Conferences related to Online Learning Systems

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2020 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE Frontiers in Education Conference (FIE)

The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.


2020 IEEE Global Engineering Education Conference (EDUCON)

The IEEE Global Engineering Education Conference (EDUCON) 2020 is the eleventh in a series of conferences that rotate among central locations in IEEE Region 8 (Europe, Middle East and North Africa). EDUCON is one of the flagship conferences of the IEEE Education Society. It seeks to foster the area of Engineering Education under the leadership of the IEEE Education Society.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


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Periodicals related to Online Learning Systems

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


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Most published Xplore authors for Online Learning Systems

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Xplore Articles related to Online Learning Systems

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Toward a value framework for online learning systems

Proceedings of the 35th Annual Hawaii International Conference on System Sciences, 2002

Many universities and private corporations are investing significant capital in online learning initiatives. Willingness on the part of the student to take part in online learning is essential for success. This paper queries the student concerning the features of these systems that add value to their learning experience. A questionnaire is utilized for gathering student values associated with online learning ...


Extending TAM for online learning systems: An intrinsic motivation perspective

Tsinghua Science and Technology, 2008

To get a better understanding of user behavior towards online learning systems, the technology acceptance model (TAM) was extended to include an intrinsic motivational factor. An online survey posted on a campus BBS was conducted to collect research data with a total of 121 usable responses. The results support the motivational model and show that the explained variance of online ...


The impact of social media networks on enhancing students’ performance in online learning systems

Fifth International Conference on the Innovative Computing Technology (INTECH 2015), 2015

Many researchers said that determining learning styles enhance the learning development and makes learning easier for students. Learning management systems (LMS) are very successful in e-learning even though it does not include learning styles. As learning styles must be taken into consideration in LMS, students' behavior in online courses needs to be analyzed and observed. In the beginning we show ...


Towards the verification and validation of online learning systems: general framework and applications

37th Annual Hawaii International Conference on System Sciences, 2004. Proceedings of the, 2004

Online adaptive systems cannot be certified using traditional testing and proving methods, because these methods rely on assumptions that do not hold for such systems. In this paper, we discuss a framework for reasoning about online adaptive systems, and see how this framework can be used to perform the verification of these systems. In addition to the framework, we present ...


Leveraging Biometrics for User Authentication in Online Learning: A Systems Perspective

IEEE Systems Journal, 2009

With the rapid proliferation of online learning, students are increasingly demanding easy and flexible access to learning content at a time and location of their choosing. In these environments, remote users connecting via the public Internet or other unsecure networks must be authenticated prior to being granted access to sensitive content such as tests or personal/private records. Today, the overwhelming ...


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Educational Resources on Online Learning Systems

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IEEE.tv Videos

Ensemble Approaches in Learning
A Recurrent Crossbar of Memristive Nanodevices Implements Online Novelty Detection - Christopher Bennett: 2016 International Conference on Rebooting Computing
Dynamic Selection of Evolutionary Algorithm Operators Based on Online Learning and Fitness Landscape Metrics
Hyperdimensional Biosignal Processing: A Case Study for EMG-based Hand Gesture Recognition - Abbas Rahimi: 2016 International Conference on Rebooting Computing
Continuously Learning Neuromorphic Systems with High Biological Realism: IEEE Rebooting Computing 2017
Combinatorial Sleeping Bandits with Fairness Constraints - Bo Ji - IEEE Sarnoff Symposium, 2019
IEEE Expert Now
Advances in Kernel Methods
Localization Services for Online Common Operational Picture and Situation Awareness
IMS 2011 Microapps - Online Design
Overcoming the Static Learning Bottleneck - the Need for Adaptive Neural Learning - Craig Vineyard: 2016 International Conference on Rebooting Computing
ICASSP 2011 Trends in Machine Learning for Signal Processing
Learning with Memristive Neural Networks: Neuromorphic Computing - Joshua Yang at INC 2019
Deep Graph Learning: Techniques and Applications - Haifeng Chen - IEEE Sarnoff Symposium, 2019
LPIRC: A Facebook Approach to Benchmarking ML Workload
Brain-like Intelligence Inside - Towards Autonomously Interacting Systems
Designing Reconfigurable Large-Scale Deep Learning Systems Using Stochastic Computing - Ao Ren: 2016 International Conference on Rebooting Computing
IEEE Themes - Efficient networking services underpin social networks
Multiple Sensor Fault Detection and Isolation in Complex Distributed Dynamical Systems
Jean Camp: Calculating and Communicating Online Risk - Industry Forum Panel: WF IoT 2016

IEEE-USA E-Books

  • Toward a value framework for online learning systems

    Many universities and private corporations are investing significant capital in online learning initiatives. Willingness on the part of the student to take part in online learning is essential for success. This paper queries the student concerning the features of these systems that add value to their learning experience. A questionnaire is utilized for gathering student values associated with online learning systems and the organization supporting these technologies. Results from the survey and a literature review are organized in a proposed value framework for online learning course effectiveness.

  • Extending TAM for online learning systems: An intrinsic motivation perspective

    To get a better understanding of user behavior towards online learning systems, the technology acceptance model (TAM) was extended to include an intrinsic motivational factor. An online survey posted on a campus BBS was conducted to collect research data with a total of 121 usable responses. The results support the motivational model and show that the explained variance of online learning system use behavior is 71.30/0 higher than that of the original TAM explanation.

  • The impact of social media networks on enhancing students’ performance in online learning systems

    Many researchers said that determining learning styles enhance the learning development and makes learning easier for students. Learning management systems (LMS) are very successful in e-learning even though it does not include learning styles. As learning styles must be taken into consideration in LMS, students' behavior in online courses needs to be analyzed and observed. In the beginning we show that students with different learning styles act differently and process information in different ways. Secondly, a new technique for determining learning styles is suggested. Finally, we propose a framework for course personalization based on Felder-Silver man for each learning styles and how course presentation will differ according to the learning style.

