Present E-learning System Research Articles

A Method of Recommending Physical Education Network Course Resources Based on Collaborative Filtering Technology

Through the current research on e-learning, it is found that the present e-learning system applied to the recommendation activities of learning resources has only two search methods: Top-N and keywords. These search methods cannot effectively recommend learning resources to learners. Therefore, the collaborative filtering recommendation technology is applied, in this paper, to the process of personalized recommendation of learning resources. We obtain user content and functional interest and predict the comprehensive interest of web and big data through an infinite deep neural network. Based on the collaborative knowledge graph and the collaborative filtering algorithm, the semantic information of teaching network resources is extracted from the collaborative knowledge graph. According to the principles of the nearest neighbor recommendation, the course attribute value preference matrix (APM) is obtained first. Next, the course-predicted values are sorted in descending order, and the top T courses with the highest predicted values are selected as the final recommended course set for the target learners. Each course has its own online classroom; the teacher will publish online class details ahead of time, and students can purchase online access to the classroom number and password. The experimental results show that the optimal number of clusters k is 9. Furthermore, for extremely sparse matrices, the collaborative filtering technique method is more suitable for clustering in the transformed low-dimensional space. The average recommendation satisfaction degree of collaborative filtering technology method is approximately 43.6%, which demonstrates high recommendation quality.

COPING WITH COVID-19: A NEW ONLINE EDUCATION MODEL

After a long lockdown period, one thing that we as students had to face is that we had to move away from the traditional classroom programs and go for the online interaction and learning by our respected teachers. As far as the COVID-19 like situation is concerned, online education is the only way to cope up. And associatively the examination systems had been affected to a huge extent, and it had become the weakest part of the full learning process. But as far as the present e-learning system is concerned it can easily be stated that the views of the people are that the e-learning is not an effective way, and it does not hold any value in the career other than getting a promotion or an additional degree in papers only without a thorough knowledge and practical exposure based on the course. In this paper we will discuss about a new education model which can reestablish the education base for the students who had kept themselves far away from daily attentive learning. The new model needs to be implemented with some new effective techniques to enforce learner to get evaluated based on their daily attentiveness and understanding on the topics along with some practical implementation of the knowledge. And in a situation to cope COVID-19 this model will stand ideal for learner to continue the learning process without visiting the Schools, Colleges and Universities.

Applying the Technology Acceptance Model to Analyze the Saudi University Students' Attitudes and Intentions toward Using E-learning in their Courses

This study aimed to analyze students' behavioral intentions to use e-learning in the kingdom of Saudi Arabia, and to examine the effectiveness of the technology acceptance model as a theoretical basis for understanding these behaviors. The researchers used analytical descriptive approach to identify the perceptions of a sample of (324) students in the Saudi government and private universities in the e-learning system. The researchers developed a questionnaire consisting of (29) items which covered nine main themes.The results of the field study showed the validity of the technology acceptance model as the theoretical background that would help in understanding and clarifying behavioral intentions of the students toward e-learning. The results showed that the behavioral intentions were affected by students' attitudes, personal standards and system accessibility, while they were not directly affected by perceived ease of use and expected benefits of the system. Also, the results showed that students' attitudes were affected by all variables, including expected benefits of the system, personal standards, system accessibility, ease of use and system effectiveness. The study recommended establishing of a center for teaching and learning within the university to present e-learning system and provide written instructions on that system and be responsible for the development of e-learning strategy. Also, the study recommended increasing self-efficacy for students in e-learning system, and providing more Courses in this system. It was also recommended that the department of e-learning system should help students to confirm or increase their positive perceptions toward e-learning system through developing more user-friendly and user-oriented e-learning content.
 Keywords: E-Learning, Technology acceptance model, Students' attitudes, Behavioral intentions, Saudi universities.

Prediction of Learner’s Profile based on Learning Styles in Adaptive E-learning System

The major requirement of present e-learning system is to provide a personalized interface with adaptiveness. This is possible to provide by analyzing the learning behaviors of the learners in the e-learning portal through Web Usage Mining (WUM). In this paper, a method is proposed where the learning behavior of the learner is captured using web logs and the learning styles are categorized according to Felder-Silverman Learning Style Model (FSLSM). Each category of FSLSM learner is provided with the respective content and interface that is required for the learner to learn. Fuzzy C Means (FCM) algorithm is used to cluster the captured data into FSLSM categories. Gravitational Search based Back Propagation Neural Network (GSBPNN) algorithm is used to predict the learning styles of the new learner. This algorithm is a modification of basic Back Propagation Neural Network (BPNN) algorithm that calculates the weights using Gravitation Search Algorithm (GSA). The algorithm is validated on the captured data and compared using various metrics with the basic BPNN algorithm. The result shows that the performance of GSBPNN algorithm is better than BPNN. Based on the identified learning style, the adaptive contents and interface can be provided to the learner.