Artificial Intelligence Applications with e-Learning System for China’s Higher Education Platform

2021 ◽  
Author(s):  
Xiaoran Fu ◽  
K. Lokesh Krishna ◽  
R. Sabitha
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Student Learning ◽  
Learning Style ◽  
Adaptive Learning ◽  
Teaching Quality ◽  
Learning System ◽  
Learning Context ◽  
E Learning ◽  
Student Learning Style

Artificial Intelligence (AI) assisted educational institutions extensively utilize electronic learning context to guarantee improved teaching and learning experiences accompanied by educational activities. E-learning or online learning plays a significant role in Chinese higher education. There is a challenge to implement e-learning in China’s higher education to improve course resources, student learning style prediction, teaching quality, and service support. Hence in this paper, Artificial Intelligence based Efficient E-learning Framework (AI-EELF) has been proposed to overcome the challenges faced by China’s higher education while implementing e-learning modules. The collected student data can be efficiently utilized and exploited to progress in an adaptive learning environment. The proposed AI-EELF method introduces multiple learning models to enhance teaching quality and predict the student learning style. The experimental results show that the proposed AI-EELF achieves high performance, prediction ratio in determining students’ learning style and improves teaching quality compared to other existing methods.

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

2018 ◽  
Vol 2 (4) ◽  
pp. 271 ◽  
Author(s):  
Outmane Bourkoukou ◽  
Essaid El Bachari
Keyword(s):  
Learning Style ◽  
Recommender System ◽  
Adaptive Learning ◽  
Learning Objects ◽  
Research Field ◽  
Learning System ◽  
K Nearest Neighbors ◽  
E Learning ◽  
Hybrid Recommender System ◽  
Hybrid Recommender

Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

scholarly journals Factors Affecting Students' Acceptance of e-Learning System in Higher Education

2020 ◽  
Vol 5 (2) ◽  
pp. 54-65
Author(s):  
Nurhafizah Ahmad ◽  
Norazah Umar Umar ◽  
Rozita Kadar ◽  
Jamal Othman
Keyword(s):  
Higher Education ◽  
Learning Style ◽  
Main Idea ◽  
Learning System ◽  
Independent Learning ◽  
Online Questionnaire ◽  
Factors Affecting ◽  
Effective Learning ◽  
Significant Relationships ◽  
E Learning

e-Learning has become the most important supporting tool offering independent learning style among students. The main idea of this paper is to dismantle and analyse factors that influence the acceptance of e-Learning among students in higher education.  An online questionnaire link was distributed to a sample comprising 123 respondents. Significant relationships and strength of relationship were observed between the e-Learning acceptance, quality, e-Learning self-efficacy, enjoyment, accessibility, and computer playfulness. The findings showed that all factors were positively correlated to the e-Learning system except the enjoyment of e-learning that did not affect the acceptance of e-learning. Conclusively, all factors stated were considered the main criteria in designing effective e-learning system. Future works such as embedding and integrating multimedia elements in the e-learning system will be additional attraction to learners and instructors for the effective learning style.

scholarly journals Personalization of an educational scenario of a learning activity according to the learning styles model David Kolb

2020 ◽  
Vol 5 (3) ◽  
pp. 099-108
Author(s):  
Anoir Lamya ◽  
Zargane Kawtar ◽  
Erradi Mohamed ◽  
Khaldi Mohamed
Keyword(s):  
Learning Styles ◽  
Learning Style ◽  
Adaptive Learning ◽  
Teaching And Learning ◽  
Research Work ◽  
Learning System ◽  
Learning Activity ◽  
Learning Context ◽  
Intermediate Space ◽  
Training Courses

The personalization of learning remains a very important subject in research particularly with the progression of technology, it refers to a pedagogical approach that is located in an intermediate space where teaching and learning come together with devices personalized and adapted training courses for the different learner profiles in a social learning context. We offer a general approach to the personalization of teaching scenarios during the different types of teaching activities and taking consideration the learning styles of the learners and based on the Kolb learning style model. Our research work aimed at developing a personalized and adaptive learning system to meet the needs of learners and adequate with their preferences and profiles all throughout the learning process offered by the system by making a correspondence with the suitable pedagogical scenario with each profile and each activity.

SVM and PCA Based Learning Feature Classification Approaches for E-Learning System

2018 ◽  
Vol 13 (2) ◽  
pp. 32-45 ◽  
Author(s):  
Aditya Khamparia ◽  
Babita Pandey
Keyword(s):  
Online Education ◽  
Student Learning ◽  
Learning Style ◽  
Least Square ◽  
Learning System ◽  
Support Vector ◽  
Learning Material ◽  
Feed Forward Neural Network ◽  
Dynamic Learning ◽  
E Learning

E-learning and online education has made great improvements in the recent past. It has shifted the teaching paradigm from conventional classroom learning to dynamic web based learning. Due to this, a dynamic learning material has been delivered to learners, instead of static content, according to their skills, needs and preferences. In this article, the authors have classified eight different types of student learning attributes based on National Centre for Biotechnical Information (NCBI) e-learning database. The eight types of attributes are Anxiety (A), Personality (P), Learning style (L), Cognitive style (C), Grades from previous sem (GP), Motivation (M), Study level (SL) and Student prior knowledge (SPK). In this article the authors have proposed an approach which uses principal components of student learning attributes and have later independently classified these attributes using feed forward neural network (NN) and Least Square –Support Vector Machine (LS-SVM).

Learning and e‐learning in HE: the relationship between student learning style and VLE use

2007 ◽  
Vol 22 (4) ◽  
pp. 443-464 ◽  
Author(s):  
Celayne Heaton‐Shrestha ◽  
Caroline Gipps ◽  
Palitha Edirisingha ◽  
Tim Linsey
Keyword(s):  
Student Learning ◽  
Learning Style ◽  
E Learning ◽  
Student Learning Style ◽  
The Relationship

scholarly journals Adult and Youth Students’ Perceptions of Distance vs. Face-to-Face Courses’ Effectiveness in the COVID-19 Pandemic

2021 ◽  
Vol 11 (4) ◽  
pp. 121-132
Author(s):  
Kuo-Ming Chu ◽  
◽  
Hui-Chun Chan ◽  
Chi-Fang Liu
Keyword(s):  
Higher Education ◽  
Teaching Quality ◽  
Learning System ◽  
Face To Face ◽  
Computer Based Learning ◽  
Quality Of Higher Education ◽  
Containment Measures ◽  
E Learning ◽  
Face To Face Learning ◽  
Traditional Classroom Teaching

On account of its contagious nature, COVID-19 has resulted in various containment measures and mandatory isolation, affecting the personal interaction between students and instructors tremendously. In the absence of face-to-face interaction and traditional classroom teaching, computer-based learning has come out as the closest substitute for offline teaching. In addition, adult and youth students’ perceptions of courses’ effectiveness towards online learning as compared to traditional face-to-face learning have largely been overlooked and thus should be designed based on the needs of adult learners. This paper aims to fill this void in the literature, presenting results indicating all students’ positive perceptions towards e-learning and thus acceptance of this new learning system. It also empirically demonstrates the significance of e-learning in the time of this COVID-19 crisis. The results also point out surprising differences in students’ perceptions of the importance of communications and collaboration, effectiveness, and self-efficacy, and surprisingly differences exist between the performances of youth and adult learner groups. Under the current debates on the cost and teaching quality of higher education, the findings herein should help educational institutions in their improvement of higher education and student enrollment and retention.

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