Construction of Mobile Learning Network System Based on Cloud Computing

2014 ◽  
Vol 631-632 ◽  
pp. 1451-1456
Author(s):  
Yan Li Wei ◽  
Han Zhao ◽  
Zhong Lu Cao
Keyword(s):  
Mobile Learning ◽  
Situated Learning ◽  
Resource Sharing ◽  
Large Scale ◽  
Learning System ◽  
Network System ◽  
Learning Collaborative ◽  
Learning Network ◽  
Building Models ◽  
Cloud Resource

Distributed computing and Resource sharing in large-scale heterogeneous system are the key to realize mobile learning system. This paper sets up a mobile learning service system through the construction of teaching cloud platform and cloud resources, cloud resource sharing, application and service of cloud resource, system and mechanism construction. The mobile learning network system is established by building models of it, which realizes mobile learning, collaborative learning and the situated learning anytime anywhere with freedom, individuality and diversification.

Developing Interactive Mobile Learning Experiences for Healthcare Professionals

2020 ◽  
Vol 12 (2) ◽  
pp. 40-52
Author(s):  
Hugh Kellam
Keyword(s):  
Best Practices ◽  
Mobile Learning ◽  
Situated Learning ◽  
Resource Sharing ◽  
Healthcare Professionals ◽  
Learning Experiences ◽  
Discussion Forums ◽  
Mixed Methods Case Study ◽  
Collaboration And Communication ◽  
Context Specific

The purpose of this article was to examine best practices for designing inquiry-based contextual instructional content and determining the pedagogical uses and impacts of communities of practice for supporting mobile learning activities. In this convergent parallel mixed methods case study, mobile learning experiences were accessed by physicians, nurses, and healthcare professionals at medical organizations across Ontario. Impact was measured by the learning outcomes and experiences of study participants. Findings highlighted the effectiveness of context-specific, situated learning content for application of learned skills, integration of new knowledge, and identification of best practices. Synchronous discussion forums were examined for collaboration and communication during mobile learning, and asynchronous forums were ideal for post-learning collaboration, problem-solving and resource sharing.

Mobile Internet Oriented M-Learning System

2013 ◽  
Vol 411-414 ◽  
pp. 2883-2887
Author(s):  
Jie Mei Lin ◽  
Rong Huang ◽  
Jia Yin Zhao ◽  
Qing Dai
Keyword(s):  
Mobile Learning ◽  
Large Scale ◽  
Mobile Internet ◽  
Learning System ◽  
Smart Devices ◽  
Key Issues ◽  
Access Information ◽  
Smart Mobile ◽  
Mobile Learning System ◽  
Future Learning

In recent years, the mobile internet is deployed rapidly in large-scale. Meanwhile the smart mobile devices are penetrated universally. The combination of them provides the sufficient precondition for the Mobile Learning, i.e. M-Learning. A revolution on future learning foreseen because of the M-learning, characterized with mobility, convenience, timeliness and other characteristics, enabling anywhere, anytime learning for anybody via smart devices in order to easier access information, flexible attend classes, and freely join discussion, etc. This paper analyzes the key elements and characteristics of mobile Internet-oriented mobile learning system, provides the framework of M-Learning system functionality architecture for core applications, and furthermore focuses on some technical key issues including knowledge aggregation/mashup and information pushing for mobile learning.

Developing a Mobile Learning Platform for a Professional Environment

2009 ◽  
pp. 273-300 ◽  
Author(s):  
Ana Dzartevska
Keyword(s):  
Mobile Learning ◽  
Situated Learning ◽  
Design Tool ◽  
Learning System ◽  
Working Environment ◽  
Learning Platform ◽  
Development Life Cycle ◽  
Design Requirements ◽  
Mobile Learning System ◽  
Further Development

