A M-Learning Content Recommendation Service by Exploiting Mobile Social Interactions

Abstract

With the rapid development of the Internet and the popularization of mobile devices, participating in a mobile community becomes a part of daily life. This study aims the influence impact of social interactions on mobile learning communities. With m-learning content recommendation services developed from mobile devices and mobile network techniques, learners can generate the learning stickiness by active participation and two-way interaction within a mobile learning community. Individual learning content is able to be recommended according to the behavioral characteristics of the response message of individual learners in the community, and other browsers not of this community are attracted to participate in the learning content with the proposed recommendation service. Finally, as the degree of devotion to the community and learning time increases, the learners' willingness to continue learning increases. The experiment results and analysis show that individualized learning content recommendation results in better learning effect. In addition, the proposed service proved that the experiment results can be easily extended to handle the recommended learning content for learners' time-varying interests.

Resource allocation in small cell networks with time‐averaged rate constraints

“…Our work in this paper is suitable for content prefetching, which transmits media content to the local storage of user equipments before consumption through SCNs off‐peak hours [11, 28]. In combination with a mechanism of content prefetching like [29], our work in this paper can be directly used to facilitate content prefetching in real SCNs.…”

Diseño y validación del cuestionario sobre Percepciones y actitudes hacia el aprendizaje por dispositivos móviles

“…Las características de movilidad y flexibilidad que ofrecen los dispositivos móviles permiten la construcción de conocimiento por parte de los estudiantes en diferentes contextos (Sánchez-García & Toledo-Morales, 2017). Una aplicación de aprendizaje por móviles bien implementada puede ayudar a ahorrar la carga cognitiva mediante el filtrado de información accesible basada en todos los factores contextuales (Chao, Lai, Chen & Huang, 2013;Sarrab, Elbasir & Alnaeli, 2016 A pesar del rápido crecimiento de los dispositivos móviles, hay una falta de datos de investigación que aborden los factores que impulsan la adopción de dichos recursos tecnológicos. La percepción y aceptación del estudiante del aprendizaje mediante el uso de móviles debe ser investigada antes de adoptar la tecnología.…”

An Empirical Study of Factors Driving the Adoption of Mobile Learning in Omani Higher Education

“…The mobility and flexibility features offered by mobile devices enable knowledge building by learners in different contexts. A wellimplemented M-learning application can assist in saving the cognitive load by filtering accessible information based on all contextual factors (Chao, Lai, Chen, & Huang, 2013;Sarrab, Elbasir, & Alnaeli, 2016).…”