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Full-Text Articles in Computer Engineering
Deep Neural Network Based M-Learning Model For Predicting Mobile Learners'performance, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq, Arsalan Ali
Deep Neural Network Based M-Learning Model For Predicting Mobile Learners'performance, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq, Arsalan Ali
Turkish Journal of Electrical Engineering and Computer Sciences
The use of deep learning (DL) techniques for mobile learning is an emerging field aimed at developing methods for finding mobile learners' learning behavior and exploring important learning features. The learning features (learning time, learning location, repetition rate, content types, learning performance, learning time duration, and so on) act as fuel to DL algorithms based on which DL algorithms can classify mobile learners into different learning groups. In this study, a powerful and efficient m-learning model is proposed based on DL techniques to model the learning process of m-learners. The proposed m-learning model determines the impact of independent learning features …
Cloud-Supported Machine Learning System For Context-Aware Adaptive M-Learning, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq
Cloud-Supported Machine Learning System For Context-Aware Adaptive M-Learning, Muhammad Adnan, Asad Habib, Jawad Ashraf, Shafaq Mussadiq
Turkish Journal of Electrical Engineering and Computer Sciences
It is a knotty task to amicably identify the sporadically changing real-world context information of a learner during M-learning processes. Contextual information varies greatly during the learning process. Contextual information that affects the learner during a learning process includes background knowledge, learning time, learning location, and environmental situation. The computer programming skills of learners improve rapidly if they are encouraged to solve real-world programming problems. It is important to guide learners based on their contextual information in order to maximize their learning performance. In this paper, we proposed a cloud-supported machine learning system (CSMLS), which assists learners in learning practical …
Development Of A Human-Ai Teaming Based Mobile Language Learning Solution For Dual Language Learners In Early And Special Educations, Saurabh Shukla
Development Of A Human-Ai Teaming Based Mobile Language Learning Solution For Dual Language Learners In Early And Special Educations, Saurabh Shukla
Browse all Theses and Dissertations
Learning English as a secondary language is often an overwhelming challenge for dual language learners (DLLs), whose first language (L1) is not English, especially for children in early education (PreK-3 age group). These early DLLs need to devote a considerable amount of time learning to speak and read the language, in order to gain the language proficiency to function and compete in the classroom. Fear of embarrassment when mispronouncing words in front of others may drive them to remain silent; effectively hampering their participation in the class and overall curricular growth. The process of learning a new language can benefit …
An M-Learning Maturity Model For Universities And Higher Educational Institutes, Luiz Fernando Capretz, Muasaad Alrasheedi, Arif Raza
An M-Learning Maturity Model For Universities And Higher Educational Institutes, Luiz Fernando Capretz, Muasaad Alrasheedi, Arif Raza
Electrical and Computer Engineering Publications
An m-learning maturity model is put forward in this research to assess the mobile technology adoption rates in universities and higher educational institutes. The model is derived from Capability Maturity Model (CMM), which has been widely used in organizations to gauge the adoption of various new processes. Five levels of m-learning maturity are specified including preliminary, established, defined, structured, and continuous improvement. Each of these maturity levels is gauged through nine critical success factors (CSFs) in assessment questionnaires. The CSFs used in measuring instrument of the model are adopted from three of our previous empirical studies. Using an assessment questionnaire …
A Maturity Model For Mobile Learning, Muasaad Alrasheedi
A Maturity Model For Mobile Learning, Muasaad Alrasheedi
Electronic Thesis and Dissertation Repository
Higher education is becoming increasingly interested in adopting innovative and modern technologies as a mode of imparting education. Mobile technologies are considered to be the next frontier of educational platforms as they have the capability to provide high-quality learning experiences and to satisfy the increasing demand for mobility and flexibility. In view of the ubiquitous nature of mobile technology and the immense opportunities it offers, there are favorable indications that the technology could be introduced as the next generation of learning platforms. The present research aims to develop a comprehensive framework based on the well-known Capability Maturity Model (CMM) and …
A Systematic Review Of The Critical Factors For Success Of Mobile Learning In Higher Education (University Students' Perspective), Muasaad Alrasheedi, Luiz Fernando Capretz, Arif Raza
A Systematic Review Of The Critical Factors For Success Of Mobile Learning In Higher Education (University Students' Perspective), Muasaad Alrasheedi, Luiz Fernando Capretz, Arif Raza
Electrical and Computer Engineering Publications
The phenomenon of the use of a mobile learning (m-Learning) platform in educational institutions is slowly gaining momentum. However, the enthusiasm with which mobile phones have been welcomed into every aspect of our lives is not yet apparent in the educational sector. To understand the reason, it is important to understand user expectations of the system. This article documents a systematic review of existing studies to find the success factors for effective m-Learning. Our systematic review collates results from 30 studies conducted in 17 countries, where 13 critical success factors were found to strongly impact m-Learning implementation. Using these results …
An Empirical Study Of Critical Success Factors Of Mobile Learning Platform From The Perspective Of Instructors, Muasaad Alrasheedi, Luiz Fernando Capretz
An Empirical Study Of Critical Success Factors Of Mobile Learning Platform From The Perspective Of Instructors, Muasaad Alrasheedi, Luiz Fernando Capretz
Electrical and Computer Engineering Publications
Mobile learning is newest learning platform and based on the rapid rate of proliferation of mobile technology throughout the world is expected to grow at a rapid rate. However, the adoption of m-Learning is proceeding at a cautious rate. This mismatch in the rate of growth of the technology itself and the use of the technology in learning is a subject of extensive interest to researchers. However, research in the area has been mostly focused on understanding the success factors of the platform from learners’ perspective. In this research, we have conducted an extensive analysis of the extent to which …