Impact of E-Learning Orientation, Moodle Usage, and Learning Planning on Learning Outcomes in On-Demand Lectures
Open accessAbstract
The growing prominence of e-learning in education has led to the need for a comprehensive understanding of the factors influencing learning outcomes. This study aims to investigate the combined effects of e-learning orientation, Moodle usage, and learning planning on learning outcomes in the context of on-demand lectures. A sample of participants from the Department of Information Science and Engineering in the Faculty of Engineering completed questionnaires related to e-learning orientation, while Moodle usage data and learning planning information were collected. Correlation, principal component, cluster, and multiple regression analysis were conducted to examine the relationships between variables and their impact on learning outcomes. The results suggest that e-learning orientation did not exert a significant influence on learning outcomes. However, Moodle usage and learning planning emerged as crucial factors. Increased engagement with Moodle, as indicated by higher clicks and utilization of its learning functionalities, was associated with improved learning outcomes. Additionally, effective learning planning, characterized by adherence to schedules and timely submissions, positively influenced learning outcomes. The results emphasize the importance of considering multiple factors, not just a single factor, for successful online learning. These findings provide valuable insights for optimizing learning outcomes in on-demand lectures.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
Using Learning Analytics to Predict Students Performance in Moodle LMS
Zhang, Y., Ghandour, A., & Shestak, V. A. (2020) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v15i20.15915 · Cited by 87 · see citing works
RetractedReliability and Predictive Validity of the Motivated Strategies for Learning Questionnaire (Mslq)
Pintrich, P. R., Smith, D. A. F., Garcia, T., & Mckeachie, W. J. (1993) · Educational and Psychological Measurement · doi:10.1177/0013164493053003024 · Cited by 3,646 · see citing works
Self-Regulated Learning and Academic Achievement
Zimmerman, B. J., & Schunk, D. H. (1989) · Springer series in cognitive development · doi:10.1007/978-1-4612-3618-4 · Cited by 1,885 · see citing works
Broadbent, J., & Poon, W. L. (2015) · The Internet and Higher Education · doi:10.1016/j.iheduc.2015.04.007 · Cited by 1,692 · see citing works
Maatuk, A. M., Elberkawi, E. K., Aljawarneh, S. A., Rashaideh, H., & Alharbi, H. F. (2021) · Journal of Computing in Higher Education · doi:10.1007/s12528-021-09274-2 · Cited by 832 · see citing works
Boekaerts, M., Pintrich, P. R., & Zeidner, M. (2000) · Handbook of Self-Regulation · doi:10.1016/b978-012109890-2/50030-5 · Cited by 449 · see citing works
COVID-19 and E-Learning: the Challenges of Students in Tertiary Institutions
Aboagye, E. T., Yawson, J. A., & Appiah, K. N. (2020) · Social Education Research · doi:10.37256/ser.212021422 · Cited by 445 · see citing works
Social Cognitive Theory and Motivation
Schunk, D. H., & Usher, E. L. (2012) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780195399820.013.0002 · Cited by 415 · see citing works
Identifying significant indicators using LMS data to predict course achievement in online learning
You, J. W. (2016) · The Internet and Higher Education · doi:10.1016/j.iheduc.2015.11.003 · Cited by 388 · see citing works
Reviewing and exploring innovative ubiquitous learning tools in higher education
Aljawarneh, S. A. (2020) · Journal of Computing in Higher Education · doi:10.1007/s12528-019-09207-0 · Cited by 333 · see citing works
Self-Regulated Learning and Academic Achievement: A Vygotskian View
Rohrkemper, M. M. (1989) · Springer series in cognitive development · doi:10.1007/978-1-4612-3618-4_6 · Cited by 276 · see citing works
Developing Adaptive Teaching Competency through coaching
Vogt, F., & Rogalla, M. (2009) · Teaching and Teacher Education · doi:10.1016/j.tate.2009.04.002 · Cited by 254 · see citing works
Model-based overlapping clustering
Banerjee, A., Krumpelman, C. S., Ghosh, J., Basu, S., & Mooney, R. J. (2005) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1081870.1081932 · Cited by 210 · see citing works
The Influence of Learning Styles on Learners in E-Learning Environments: An Empirical Study
Manochehr, N.-N. (2006) · RePEc: Research Papers in Economics · Cited by 173 · see citing works
Examining the Effect of Academic Procrastination on Achievement Using LMS Data in E-Learning.
You, J. W. (2015) · Educational Technology & Society · Cited by 133 · see citing works
Impact of the COVID-19 pandemic on online learning in higher education: a bibliometric analysis
Aristovnik, A., Karampelas, K., Umek, L., & Ravšelj, D. (2023) · Frontiers in Education · doi:10.3389/feduc.2023.1225834 · Cited by 107 · see citing works
Special issue on the current trends in E-learning Assessment
Lara, J. A., Aljawarneh, S. A., & Pamplona, S. (2019) · Journal of Computing in Higher Education · doi:10.1007/s12528-019-09235-w · Cited by 95 · see citing works
Study of Usage Patterns and Learning Gains in a Web‐based Interactive Static Course
Steif, P. S., & Dollár, A. (2009) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2009.tb01030.x · Cited by 71 · see citing works
Alserhan, S., Alqahtani, T. M., Yahaya, N., Al-Rahmi, W. M., & Abuhassna, H. (2023) · IEEE Access · doi:10.1109/access.2023.3236504 · Cited by 65 · see citing works
Motivation and Learning Strategies for College Success
Seli, H., & Dembo, M. H. (2019) · Routledge eBooks · doi:10.4324/9780429400711 · Cited by 43 · see citing works
Cobb, J. R. (2003) · VTechWorks (Virginia Tech) · Cited by 42 · see citing works
Ocak, G., & Boyraz, S. (2016) · Journal of Education and Training Studies · doi:10.11114/jets.v4i5.1313 · Cited by 42 · see citing works
On the Impact of Personality in Massive Open Online Learning
Chen, G., Davis, D., Hauff, C., & Houben, G. (2016) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/2930238.2930240 · Cited by 40 · see citing works
Time for teacher learning, planning critical for school reform
Merritt, E. G. (2016) · Phi Delta Kappan · doi:10.1177/0031721716681774 · Cited by 35 · see citing works
Tan, T. K., & Lakshminarayanan, S. (2022) · Computers and Education Open · doi:10.1016/j.caeo.2022.100112 · Cited by 29 · see citing works
Teachers’ Perceptions of Remote Learning during the Pandemic: A Case Study
Silva, S., Fernandes, J., Péres, P., Lima, V. M., & Silva, C. E. P. (2022) · Education Sciences · doi:10.3390/educsci12100698 · Cited by 26 · see citing works
Deemer, E. D., Yough, M. S., & Morel, S. A. (2017) · Motivation and Emotion · doi:10.1007/s11031-017-9649-z · Cited by 18 · see citing works
Changes in online course designs: Before, during, and after the pandemic
Misiejuk, K., Ness, I. J., Gray, R., & Wasson, B. (2023) · Frontiers in Education · doi:10.3389/feduc.2022.996006 · Cited by 17 · see citing works
Zitha, I., Mokganya, M. G., & Manyage, T. (2023) · Education Sciences · doi:10.3390/educsci13070704 · Cited by 14 · see citing works
Santos-Berbel, C. D., García, J. I. H., & Berbel, L. D. S. (2022) · Education Sciences · doi:10.3390/educsci12080561 · Cited by 10 · see citing works
Who Will Continue Using MOOCs in the Future? Personality Traits Perspective
Abdullatif, H. A. A., & Velázquez‐Iturbide, J. Á. (2020) · IEEE Access · doi:10.1109/access.2020.2979180 · Cited by 7 · see citing works
Tin, T. T., Teh, S. L., & Wee, M. C. (2022) · Education Sciences · doi:10.3390/educsci12120935 · Cited by 3 · see citing works
Watanabe, F., & Kogo, C. (2017) · Journal on Educational Technology · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- Sugiura, M.; Kogo, C. Effectiveness of the Training Which is Combined e-Learning and Realistic Simulation toward Sudden Changes of Patients. Res. Rep. JSET Conf. 2013, 13, 81-88.
