Personalized Mobile Learning and Course Recommendation System

2021 ◽  
Vol 13 (1) ◽  
pp. 38-48
Author(s):  
Madhubala Radhakrishnan
Keyword(s):  
Mobile Learning ◽  
Recommendation System ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning System ◽  
Mobile Learning System ◽  
Media Formats ◽  
Educational Contents ◽  
Different Levels

Mobile-based learning provides new experience to the learners to learn anything from anywhere and anytime by using their portable or mobile device. Vast educational contents and also different media formats can be supported by the mobile devices. Access speed of those materials has also improved a lot. With this advancement, providing required content or materials in the desired format to the learner is essential to the learning management system. Also, it is very important to guide the learner based on their interest in learning. With this outset, the proposed mobile learning system helps the learners to access different courses under different levels and different specializations. The course contents are in different formats called learning objects (LO). In order to provide personalized learning experience to the learner, the system finds the learner's preferences and selects the desired learning objects. It also recommends some specializations with level to the learners to achieve higher grades.

scholarly journals A design of a multi-agent recommendation system using ontologies and rule-based reasoning: pandemic context

2022 ◽  
Vol 12 (1) ◽  
pp. 515
Author(s):  
Amina Ouatiq ◽  
Kamal ElGuemmat ◽  
Khalifa Mansouri ◽  
Mohammed Qbadou
Keyword(s):  
Mental State ◽  
Adaptive Systems ◽  
Recommendation System ◽  
Unified Modeling Language ◽  
Social Situation ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning System ◽  
Holistic View ◽  
Rule Based

Learners attend their courses in remote or hybrid systems find it difficult to follow one size fits all courses. These difficulties have increased with the pandemic, lockdown, and the stress they cause. Hence, the role of adaptive systems to recommend personalized learning resources according to the learner's profile. The purpose of this paper is to design a system for recommending learning objects according learner's condition, including his mental state, his COVID-19 history, as well as his social situation and ability to connect to the e-learning system on a regular basis. In this article, we present an architecture of a recommendation system for personalized learning objects based on ontologies and on rule-based reasoning, and we will also describe the inference rules required for the adaptation of the educational content to the needs of the learners, taking into account the learner’s health and mental state, as well as his social situation. The system designed, and validated using the unified modeling language (UML). It additionally allows teachers to have a holistic view of learners’ progress and situations.

scholarly journals Design of an Adaptive e-Learning System based on Multi-Agent Approach and Reinforcement Learning

2021 ◽  
Vol 11 (1) ◽  
pp. 6637-6644
Author(s):  
H. El Fazazi ◽  
M. Elgarej ◽  
M. Qbadou ◽  
K. Mansouri
Keyword(s):  
Reinforcement Learning ◽  
Learning Style ◽  
Learning Process ◽  
Learning Experience ◽  
Personalized Learning ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
E Learning ◽  
Multi Agent

Adaptive e-learning systems are created to facilitate the learning process. These systems are able to suggest the student the most suitable pedagogical strategy and to extract the information and characteristics of the learners. A multi-agent system is a collection of organized and independent agents that communicate with each other to resolve a problem or complete a well-defined objective. These agents are always in communication and they can be homogeneous or heterogeneous and may or may not have common objectives. The application of the multi-agent approach in adaptive e-learning systems can enhance the learning process quality by customizing the contents to students’ needs. The agents in these systems collaborate to provide a personalized learning experience. In this paper, a design of an adaptative e-learning system based on a multi-agent approach and reinforcement learning is presented. The main objective of this system is the recommendation to the students of a learning path that meets their characteristics and preferences using the Q-learning algorithm. The proposed system is focused on three principal characteristics, the learning style according to the Felder-Silverman learning style model, the knowledge level, and the student's possible disabilities. Three types of disabilities were taken into account, namely hearing impairments, visual impairments, and dyslexia. The system will be able to provide the students with a sequence of learning objects that matches their profiles for a personalized learning experience.

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.

Devised Mobile Learning System Based on Campus

2011 ◽  
Vol 271-273 ◽  
pp. 1160-1163
Author(s):  
Yan Xin Hu
Keyword(s):  
Mobile Learning ◽  
Learning System ◽  
Learning Content ◽  
Function Module ◽  
Basic Features ◽  
Mobile Learning System

Introduced the mobile learning content, analyzed the basic features of mobile learning, designed mobile leaening system based on campus, and each function module of mobile learning system.

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