The “Handbook for Distance Education” begins with a fourpage preface followed by an eleven page overview, both written by Dr. Michael Moore. The book is then divided into seven sections, each section containing several chapters (55 in total). Each chapter is written by a well known (previously published in The American Journal of Distance Education) author or collection of authors. The title of each section and the number of chapters found in each section is shown below:

Handbook of distance education

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 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 Recommendations 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

/pdf/factors-affecting-the-quality-of-e-learning-during-the-covid-3zjckzcs7t.pdf

Factors affecting the quality of e-learning during the covid-19 pandemic from the perspective of higher education students

In the last decade, online learning has grown rapidly However, the outbreak of coronavirus (COVID-19) has caused learning institutions to embrace online learning due to the lockdown and campus closure This paper presents an analysis of students’ feedback (n=354) from the Centre of Pre-University Studies (PPPU), Universiti Malaysia Sarawak (UNIMAS), Malaysia, during the transition to fully online learning Three phases of online surveys were conducted to measure the learners’ acceptance of the migration and to identify related problems The result shows that there is an increased positivity among the students on the vie of teaching and learning in STEM during the pandemic It is found that online learning would not be a hindrance, but blessing towards academic excellence in the face of calamity like the COVID-19 pandemic The suggested future research direction will be of interest to educators, academics, and researchers’ community

/pdf/transitioning-to-online-learning-during-covid-19-pandemic-3npt1gxauv.pdf

Transitioning to Online Learning during COVID-19 Pandemic: Case Study of a Pre-University Centre in Malaysia

E-learning in higher education is exponentially increased during the past decade due to its inevitable benefits in critical situations like natural disasters (e.g. COVID-19 pandemic etc.) and war circumstances. The reliable, fair, and seamless execution of online exams in E-learning is highly significant. Particularly, online exams are conducted on E-learning platforms without the physical presence of students and instructors at the same place. This poses several issues like integrity and security during online exams. To address such issues, researchers frequently proposed different techniques and tools. However, a study summarizing and analyzing latest developments, particularly in the area of online examination, is hard to find in the literature. In this article, a Systematic Literature Review (SLR) of online examination is performed to select and analyze 53 studies published during the last five years (i.e. Jan 2016 to July 2020). Subsequently, five leading online exams features targeted in the selected studies are identified. Moreover, underlying development approaches for the implementation of online exams solutions are explored. Furthermore, 16 important techniques / algorithms and 11 datasets are presented. In addition to this, 21 online exams tools proposed in the selected studies are identified. Additionally, 25 leading existing tools used in the selected studies are also presented. Finally, the participation of countries in online exam research is investigated. Key factors for the global adoption of online exams are identified and compared with major online exams features. This facilitates the selection of right online exam system for a particular country on the basis of existing E-learning infrastructure and overall cost. To conclude, the findings of this article provide a solid platform for the researchers and practitioners of the domain to select appropriate features along with underlying development approaches, tools, and techniques for the implementation of a particular online exams solution as per given requirements.

/pdf/a-systematic-review-of-online-exams-solutions-in-e-learning-4phumx0fcu.pdf

A Systematic Review of Online Exams Solutions in E-Learning: Techniques, Tools, and Global Adoption

E-learning in higher education is exponentially increased during the past decade due to its inevitable benefits in critical situations like natural disasters, and pandemic. The reliable, fair, and seamless execution of online exams in E-learning is highly significant. Particularly, online exams are conducted on E-learning platforms without the physical presence of students and instructors at the same place. This poses several issues like integrity and security during online exams. To address such issues, researchers frequently proposed different techniques and tools. However, a study summarizing and analyzing latest developments, particularly in the area of online examination, is hard to find in the literature. In this article, an SLR for online examination is performed to select and analyze 53 studies published during the last five years. Subsequently, five leading online exams features targeted in the selected studies are identified and underlying development approaches for the implementation of online exams solutions are explored. Furthermore, 16 important techniques and 11 datasets are presented. In addition, 21 online exams tools proposed in the selected studies are identified. Additionally, 25 leading existing tools used in the selected studies are also presented. Finally, the participation of countries in online exam research is investigated. Key factors for the global adoption of online exams are identified and investigated. This facilitates the selection of right online exam system for a particular country on the basis of existing E-learning infrastructure and overall cost. To conclude, the findings of this article provide a solid platform for the researchers and practitioners of the domain to select appropriate features along with underlying development approaches, tools and techniques for the implementation of a particular online exams solution as per given requirements.

A Systematic Review of Online Exams Solutions in E-learning: Techniques, Tools and Global Adoption

Learning persistence is a critical element for successful online learning. The evidence provided by psychologists and educators has shown that students’ interaction (student-student (SS) interaction, student-instructor (SI) interaction, and student-content (SC) interaction) significantly affects their learning persistence, which is also related to their academic emotions. However, few studies explore the relations among students’ interaction, academic emotions and learning persistence in online learning environments. Furthermore, no research has focused on multi-dimensional students’ interaction and specific academic emotions. Based on person-environment interaction model and transactional distance theory, this study investigates the relationship between students’ interaction and learning persistence from the perspective of moderation and mediation of academic emotions including enjoyment, boredom, and anxiety. Data were collected from 339 students who had online learning experience in China. AMOS 22.0 (IBM, Armonk, NY, USA) and SPSS 22.0 (IBM, Armonk, NY, USA) were employed to analyze the mediating and moderating effects of academic emotions, respectively. The results revealed that students’ interaction and academic emotions directly related to learning persistence. Specifically, enjoyment, anxiety and boredom had significant mediating and moderating effects on the relationship between students’ interaction and learning persistence. Based on these findings, we further discussed the theoretical and practical implications on how to facilitate students’ learning persistence in online learning environments.

Investigating the Influence of Interaction on Learning Persistence in Online Settings: Moderation or Mediation of Academic Emotions?

Social participation of the elderly in China: The roles of conventional media, digital access and social media engagement

Exam paper generation based on performance prediction of student group

Online learning has become a vital option for ensuring daily instruction in response to the emergence of the COVID-19 epidemic. However, different from conventional massive online learning, inadequate available data bring challenges for instructors to identify underachieving students in school-based online learning, which may obstruct timely guidance and impede learning performance. Exploring small-sample-supported learning performance prediction and personalized feedback methods is an urgent need to mitigate these shortcomings. Consequently, considering the problem of insufficient data, this study proposes a machine learning model for learning performance prediction with additional pre-training and fine-tuning phases, and constructs a personalized feedback generation method to improve the online learning effect. With a quasi-experiment involving 62 participants (33 in experimental group and 29 in control group), the validity of the prediction model and personalized feedback generation, and the impact of the personalized feedback on learning performance and cognitive load, were evaluated. The results revealed that the proposed model reached a relatively high level of accuracy compared to the baseline models. Additionally, the students who learned with personalized feedback performed significantly better in terms of learning performance and showed a lower cognitive load.

/pdf/learning-performance-prediction-based-personalized-feedback-2eccszok.pdf

Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning

Investigating students’ emotional self-efficacy profiles and their relations to self-regulation, motivation, and academic performance in online learning contexts: A person-centered approach

Tao He

Papers

Investigating the Influence of Interaction on Learning Persistence in Online Settings: Moderation or Mediation of Academic Emotions?

Social participation of the elderly in China: The roles of conventional media, digital access and social media engagement

Exam paper generation based on performance prediction of student group

Learning Performance Prediction-Based Personalized Feedback in Online Learning via Machine Learning

Investigating students’ emotional self-efficacy profiles and their relations to self-regulation, motivation, and academic performance in online learning contexts: A person-centered approach