scholarly journals Analysis of the Effect of Course Design, Course Content Support, Course Assessment and Instructor Characteristics on the Actual Use of E-Learning System

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171907-171922 ◽  
Author(s):  
Mohammed Amin Almaiah ◽  
Ibrahim Youssef Alyoussef
Keyword(s):  
Course Design ◽  
Learning System ◽  
Course Content ◽  
Course Assessment ◽  
Actual Use ◽  
E Learning ◽  
Content Support ◽  
Instructor Characteristics

scholarly journals Factors Affecting the Quality of E-Learning During the COVID-19 Pandemic from the Perspective of Higher Education Students

10.28945/4628 ◽  
2020 ◽  
Vol 19 ◽  
pp. 731-753
Author(s):  
Kesavan Vadakalu Elumalai ◽  
Jayendira P Sankar ◽  
Kalaichelvi R ◽  
Jeena Ann John ◽  
Nidhi Menon ◽  
...  
Keyword(s):  
Higher Education ◽  
Social Support ◽  
Administrative Support ◽  
Course Design ◽  
Educational Institutions ◽  
Learner Characteristics ◽  
Course Content ◽  
E Learning ◽  
Instructor Characteristics

Aim/Purpose: The objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during the COVID-19 pandemic. Further, the study analyzes the moderating effect(s) of gender and level of the course on the quality of e-learning in higher education during the COVID-19 pandemic. objective of the research was to study the relationship of seven independent factors: administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technical support on quality of e-learning in higher education during COVID-19 pandemic. Background: The COVID-19 pandemic situation has impacted the entire education system, especially universities, and brought a new phase in education “e-learning.” The learning supported with electronic technology like online classes and portals to access the courses outside the classroom is known as e-learning. This study aimed to point out the variables influencing the quality of e-learning, such as administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support. Methodology: An inferential statistics cross-sectional study was conducted of the students of higher education institutions in India and the Kingdom of Saudi Arabia with a self-administered questionnaire to learn the students’ perception of e-learning. All levels of undergraduate and postgraduate students took part in the study with a sample size of 784. Ultimately, this study used a Structural Equation Modelling (SEM) approach to find the positive relationship between the quality of e-learning and the seven independent variables and two moderating variables in the higher education sector. Contribution: The study aims to explore the quality of e-learning in higher education from the students’ perspective. The study was analyzed based on the student’s data collected from the higher educational institutions of India and Saudi Arabia. The study will support the top management and administrators of higher educational institutions in decision making. Findings: The findings revealed that there is a positive relationship between the set of variables and the quality of e-learning in the higher education sector. Also, there is a significant difference in the perception of the students between gender, level of the course, and quality of e-learning in the higher education sector during the COVID-19 pandemic. Recommendations for Practitioners: The results of the study can help top management and administrators of higher educational institutions to improve their actions. Higher educational institutions need to concentrate on the study outcomes related to administrative support, course content, course design, instructor characteristics, learner characteristics, social support, and technological support to enhance the quality of e-learning. The study revealed that there should be a difference in the procedure of providing e-learning based on the level of the course and gender of the students. Recommendation for Researchers: The results were examined and interpreted in detail, based on the perspective of the students, and concluded with a view for future research. The study will be beneficial for academic researchers from different countries with a different set of students and framework. Impact on Society: The study revealed that the positive results of the students’ perspective on the quality of e-learning would help the policy-makers of the country in providing the learning process during the COVID-19 pandemic. Also, the result explored the importance of the quality aspects of e-learning for improvement. Future Research: There is a need for future studies to expose the quality of e-learning in higher education in the post-COVID-19 pandemic. Further researchers will bring the performance level of e-learning during the COVID-19 pandemic.

