show substantial promise. Data suggest that human experts working with machine learning tools outperform humans or machines separately, but the optimal form for this combination in pathology has not been established. Significant questions related to the generalizability of machine learning systems, local site verification, and performance monitoring remain to be resolved before a consensus on best practices and a regulatory environment can be established.

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