Subjects -> EDUCATION (Total: 2346 journals)
    - ADULT EDUCATION (24 journals)
    - COLLEGE AND ALUMNI (10 journals)
    - E-LEARNING (38 journals)
    - EDUCATION (1996 journals)
    - HIGHER EDUCATION (140 journals)
    - ONLINE EDUCATION (42 journals)
    - SCHOOL ORGANIZATION (14 journals)

ONLINE EDUCATION (42 journals)

Showing 1 - 41 of 41 Journals sorted alphabetically
Aprendo con NooJ     Open Access   (Followers: 1)
Asian Association of Open Universities Journal     Open Access   (Followers: 1)
Asian Journal of Distance Education     Open Access   (Followers: 2)
Campus Virtuales     Open Access   (Followers: 3)
Current Issues in Emerging eLearning     Open Access   (Followers: 11)
Designs for Learning     Open Access   (Followers: 12)
Digital Education Review     Open Access   (Followers: 7)
Edu Komputika Journal     Open Access   (Followers: 1)
Edutec : Revista Electrónica de Tecnología Educativa     Open Access  
European Journal of Open, Distance and E-Learning     Open Access   (Followers: 4)
ICT Learning     Open Access   (Followers: 4)
IEEE Transactions on Learning Technologies     Full-text available via subscription   (Followers: 12)
International Journal of Computer-Assisted Language Learning and Teaching     Full-text available via subscription   (Followers: 14)
International Journal of Cyber Ethics in Education     Full-text available via subscription   (Followers: 2)
International Journal of Educational Technology     Open Access   (Followers: 10)
International Journal of Educational Technology in Higher Education     Open Access   (Followers: 26)
International Journal of Game-Based Learning     Hybrid Journal   (Followers: 17)
International Journal of Mobile and Blended Learning     Full-text available via subscription   (Followers: 25)
International Journal of Mobile Learning and Organisation     Hybrid Journal   (Followers: 16)
International Journal of Virtual and Personal Learning Environments     Full-text available via subscription   (Followers: 18)
International Journal of Web-Based Learning and Teaching Technologies     Hybrid Journal   (Followers: 20)
International Journal on Advances in ICT for Emerging Regions (ICTer)     Open Access   (Followers: 2)
International Review of Research in Open and Distance Learning     Open Access   (Followers: 24)
International Technology and Education Journal     Open Access   (Followers: 1)
Irish Journal of Academic Practice     Open Access   (Followers: 1)
Irish Journal of Technology Enhanced Learning     Open Access   (Followers: 2)
Journal of Computers in Education     Hybrid Journal   (Followers: 9)
Journal of Digital Learning in Teacher Education     Hybrid Journal   (Followers: 25)
Journal of Learning and Teaching in Digital Age     Open Access   (Followers: 6)
Journal of Learning for Development     Open Access   (Followers: 4)
Journal of Open, Flexible and Distance Learning     Open Access   (Followers: 29)
Journal of Research on Technology in Education     Hybrid Journal   (Followers: 16)
Journal of Teaching and Learning with Technology     Open Access   (Followers: 10)
Multicultural Education Review     Hybrid Journal   (Followers: 8)
Networks : An Online Journal for Teacher Research     Open Access   (Followers: 1)
OUSL Journal     Open Access  
Research and Practice in Technology Enhanced Learning     Open Access   (Followers: 7)
Revista Interuniversitaria de Investigación en Tecnología Educativa     Open Access  
Smart Learning Environments     Open Access   (Followers: 6)
Teknodika     Open Access  
Theory and methods of e-learning     Open Access   (Followers: 11)
Similar Journals
Journal Cover
International Journal on Advances in ICT for Emerging Regions (ICTer)
Number of Followers: 2  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1800-4156 - ISSN (Online) 2550-2794
Published by Sri Lanka Journals Online Homepage  [71 journals]
  • Estimating the Effects of Text Genre, Image Resolution and Algorithmic
           Complexity needed for Sinhala Optical Character Recognition

    • Abstract: While optical character recognition for Latin based scripts have seen near human quality performance, the accuracy for the rounded scripts of South Asia still lags behind. Work on Sinhala OCR has mainly reported on performance on constrained classes of font faces and so been inconclusive. This paper provides a comprehensive series of experiments using conventional machine learning as well as deep learning on texts and font faces of diverse types and in diverse resolutions, in order to present a realistic estimation of the complexity of recognizing the rounded script of Sinhala. While texts of both old and contemporary books can be recognized with over 87% accuracy, those in old newspapers are much harder to recognize owing to poor print quality and resolution. Published on 2021-08-04 00:00:00
  • Don’t Forget to Include that Camera in the Threat Model: Vulnerability
           of ATM Systems due to Surveillance Cameras

