The Factors That Make an Online Learning Experience Powerful: Their Roles and the Relationships Amongst Them
Bibliographic record
Abstract
The rapid surge of digitalization in the education sector has redefined the dynamics of the modern classroom. In a world that embraces convenience, online learning has established its own relevance and has been a subject of considerable interest to researchers worldwide. The majority of studies have focused upon factors that make or break the success of an online program. In the current paper we attempt to model these factors by analyzing the relationships and interplay among them. The methodology of Interpretive Structural Modelling was applied to identify the most critical factors and analyze how they interact to determine the quality of the learning experience. A directed graph model representing the interplay of these factors was developed to identify the strongest drivers called strategic variables, the workable factors called operational variables and the dependent factors which subsequently lead to the success of an online program. The results of this research can enable the practitioners to focus on the right variables at the right time for ensuring the success of an online program. For researchers, the findings provide a holistic platform to empirically explore the relationships between variables.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".