Working Together: The Context of Teams in an Online MBA Program
Bibliographic record
Abstract
The purpose of this study was to explore learning in an online MBA program and the structures necessary to support and enhance that learning from the perspective of the learners. This qualitative study was multi-method in nature, and included in-depth interviews (the focus of this report) with ten of the 32 learners who participated in the study, as well as a document review of course transcripts. Findings indicated that the collaborative nature of the teamwork required in this web-based MBA program provided major benefits as well as challenges for the learners in this program. Benefits included decreased feelings of isolation, the development of support systems, authentic "real world" learning, and an improved ability to communicate clearly online. Challenges included learning how to deal with the different learning styles, academic goals, and varying time commitments to the program of various team members. Learners recognized the pace of the online work, the requirements for technical competence, and the need for alternative communication links as other factors in their learning experience. Learners also identified a strong perception of academic efficacy, and an affirmation of their own individual growth and development through their work in the online MBA program.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.034 | 0.014 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".