    • Abstract: Video Surveillance Systems (VSS) that are used to provide physical protection to assets and personnel of organizations open up new information channels, but they are often not considered an integral part of the organization's information system. Therefore, more often than not, VSS is not considered when designing and evaluating organizations' information security. Hence, a VSS may weaken the information security of an organization while strengthening physical security. We present such a threat that the VSS used in ATM kiosks of Sri Lankan banks can severely weaken the ATM PIN security due to the ad hoc placement of cameras. While we have observed that in some installations, the video camera directly captures the PIN-pad, we show that forearm movements' visibility is sufficient to infer PINs with a significant level of accuracy. We used a mock-up of an ATM kiosk for our analysis, and we show that a human observer can guess a PIN with 22.5% accuracy within 3 attempts without the PIN Pad's visuals. A computer can infer the PIN using the same footages with an accuracy of 50% using a straightforward algorithm. Critical processes in the banks, such as authentication, are built around the assumption of the confidentiality of the PIN thus invest heavily in the PIN generation process. This well-protected PIN is exposed to the VSS when entering the PIN, thus violating a crucial assumption. However, this violation has hitherto gone unnoticed by the banks' security audits because VSS is not considered an inalienable component of the information system. Published on 2021-08-04 00:00:00
  • Scene-Specific Dark Channel Prior for Single Image Fog Removal

    • Abstract: Extensive presence of fog in outdoor images severely alters the scene appearance and hence reduces the visibility. Image processing based defogging algorithms are used to restore the details and colour in a single foggy image. Performances of the previous defogging approaches are considerably low since they fail to consider the image-specific cues. In this paper, a novel and simple defogging approach is proposed based on the estimation of depth map by considering the density of fog in local image regions. The proposed approach uses the scene-specific depth map information to compute the dark channel and transmission. The quality of recovered image is further improved by a post-processing technique. Experimental evaluation performed on FRIDA and FRIDA2 benchmark datasets demonstrates the proposed defogging framework outperforms state-of-the-art approaches. The code and the results of this work are open-sourced for reproducibility ( Published on 2021-08-04 00:00:00
  • A Scoping Review on Automated Software Testing with Special Reference to
           Android Based Mobile Application Testing

    • Abstract: Despite all the techniques practiced for ensuring the quality of a software product, software testing is being the widely accepted practice. With the explosive evolution and the usage of mobile application, new developments in the process of software testing are introduced too to acquire market presence in mobile application development by introducing high quality products. As of this the introduction of automated tools for testing has gained attention in the last few years. Though the topic of automation in software testing has been there for a while, introduction of new tools and techniques has gained attention recently. Hence, this research work focuses on investigating and analyzing the current trends on automated testing of mobile application by choosing the android platform as a case study. With the aim of fact finding, a systematic literature review was carried out on existing studies which were retrieved from different databases by exploring the electronic search space. It discusses the points based on the chosen research questions by referring the papers cited. The topics discussed in this review article includes the facts related to why and how automated testing on mobile application, the tools and techniques used and the challenges on it. This work also highlights why the focus has been concentrated on the mobile application testing rather than generally highlighting the importance of automated software testing. As a conclusion the paper proposes some good practices on the topic based on existing literature reviewed and referred throughout the study. Published on 2021-08-04 00:00:00
  • Neural Machine Translation Approach for Singlish to English Translation

    • Abstract: Comprehension of “Singlish” (an alternative writing system for Sinhala language) texts by a machine had been a requirement for a long period. It has been a choice of many Sri Lankan’s writing style in casual conversations such as small talks, chats and social media comments. Finding a method to translate Singlish to Sinhala or English has been tried for a couple of years by the research community in Sri Lanka and many of the attempts were tried based on statistical language translation approaches due to the challenge of finding a large dataset to use Deep Learning approaches. This research addresses the challenge of preparing a data set to evaluate deep learning approach’s performance for the machine translation activity for Singlish to English language translation and to evaluate Seq2Seq Neural Machine Translation (NMT) model. The proposed seq2seq model is purely based on the attention mechanism, as it has been used to improve NMT by selectively focusing on parts of the source sentence during translation. The proposed approach can achieve 24.13 BLEU score on Singlish-English by seeing ~0.26 M parallel sentence pairs with 50 K+ word vocabulary. Published on 2021-07-31 00:00:00
  • User-Controlled Subflow Selection in MPTCP: A Case Study

    • Abstract: It is common to find multiple network interfaces connected to different Internet Service Providers (ISPs) in devices such as smartphones. Multipath TCP (MPTCP) enables TCP connections to use all these network interfaces in a single TCP connection in an application transparent manner. MPTCP schedules traffic of one TCP connection over subflows created over these network interfaces. It is evident that this requires some scheduling policy. There have been some attempts to allow applications to decide on the scheduling policy. However, this violates the application transparency of MPTCP, and applications do not have all the information required to decide on such a policy. In addition, this allows the applications to monopolize the network connection thus posing a security threat as well. We argue that only the owner of the device (the user) has the right to make that policy decision and only the user can make an informed decision on the scheduling policy. For example, the user has the information on the monetary cost of the connections through different interfaces. In this paper we present a mechanism that allows the user to provide hints to the TCP scheduler to alter its scheduling policy. While this is not a mechanism to implement generic scheduling policies, it demonstrates how a user can guide the scheduling policies. As a proof of the concept, we demonstrate how MPTCP scheduler can be influenced to select a less stable and lossy path over a stable path based on a user preference. Published on 2021-03-30 00:00:00
  • Cluster Identification in Metagenomics – A Novel Technique of
           Dimensionality Reduction through Autoencoders