  • Towards the verification and validation of online learning systems: general framework and applications

    Online adaptive systems cannot be certified using traditional testing and proving methods, because these methods rely on assumptions that do not hold for such systems. In this paper, we discuss a framework for reasoning about online adaptive systems, and see how this framework can be used to perform the verification of these systems. In addition to the framework, we present some preliminary results on concrete neural network models.

  • Leveraging Biometrics for User Authentication in Online Learning: A Systems Perspective

    With the rapid proliferation of online learning, students are increasingly demanding easy and flexible access to learning content at a time and location of their choosing. In these environments, remote users connecting via the public Internet or other unsecure networks must be authenticated prior to being granted access to sensitive content such as tests or personal/private records. Today, the overwhelming majority of online learning systems rely on weak authentication mechanisms to verify the identity of remote users only at the start of each session. One-time authentication using password, personal identification number (PIN), or even hardware tokens is clearly inadequate in that it cannot defend against insider attacks including remote user impersonation or illegal sharing or disclosure of these authentication secrets. As such, these methods are entirely unsuitable for circumstances where the outcome of an online assessment or a course of study is the granting of a formal degree, professional certification, or qualification or requalification for a particular skill or function. This paper examines the problem of remote authentication in online learning environments and explores the challenges and options of using biometric technology to defend against user impersonation attacks by certifying the presence of the user in front of the computer, at all times. It also leverages a 5-step process as the basis for a systems approach to ensuring that the proposed solution will meet the critical remote authentication assurance requirements. The process and systems approach employed here are generic, and can be exploited when introducing biometric-enabled authentication solutions to other applications and business domains. The paper concludes by presenting a biometrics-based client-server architecture for continuous user authentication in e-learning environments.

  • Learning path adaptation in online learning systems

    Learning path in online learning systems refers to a sequence of learning objects which are designated to help the students in improving their knowledge or skill in particular subjects or degree courses. In this paper, we review the recent research on learning path adaptation to pursue two goals, first is to organize and analyze the parameter of adaptation in learning path; the second is to discuss the challenges in implementing learning path adaptation. The survey covers the state of the art and aims at providing a comprehensive introduction to the learning path adaptation for researchers and practitioners.

  • Using Massive Processing and Mining for Modelling and Decision Making in Online Learning Systems

    Online Learning and Virtual Campuses have become commonplace paradigms for distance teaching and learning. Unlike face to face teaching and learning methods in which teachers and managers can take decisions based on information from everyday classroom activities, decision making in online learning becomes more complex due to the online setting. Teachers need to get information from the online learning system on the learning processes and learners' activities in order to better support them during the learning process. On the other hand, managers need information on the usage of computational resources of the Virtual Campus to make the computational infrastructure as much efficient as possible. In this work we will address the use of massive processing and data mining techniques to assist teachers, managers and developers of a Virtual Campus in their decision making, aiming to better support teaching and learning processes. Our approach is based on processing log files of the online learning system (Virtual Campus, specific learning platform, document repositories) which keep information on online users during their interaction with and within the system. Log files, which are nowadays commonplace in all learning management systems, tend to be large to very large in size, and thus require a massive processing and then statistical analysis and data mining techniques to extract useful information on user activities, resource usage in the Virtual Campus and web content access, among others.

  • User attention analysis for e-learning systems using gaze and speech information

    In this paper, a practical approach to detecting user attention for online learning systems using gaze and speech activity information is proposed. A portable camera and microphone system to acquire human-computer interaction data is used in this approach. User attentiveness is classified into three categories: attentive, inattentive-not looking, and inattentive-speaking. Experimental results show that the proposed system clearly differentiates the attention level of users that participate in e-learning sessions. The proposed system is suitable for realtime applications.

  • Metrics Development for Measuring Virtual University Social Responsibility

    Online learning systems in general and virtual universities in particular received considerable attention and had an increasing growth through two last decades. However, the acceptance and enough support from stakeholders sounds to be the main challenges of this kind of educational systems. Regarding this issue, the recent study is aimed to propose and validate a methodology for measuring social responsibility of virtual universities, by which higher education systems would be able to evaluate how each virtual university is responsible to its stakeholders and it can provide a useful scale to be used for ranking of this higher education institutions. The factors that comprise university social responsibility are inherently fuzzy and subjective in nature; therefore, in this research we propose to make use of Fuzzy Logic for measuring and quantifying this concept. Additionally in this research, based on the identified factors, we will develop ontological manifestations of social responsibility of virtual universities.

  • Learning Analytics to Adaptive Online IRT Testing Systems "Ai Arutte" Harmonized with University Textbooks

    Item response theory (IRT) provides more accurate and fairer evaluations of individual abilities than classical test theory does, and thus the IRT has gradually been recognized as one of the proper evaluation methodologies in many testing fields. Teaching using textbooks works in university education as well as self-studying using online learning systems. However, they have not been connected each other. Thus, to utilize both beneficial sides of textbooks and the internet system, we propose a new learning style using textbooks and online testing for exercises using the adaptive online IRT testing systems, called Ai Arutte. In this paper, we introduce a new use of the adaptive online IRT testing system to assist self-learning in studying undergraduate subject, Linear Algebra, and we show its learning analytics. The combination of a mathematical textbook and the adaptive online IRT system works well. Ai Arutte case shows that students feel this kind of self-studying is fun and interesting.



Standards related to Online Learning Systems

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