This chapter describes some conceptual and practical issues in the development of a professional mobile learning system. The main focus of the chapter is the implementation of a mobile learning solution for the requirements of a specific working environment. The study proposes a conceptual framework for mobile learning applications, based on multiple studies of mobile learning design requirements, and uses it as a design tool for the development of the new system based on situated learning in the workplace. This approach is illustrated through the development life cycle of a prototype mobile learning solution. Finally, the chapter reviews and evaluates the success of the prototype and the utility of the framework as a design tool, identifies potential issues and outlines ideas for further development and research.

scholarly journals Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

2020 ◽  
pp. bjophthalmol-2020-317825
Author(s):  
Yonghao Li ◽  
Weibo Feng ◽  
Xiujuan Zhao ◽  
Bingqian Liu ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Deep Learning ◽  
High Myopia ◽  
Large Scale ◽  
Learning System ◽  
Youden Index ◽  
Optical Coherence ◽  
Test Dataset ◽  
Independent Test ◽  
Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

scholarly journals Telepresence and Interactivity in Mobile Learning System: Its Relation with Open Innovation

2021 ◽  
Vol 7 (1) ◽  
pp. 78
Author(s):  
Na Wei ◽  
ZhongWu Li
Keyword(s):  
Technology Acceptance ◽  
Mobile Learning ◽  
Open Innovation ◽  
Online Survey ◽  
Mediating Effect ◽  
Learning System ◽  
Equation Modeling ◽  
Flow Experience ◽  
Mobile Learning System ◽  
System Characteristics

Mobile learning applications enable people to spend fragmented time to improve their knowledge and competitiveness. Enterprises aim to design innovative applications and create a new learning mode for the public, and the open innovation strategies may help companies achieve their goals. In the current study, the English learning application “LAIX” was investigated, and an online survey was used to obtain data from 289 university students in Guangzhou. This study combines the technology acceptance model (TAM) with flow theory (FT), investigating the psychological experience factors and the system characteristics that influence users’ behavior intentions. The exploration of perceptual variables will promote the establishment of an open innovation model of mobile learning applications. The aim of the study was to establish a theoretical framework to more deeply explore users’ intentions in mobile learning applications. Structural equation modeling (SEM) was used to help measure the relationship between variables and determine the model fit. This research reveals that telepresence is the most important variable that impacts user intentions to use mobile learning applications. In addition, the mediating effect of the flow experience was tested. Telepresence and interactivity indirectly influence behavioral intention through the variable “flow”. Users appear to be more concerned with the flow experience, which shows the highest correlation with intention to use the application. This study may assist companies to innovate system characteristics and improve customers’ user experience, for instance, by integrating virtual reality (VR) technology into the mobile learning system to improve their open innovation level and market popularity.

scholarly journals Learning As It Happens: A Decade of Analyzing and Shaping a Large-Scale Online Learning System

2018 ◽  
Vol 5 (2) ◽  
Author(s):  
Matthieu J. S. Brinkhuis ◽  
Alexander O. Savi ◽  
Abe D. Hofman ◽  
Frederik Coomans ◽  
Han L. J. Van der Maas ◽  
...  
Keyword(s):  
Online Learning ◽  
Large Scale ◽  
Response Times ◽  
Learning System ◽  
Learning Gains ◽  
Online Experimentation ◽  
Quality Of Fit ◽  
Item Reliability ◽  
Made In

With the advent of computers in education, and the ample availability of online learning and practice environments, enormous amounts of data on learning become available. The purpose of this paper is to present a decade of experience with analyzing and improving an online practice environment for math, which has thus far recorded over a billion responses. We present the methods we use to both steer and analyze this system in real-time, using scoring rules on accuracy and response times, a tailored rating system to provide both learners and items with current ability and difficulty ratings, and an adaptive engine that matches learners to items. Moreover, we explore the quality of fit by means of prediction accuracy and parallel item reliability. Limitations and pitfalls are discussed by diagnosing sources of misfit, like violations of unidimensionality and unforeseen dynamics. Finally, directions for development are discussed, including embedded learning analytics and a focus on online experimentation to evaluate both the system itself and the users’ learning gains. Though many challenges remain open, we believe that large steps have been made in providing methods to efficiently manage and research educational big data from a massive online learning system.

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