    • Abstract: Analysis of metagenomic data is not only challenging because they are acquired from a sample in their natural habitats but also because of the high volume and high dimensionality. The fact that no prior lab based cultivation is carried out in metagenomics makes the inference on the presence of numerous microorganisms all the more challenging, accentuating the need for an informative visualization of this data. In a successful visualization, the congruent reads of the sequences should appear in clusters depending on the diversity and taxonomy of the microorganisms in the sequenced sample. The metagenomic data represented by their oligonucleotide frequency vectors is inherently high dimensional and therefore impossible to visualize as is. This raises the need for a dimensionality reduction technique to convert these higher dimensional sequence data into lower dimensional data for visualization purposes. In this process, preservation of the genomic characteristics must be given highest priority. Currently, for dimensionality reduction purposes in metagenomics, Principal Component Analysis (PCA) which is a linear technique and t-distributed Stochastic Neighbor Embedding (t-SNE), a non-linear technique, are widely used. Albeit their wide use, these techniques are not exceptionally suited to the domain of metagenomics with certain shortcomings and weaknesses. Our research explores the possibility of using autoencoders, a deep learning technique, that has the potential to overcome the prevailing impediments of the existing dimensionality reduction techniques eventually leading to richer visualizations. Published on 2021-03-30 00:00:00
  • Tool Support for Distributed Workflow Management with Task Clustering

    • Abstract: When in need for executing complex sets of interrelated calculations on High-Performance Computing (HPC) environments the obvious choice is to use scientific workflows. As workload management software do not support the execution of interrelated tasks, workflow management systems have been introduced to execute workflows on HPC environments. Recently, a new distributed architectural model that offers dynamic workflow execution capabilities to workflow management systems is introduced. It executes workflows on a per-task basis. While this approach facilitates dynamic workflows, it adds a considerable overhead to workflows substantially increasing their makespans. As most workflows are static, task-wise execution of workflows degrades the performance of most workflows. In this paper, we introduce a distributed workflow management system, SwarmForm that introduces task clustering to the new architectural model. SwarmForm is open source and offers better performance than existing distributed workflow management systems by clustering workflow tasks to reduce overheads while allowing the users to choose between task-wise and cluster-wise execution of workflows depending on the workflow nature. The paper proves that SwarmForm enables the use of all the features introduced with the new architectural model while providing better makespans for scientific workflows. Published on 2021-03-30 00:00:00
  • A Multi-layered Data Preparation Model for Health Information in Sudan

    • Abstract: Data quality is a major challenge in almost every data project in today’s world, especially when the required data has a national or global look and feel. However, data preparation activities dominate the efforts, cost, and time consumption. Nowadays, many data collection approaches are continuing to evolve in the era of big data to accommodate revolutionary data flows, especially in the health sector, which contains many different levels of data types, formats, and structures. The lack of qualified and reliable data models is still an ongoing challenge. These issues are even magnified in developing countries where there is a struggle to make advances in health systems with limited resources environments, and to adopt the advantages of ICT to minimize the gaps in health information systems. This article introduces a geo-political multi-layered model for data collection and preparation, the model enables the health data to be collected, prepared and aggregated by using data attendance approach and address data challenges such as data missing, incompetence and format. The currently used data collection method in health sector in Sudan was analysed and data challenges were identified, with respect to geo-political structure of the country. The result of the model provides structured datasets framed by time and geographical spaces that can be used to enrich analytical projects and decision-making in the health sector. Published on 2020-12-31 00:00:00
  • An Affordable, Virtual Reality Based Training Application for
           Cardiopulmonary Resuscitation

    • Abstract: In Medical science, proficiency in Cardiopulmonary Resuscitation (CPR) is considered as a vital skill for physicians. For training CPR, medical professionals use mechanical manikin which has some drawbacks when it comes to the realism of the simulation and the feedback of performance. This paper presents a Virtual Reality (VR) based solution to address some of these shortcomings. The approach here is augmenting the mechanical manikin with VR using HTC Vive, Leap Motion Controller, and a glove. To test the acceptance of this solution, a user-based evaluation was carried out. 85.7% of the users who have participated in the evaluation have expressed their preference upon using VR in CPR training. Even though the overall evaluation depicts a neutral output, this study opens avenues for future research in combining VR into medical training processes. Published on 2021-03-13 00:00:00
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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