Skip to content
Research Article Open access CC BY 4.0

Investigating Online Learning Satisfaction in Jordan Schools during the COVID-19 Outbreak: The Student Perspective

Hayfaa Jebril

Asian Journal of Education and Social Studies · pp. 46–57 · Published 25 Nov 2021

10.9734/ajess/2021/v23i430565

Abstract

In the past two years, the flare-up of the COVID-19 pandemic in Jordan has turned traditional education into online teaching, which impacted the education quality. This studyuses an integrated model of UTAUT model, ISS model, and quality of interaction to study the online learning satisfaction in Jordan from the students' point of view during the COVID-19 outbreak. For such purpose, a questionnaire survey was distributed among students from five different Jordanian schools to gather the data, and the SmartPLS tool was used to analyze this data. The results showed that all the employed factors in the proposed model significantly influenced the students' satisfaction with online learning throughout the COVID-19 pandemic proliferation. Thus, these research results will be a significant reference for educational institutes, decision-makers, developers, and researchers on the significant factors that influence students' satisfaction with online learning, which in turn can improve the development of online learning systems.

Online learning students’ satisfaction education quality

References (38)

  1. 1 User Acceptance of Information Technology: Toward A Unified View1 [DOI]
  2. 2 The DeLone and McLean Model of Information Systems Success: A Ten-Year Update [DOI]
  3. 3 Information Systems Success: The Quest for the Dependent Variable [DOI]
  4. 4 Closure of Universities Due to Coronavirus Disease 2019 (COVID-19): Impact on Education and Mental Health of Students and Academic Staff [DOI]
  5. 5 Understanding e-learning continuance intention: An extension of the Technology Acceptance Model [DOI]
  6. 6 A review of technology acceptance and adoption models and theories [DOI]
  7. 7 Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model [DOI]
  8. 8 Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors [DOI]
  9. 9 Predicting student satisfaction and perceived learning within online learning environments [DOI]
  10. 10 A theory of online learning as online participation [DOI]
  11. 11 Analysis of User Satisfaction with Online Education Platforms in China during the COVID-19 Pandemic [DOI]
  12. 12 Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context [DOI]
  13. 13 Assessing the Relationship of Student-Instructor and Student-Student Interaction to Student Learning and Satisfaction in Web-Based Online Learning Environment
  14. 14 Applying the UTAUT Model to Explain the Students’ Acceptance of Mobile Learning System in Higher Education [DOI]
  15. 15 Investigating e-learning system usage outcomes in the university context [DOI]
  16. 16 Extending the TAM to examine the effects of quality features on mobile learning acceptance [DOI]
  17. 17 Developing a Holistic Success Model for Sustainable E-Learning: A Structural Equation Modeling Approach [DOI]
  18. 18 Analysis of Student Satisfaction Toward Quality of Service Facility [DOI]
  19. 19 Toward an Understanding of University Students’ Continued Intention to Use MOOCs: When UTAUT Model Meets TTF Model [DOI]
  20. 20 Structural Relationship of Key Factors for Student Satisfaction and Achievement in Asynchronous Online Learning [DOI]
  21. 21 An analysis of factors affecting students´ perceptions of learning outcomes with Moodle [DOI]
  22. 22 Investigating the effects of quality factors and organizational factors on university students’ satisfaction of e-learning system quality [DOI]
  23. 23 Measuring Students Satisfaction in Using Learning Management System [DOI]
  24. 24 E-Learning: Confusing Terminology, Research Gaps and Inherent Challenges
  25. 25 Frequency, intensity and topicality in online learning: An exploration of the interaction dimensions that contribute to student satisfaction in online learning
  26. 26 Interaction and e-learning: the student experience [DOI]
  27. 27 Robot Intelligence Technology and Applications 2012 [DOI]
  28. 28 TRANSACTIONAL DISTANCE AND STUDENT SATISFACTION IN A POSTGRADUATE DISTANCE LEARNING PROGRAM [DOI]
  29. 29 Novice Researchers’ Views About Online Ethics Education and the Instructional Design Components that May Foster Ethical Practice [DOI]
  30. 30 A New Approach to Measuring Information Systems Quality [DOI]
  31. 31 A Study on the Technical Evaluation of the Quality Certification for e-Learning Contents [DOI]
  32. 32 The Mediating Role of Student Satisfaction in the Relationship between Determinants of Online Student Satisfaction and Student Commitment [DOI]
  33. 33 The Paradox of Online Education: Images, Perceptions, and Interests [DOI]
  34. 34 Effect of Student Satisfaction on e-learning Quality and Learning Outcome Among Malaysian Undergraduate Nursing Students [DOI]
  35. 35 The Reality of Using Darsak Platform and Its Obstacles By The Teachers of Primary School in Southern Amman Schools in Light of The Corona Pandemic COVID-19 [DOI]
  36. 36 Key Factors Affecting Students’ Satisfaction and Intention to Use e-Learning in Rwanda’s Higher Education [DOI]
  37. 37 Students’ Continuance of Using E-Learning System: A Review of Conceptual Frameworks [DOI]
  38. 38 A Study of Factors Affecting on m-learning Satisfaction based on UTAUT [DOI]

Cited by 6

Factors Affecting School Teachers' Satisfaction with Online Classes during the Outbreak of COVID-19

Fatima Mosleh Falah Alfalahat · Asian Journal of Education and Social Studies · 2021

Showing 2 of 6 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

26

Page views

0

PDF downloads

4

Outbound clicks

6

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.