The H.E.R.O.s of Online Education: What makes students succeed despite the odds?
Bibliographic record
Abstract
The study of psychological capital (PsyCap) is prevalent in organizations globally and is part of the movement towards attaining positive organizational behavior. This concept is slowly being transferred to the education realm with teachers becoming more mindful of students’ inner H. E. R. O. (Hope, Efficacy, Resilience, and Optimism). Little research, however, has been conducted upon the PsyCap of university students in fully online programs. The purpose of this study was to determine what aspects of students’ psychological capital lead to success despite adversity. An exploratory qualitative methodology was used to interview five participants from the United States, Canada, Africa, France, and Serbia in order to determine whether or not PsyCap influenced their drive to complete their online graduate programs of study at the University of Liverpool. This convenience sample yielded compelling results for future research and indicated similarities in hope and efficacy as well as differences in gender regarding participant resilience and approach to challenges. Further research is needed to determine whether gender does play a critical role in online students’ PsyCap, especially resilience. Another revealing result was that the participants credited their online instructors for motivating and discouraging them based on their feedback, grading, and overall communication. This points to a possible relationship between the students’ PsyCap and the three online teaching presences in communities of inquiry (cognitive, teaching, and social).
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".