ترغب بنشر مسار تعليمي؟ اضغط هنا

نظام التعلم المحمول المستند إلى IMS

IMS-based mobile learning system

505   0   0.0 ( 0 )
 نشر من قبل M. Rizwan Jameel Qureshi Dr.
 تاريخ النشر 2014
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Electronic (E) learning management system is not a novel idea in the educational domain. Learning management systems are used to deal with academic activities such as course syllabi, time table scheduling, assessments and project discussion forums. Almost, all the top universities of world are using general purpose/customized solutions to manage learning management systems like SAP, Oracle, Moodle and Blackboard. The aim of this paper i.e., Mobile (M) Learning System (MLS) is not to substitute the traditional web based E learning applications but to enhance it by amalgamating both web and mobile technologies. This idea justifies the proposal of M learning system to use some of the services of E learning system from mobiles. MLS will use state-of-the-art IP Multimedia Sub System technology. The emphasis in this research will be on the technical implementation of the Session Initiation Protocol (SIP) using IP Multimedia Subsystem (IMS) to develop an MLS not only for the students of the King Abdulaziz University but it will be beneficial for the students of other universities at Kingdom of Saudi Arabia. A customized CBD is proposed as per the nature of MLS project. MLS case study is used as a research design to validate the customized CBD model. Multi-tier applications architecture (client, web, and business) will be adopted during the development of MLS case study. An MLS will be developed and tested using IMS platform to check its practicality for the students of King Abdulaziz University. It is anticipated that the proposed system will significantly facilitate to both the students and teachers of KAU during their off campus activities.

قيم البحث

اقرأ أيضاً

Innovative, real-time solutions are needed to address the mismatch between the demand for and supply of critical information to inform and motivate diet and health-related behavior change. Research suggests that interventions using mobile health tech nologies hold great promise for influencing knowledge, attitudes, and behaviors related to energy balance. The objective of this paper is to present insights related to the development and testing of a mobile food recommendation system targeting fast food restaurants. The system is designed to provide consumers with information about energy density of food options combined with tips for healthier choices when dining out, accessible through a mobile phone.
66 - Yuanbang Li 2021
With the widespread use of mobile phones, users can share their location and activity anytime, anywhere, as a form of check in data. These data reflect user features. Long term stable, and a set of user shared features can be abstracted as user roles . The role is closely related to the users social background, occupation, and living habits. This study provides four main contributions. Firstly, user feature models from different views for each user are constructed from the analysis of check in data. Secondly, K Means algorithm is used to discover user roles from user features. Thirdly, a reinforcement learning algorithm is proposed to strengthen the clustering effect of user roles and improve the stability of the clustering result. Finally, experiments are used to verify the validity of the method, the results of which show the effectiveness of the method.
The primary purpose of this paper is to provide a design of a blockchain-based system, which produces a verifiable record of achievements. Such a system has a wide range of potential benefits for students, employers and higher education institutions. A verifiable record of achievements enables students to present academic accomplishments to employers, within a trusted framework. Furthermore, the availability of such a record system would enable students to review their learning throughout their career, giving them a platform on which to plan for their future accomplishments, both individually and with support from other parties (for example, academic advisors, supervisors, or potential employers). The proposed system will help students in universities to increase their extra-curricular activities and improve non-academic skills. Moreover, the system will facilitate communication between industry, students, and universities for employment purposes and simplify the search for the most appropriate potential employees for the job.
Mobile devices have evolved from just communication devices into an indispensable part of peoples lives in form of smartphones, tablets and smart watches. Devices are now more personal than ever and carry more information about a person than any othe r. Extracting user behaviour is rather difficult and time-consuming as most of the work previously has been manual or requires feature extraction. In this paper, a novel approach of user behavior detection is proposed with Deep Learning Network (DNN). Initial approach was to use recurrent neural network (RNN) along with LSTM for completely unsupervised analysis of mobile devices. Next approach is to extract features by using Long Short Term Memory (LSTM) to understand the user behaviour, which are then fed into the Convolution Neural Network (CNN). This work mainly concentrates on detection of user behaviour and anomaly detection for usage analysis of mobile devices. Both the approaches are compared against some baseline methods. Experiments are conducted on the publicly available dataset to show that these methods can successfully capture the user behaviors.
Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driv en smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July, 2020) in 8 languages and attracted 7,290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا