Understanding online learning dropout: Integrative perspective
Bibliographic record
Abstract
Information and communication technologies (ICTs) have become the preferred medium for distance learning services (Zerkouk, Mihoubi, Chikhaoui, & Wang, 2025), significantly reducing both temporal and spatial constraints, including geographical disparities. Currently, online learning is increasingly becoming a viable option for many instructors and universities to meet the evolving needs of students. However, while online learning has grown, low student retention rates have emerged as a prominent issue, with only about 15% of Open University students completing their degrees (Mishra, 2017). Furthermore, dropout rates for Massive Open Online Courses (MOOCs) can be as high as 90% (Li, Zhao, Yan, Zou, Xiao, & Qian, 2023). Retention remains a universal challenge, and widespread dropout rates threaten the future development of online learning. Recent efforts have aimed at understanding the factors influencing online dropout rates in higher education. Nonetheless, existing studies do not provide a comprehensive view of these factors. Despite numerous proposed factors, there is no consensus on the most relevant ones. This lack of agreement arises from several challenges, including the difficulty of comparing different studies, the challenge of assessing the effects of these factors over time, issues with validating measurement instruments, and the absence of standardization and accumulation of knowledge in this research area. It is also important to note that no single factor can fully explain the high dropout rate; additionally, what may be considered a positive factor in one context could also be inadequate in another. Consequently, there is a gap in our understanding of how various elements interact to explain dynamics within a constantly evolving digital learning environment that contributes to student dropout. This research can generate new and interesting insights and extend our current understanding of online learning sustainability by filling that gap. This study aims to empower key stakeholders to embrace and leverage online learning by uncovering the factors driving the intention to continue using this emerging approach. Understanding these dynamics is vital, as they play a pivotal role in shaping the long-term success of online learning. Gaining this insight is not just beneficial; it is essential for fostering a thriving learning environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".