Students' satisfaction and continuance intention of the cloud-based e-learning system: roles of interactivity and course quality factors

2020 ◽  
Vol 62 (9) ◽  
pp. 1037-1059
Author(s):  
Yung-Ming Cheng
Keyword(s):  
Course Design ◽  
Learning System ◽  
Quality Factors ◽  
Continuance Intention ◽  
Design Quality ◽  
Content Type ◽  
Content Quality ◽  
Course Content ◽  
E Learning ◽  
Students Satisfaction

PurposeThe purpose of this study is to propose a research model based on expectation-confirmation model (ECM) to examine whether interactivity and course quality factors (i.e. course content quality, course design quality) as antecedents to student beliefs can influence students' satisfaction and continuance intention of the cloud-based electronic learning (e-learning) system within the educational institution.Design/methodology/approachSample data were collected from students enrolled in a comprehensive university in Taiwan. A total of 600 questionnaires were distributed in the campus, and 515 (85.8%) useable questionnaires were analyzed using structural equation modeling.FindingsFindings showed that students' perceptions of interactivity, course content quality and course design quality positively significantly contributed to their perceived usefulness, confirmation and satisfaction with the cloud-based e-learning system, which in turn directly or indirectly led to their continuance intention of the system. Thus, the results strongly supported the research model based on ECM via positioning key constructs as the drivers with all hypothesized links being significant.Originality/valueThis study identifies three factors (i.e. interactivity, course content quality, course design quality) as drivers from the learner perspective within the cloud-based e-learning environment, and links these factors to students' satisfaction and continuance intention of the cloud-based e-learning system based on ECM. It is particularly worth mentioning that the three drivers can serve as precursors for recognizing the determinants that are crucial to understand students' satisfaction and continuance intention of the cloud-based e-learning system. Hence, this study may provide new insights in nourishing the cloud-based e-learning continuance literature in the future.

scholarly journals Relationship Modeling of Critical Success Factors for Enhancing Sustainability and Performance in E-Learning

2018 ◽  
Vol 10 (12) ◽  
pp. 4776 ◽  
Author(s):  
Naim Ahmad ◽  
Noorulhasan Quadri ◽  
Mohamed Qureshi ◽  
Mohammad Alam
Keyword(s):  
Critical Success Factors ◽  
Success Factors ◽  
Course Design ◽  
Learning System ◽  
Interpretive Structural Modeling ◽  
Micmac Analysis ◽  
Critical Success ◽  
E Learning ◽  
And Performance ◽  
Depth Interviews

E-learning, a technology-mediated learning approach, is a pervasively adopted teaching/learning mode for transferring knowledge. Some of the motivational factors for its wide adoption are time and location independence, user-friendliness, on-demand service, resource richness, and multi-media and technology driven factors. Achieving sustainability and performance in its delivery is of paramount importance. This research utilizes the critical success factors (CSFs) approach to identify the sustainable E-learning implementation model. Fifteen CSFs have been identified through the literature review, expert opinions, and in-depth interviews. These CSFs have been modeled for interdependence using interpretive structural modeling and Matriced’ Impacts Croise’s Multiplication Appliquée a UN Classement (MICMAC) analysis. Further, the model has been validated through in-depth interviews. The present research provides quantification of CSFs of E-learning in terms of their driving and dependence powers and their classification thorough MICMAC analysis. The E-learning system organizers may focus on improving upon the enablers such as organizational infrastructure readiness, efficient technology infrastructure, appropriate E-learning course design, course flexibility, understandable relevant content, stakeholders’ training, security, access control and privileges, commitment, and being user–friendly and well-organized, in order to enhance the sustainability and performance in E-learning. This study will also help E-learning stakeholders in relocating and prioritizing resources.

Optimizing Student Engagement in Online Learning Environments

2018 ◽  
pp. 671-702
Author(s):  
Mukta Goyal ◽  
Rajalakshmi Krishnamurthy
Keyword(s):  
Information And Communication Technologies ◽  
Teaching And Learning ◽  
Communication Technologies ◽  
Learning System ◽  
Learning Material ◽  
Learning Materials ◽  
Efficient Manner ◽  
Course Content ◽  
Information And Communication ◽  
E Learning

In today's scenario, e-learning has become a significant part of the academic environment as well as of the corporate training sectors. Advancement in Information and Communication Technologies (ICTS) has brought new intersection of education, teaching, and learning that defines e-learning. E-learning systems deliver information for education at any time and at any place in an efficient manner. E-learning system consists of course content or learning materials in the form of nodes. These nodes are linked such that users can traverse the other nodes in the hypermedia environment. These learning concepts are available synchronously and asynchronously in different ways of representation. This presents learning materials in a disorganized manner to the learners. Due to this, learners may decline to adapt the learning material or may deviate from their goals. This requires a user model to respond to different needs of a learner. To handle the uncertainty of learner's mind while learning the concepts an intuitionistic fuzzy approach is used.

