Doctoral Students’ Learning Success in Online-Based Leadership Programs: Intersection With Technological and Relational Factors
Bibliographic record
Abstract
This study examines how technological and relational factors independently and interactively predict the perceived learning success of doctoral students enrolled in online-based leadership programs offered in the United States. The 73-item Online Learning Success Scale (OLSS) was constructed, based on existing instruments, and administered online to collect self-reported data on three primary variables: student learning success (SLS), relational factors (RF), and technological factors (TF). The SLS variable focuses on the gain of knowledge and skills, persistence, and self-efficacy; the RF on the student-student relationship, the student-faculty relationship, and the student-non-teaching staff relationship; and the TF on the ease of use, flexibility, and usefulness. In total, 210 student responses from 26 online-based leadership doctoral programs in the United States were used in the final analysis. The results demonstrate that RF and TF separately and together predict SLS. A multiple regression analysis indicates that, while all dimensions of TF and RF are significant predictors of SLS, the strongest predictor of SLS is the student-faculty relationship. This study suggests that building relationships with faculty and peers is critical to leadership doctoral students’ learning success, even in online-based programs that offer effective technological support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".