- Watanabe, F.; Kogo, C. Causal Relationship among Questionnaire Variables Investigating Learners' e-Learning Orientation in Massive Online Courses. Jpn. J. Educ. Technol. 2017, 41, 77-87. [CrossRef] · DOI
- Zimmerman, B.J.; Schunk, D.H. (Eds.) Self-regulated learning and performance: An introduction and an overview. In Handbook of Self-Regulation of Learning and Performance, 1st ed.; Routledge: New York, NY, USA, 2011; pp. 1-12.
- Boekaerts, M., Pintrich, P.R., Zeidner, M., Eds.; Academic Press: San Diego, CA, USA, 2000; pp. 13-39.
- Aida, S. Data Science Technology, 3rd ed.; Gakujutsu Tosho Shuppan-Sha Co., Ltd.: Tokyo, Japan, 2023; pp. 1-209.
- Jonassen, D.H.; Land, S.M. (Eds.) Theoretical Foundations of Learning Environments; Lawrence Erlbaum Associates Publishers: Mahwah, NJ, USA, 2000.
Cited by
Sorted by citations
Unpacking student engagement in higher education learning analytics: a systematic review
Bergdahl, N., Bond, M., Sjöberg, J., Dougherty, M., & Oxley, E. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00493-y · Cited by 138 · see citing works
Zitha, I., Masete, D., & Matsetela, S. (2025) · E-Journal of Humanities Arts and Social Sciences · doi:10.38159/ehass.20256712 · Cited by 4 · see citing works
Chikuvadze, P., Permana, E. P., Makuvire, C., & Mugijima, S. (2025) · Jurnal Simki Pedagogia · doi:10.29407/jsp.v8i1.1080 · Cited by 1 · see citing works
Vlassopoulos, G., Karikas, G. A., Papageorgiou, E. G., Psaromiligkos, I., Giannouli, N., Vlassopoulou, P., & Καρκαλούσος, Π. (2024) · Journal of Intelligent Learning Systems and Applications · doi:10.4236/jilsa.2024.163016
Strategy for Quality Assurance of Leading School Education in Lamongan Regency
Mumayyizah, M., Sujarwanto, S., Hariyati, N., & Saphıra, H. V. (2024) · IJORER International Journal of Recent Educational Research · doi:10.46245/ijorer.v5i6.710
Rusmiyati, A., Marwoto, P., Subali, B., & Rusilowati, A. (2024) · Unnes Science Education Journal · doi:10.15294/usej.v13i3.13349
Salamah, Wilujeng, İ., & Muthmainah (2026) · Multidisciplinary Reviews · doi:10.31893/multirev.2026378
Sarika, K., Genitha, C. H., M, V. A., & A, A. T. (2025) · International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF) · doi:10.1109/iceconf65644.2025.11379464
- Authors
- Aida, S.
- Affiliations
- Yamaguchi University (JP)
- Corresponding author
- Aida, Saori
- ORCID
- Aida, Saori
- Venue
- Education Sciences
- Publisher
- MDPI AG
- Published
- 2023-10-03
- Volume
- 13
- Issue
- 10
- Pages
- 1005
- ISSN
- 2227-7102 (ISSN-L)
- OpenAlex
- W4387342771
- Cited by
- 8
- References
- 32
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4
- FWCI
- 1.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Education Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
- Social Sciences › Education › Online and Blended Learning
- Social Sciences › Education › Technology-Enhanced Education Studies
- Keywords
- learning planning, learning outcomes, on-demand lectures, online learning, student engagement, engineering students
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review