Engage Me Through BigBlueButton

2021 ◽  
pp. 1-15
Author(s):  
Lejla Turulja ◽  
Amra Kapo ◽  
Merima Činjarević
Keyword(s):  
Student Engagement ◽  
Undergraduate Students ◽  
Online Teaching ◽  
Student Perception ◽  
Value Theory ◽  
Learning System ◽  
Expectancy Value ◽  
Course Content ◽  
Course Rigor ◽  
E Learning

This study examines student engagement in an online environment concerning the perception regarding the course and the technology used. A research model was developed from the principal tenets of the expectancy-value theory to which values and expectations are assumed to influence how students build engagement. The model conjoins student perception related to course factors (content and rigor), technology factor (technology convenience), and student engagement (psychological, cognitive, emotional, and behavioral). The model was tested using a sample composed of 328 business undergraduate students taking the courses online using the BigBlueButton e-learning system due to the global emergency caused by the COVID-19 pandemic. Hence, respondents did not voluntarily choose the online teaching delivery method. The results imply that both course content and perceived technology convenience predict overall student engagement, while course rigor influences student cognitive, emotional, and behavioral commitment, but not psychological engagement.

Measuring E-Learning System Adoption in Universities in Tanzania

2020 ◽  
Vol 9 (2) ◽  
pp. 1-18
Author(s):  
Deogratius Mathew Lashayo
Keyword(s):  
Environmental Factors ◽  
Structural Equation ◽  
Learning Systems ◽  
Learning System ◽  
Clear Understanding ◽  
Perceived Benefits ◽  
Is Success ◽  
Actual Use ◽  
E Learning ◽  
The University

The success of e-learning systems in Tanzania relies on various factors that influence its measurement. Examples of the key factors include trust, environmental factors, and the university readiness. However, influence of these factors towards e-learning systems is not clear. Understanding their impacts and significance helps decision makers and stakeholders in making informed decisions on how to handle them. This study modifies the information systems (IS) success model whereby it adopts 12 factors that had been suggested by this author in his previous study conducted in Open University of Tanzania (OUT) in 2017. A sample of 1,005 students from eight universities in Tanzania was collected. A structural equation modelling was used in data analysis. The results shows trust (T) has positive and significant impact on e-learning actual use (EAU) while environmental factors (EF) had positive and significant impacts on e-learning actual use and perceived benefits, and at the same time, university readiness had a positive and significant impact on perceived benefits (PB).

scholarly journals Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Mushtaq Hussain ◽  
Wenhao Zhu ◽  
Wu Zhang ◽  
Syed Muhammad Raza Abidi
Keyword(s):  
Student Engagement ◽  
Student Performance ◽  
Virtual Learning Environment ◽  
Learning System ◽  
Final Exam ◽  
Course Assessment ◽  
Assessment Scores ◽  
Kappa Value ◽  
E Learning ◽  
Input Variables

Several challenges are associated with e-learning systems, the most significant of which is the lack of student motivation in various course activities and for various course materials. In this study, we used machine learning (ML) algorithms to identify low-engagement students in a social science course at the Open University (OU) to assess the effect of engagement on student performance. The input variables of the study includedhighest education level,final results,score on the assessment, and the number of clicks on virtual learning environment (VLE) activities, which includeddataplus,forumng,glossary,oucollaborate,oucontent,resources,subpages,homepage,and URLduring the first course assessment. The output variable was the student level of engagement in the various activities. To predict low-engagement students, we applied several ML algorithms to the dataset. Using these algorithms, trained models were first obtained; then, the accuracy and kappa values of the models were compared. The results demonstrated that the J48, decision tree, JRIP, and gradient-boosted classifiers exhibited better performance in terms of the accuracy, kappa value, and recall compared to the other tested models. Based on these findings, we developed a dashboard to facilitate instructor at the OU. These models can easily be incorporated into VLE systems to help instructors evaluate student engagement during VLE courses with regard to different activities and materials and to provide additional interventions for students in advance of their final exam. Furthermore, this study examined the relationship between student engagement and the course assessment score.

scholarly journals The Data of the Initial Motivation – a Valuable Source for the Development of the Course Content. A Case Study in the Group of Business Students

2018 ◽  
Vol 7 (2.28) ◽  
pp. 89
Author(s):  
Iveta Daugule ◽  
Atis Kapenieks
Keyword(s):  
Learning Environment ◽  
Business Students ◽  
Personal Characteristics ◽  
Learning System ◽  
Individual Motivation ◽  
Course Content ◽  
Study Materials ◽  
Learning Content ◽  
E Learning ◽  
Complex Knowledge

This study continues the authors’ previous search for key factors to determine the stickiness of different kinds of knowledge. In the authors’ opinion, identifying the stickiness of every type of knowledge in the e-course it is possible to create the optimal distribution of the intensity of time and study materials, paying more attention to the more complex knowledge, while promoting the acquisition of the free flow of knowledge with some tasks aimed at collaboration between students. Also, our previous study showed that individual motivation plays an important role in students’ success. That is why the scope of this study embraces the link of students' personal characteristics with their behavior and achievements in a learning environment. In this study, we are looking for key features for the future development of a more advanced learning system. Our scope of this study embraces the aspects of student’s initial motivation – how to ascertain, define and then use it for the development of the further learning content. Students were divided into three groups, according to their plans for engagement in business projects. The study was conducted in the course, that’s organized according to the blended learning delivery approach. Part of this course takes place in a classroom, while the other part is embedded in an electronic environment on an Open edX platform-based E-Learning environment.  

scholarly journals E-Learning System Use During Emergency: An Empirical Study During the COVID-19 Pandemic

2021 ◽  
Vol 6 ◽  
Author(s):  
Ibrahim Alyoussef
Keyword(s):  
Structural Equation ◽  
Online Survey ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Learning System ◽  
Equation Modeling ◽  
Perceived Ease Of Use ◽  
Positive Effects ◽  
Actual Use ◽  
E Learning

The pandemic of COVID-19 quickly led to the closure of universities and colleges around the world, hoping that the guidance of social distancing from public health authorities will help flatten the curve of infection and minimize the overall fatalities from the epidemic. The e-learning framework, however, is the best solution to enable students to learn about the quality of education. The aim of this research was to examine variables reflecting the actual use of the e-learning system during the COVID-19 pandemic among university students. The perceived ease of use and perceived usefulness are positively correlated with facilitating condition, perceived control, and self-efficacy, which in turn influences students’ attitude toward use, which in turn affects the actual use of the e-learning system during the COVID-19 pandemic. To exam the model on the basis of user data from the e-learning system used collected through an online survey, structural equation modeling (SEM) and path analysis were used. The findings showed that the mindset of students to use had positive effects on the learning of students during the COVID-19 pandemic through the actual use of the e-learning system. In the context of e-learning programs in developing countries, previous studies have seldom explored an integrated model. In addition, this article aims to include a literature review of recently published research on the actual use of the e-learning system during the pandemic of COVID-19.

scholarly journals Adaptive Virtual Learning Environment based on Learning Styles for Personalizing E-learning System: Design and Implementation

2020 ◽  
Vol 8 (6) ◽  
pp. 3398-3406
Keyword(s):  
Learning Environment ◽  
Learning Styles ◽  
User Interfaces ◽  
Adaptive Learning ◽  
Architectural Design ◽  
Virtual Learning ◽  
Virtual Learning Environment ◽  
Learning System ◽  
Course Content ◽  
E Learning

Most virtual learning environment fails to recognize that students have different needs when it comes to learning. With the evolving characteristics and tendencies of students, these learning environments must provide adaptation and personalization features for adaptive learning materials, course content and navigational designs to support student’s learning styles. Based from the data mining results of learner behavioral features of five hundred seven (507) tertiary students, an accurate model for classification of student’s learning styles were derived using J48 decision tree algorithm. The model was implemented in a prototype using a framework and a proposed system architectural design of an adaptive virtual learning environment. The study resulted in the development of an adaptive virtual learning environment prototype where learner’s preferences are dynamically diagnosed to intelligently personalize the course content design and user interfaces for them.

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