UNDERSTANDING THE FACULTY EXPERIENCE DESIGNING, DEVELOPING, AND DELIVERING MASSIVE OPEN ONLINE COURSES TO INFORM ACADEMIC LEADERS CONSIDERING MOOC INITIATIVES
Bibliographic record
Abstract
The work of academic faculty is what defines institutions of higher learning (Steward, 2013). Institutional leaders and decision-makers need valid, qualitative research information regarding faculty lived experiences in order to understand the opportunities and challenges of designing, developing, and delivering instruction on a massive scale. From 2008 to 2011 the Massive Open Online Course (MOOC) went from an obscure experimental course to full-scale adoption by world-renowned institutions without consulting experts in the field of online learning, utilized older pedagogical frameworks, and still few have asked the academic faculty designing, developing, and delivering MOOCs if MOOCs are a viable learning experience or if MOOCs further institutional goals. The researcher chose to conduct a classical phenomenology by developing a 10 question semi-structured telephonic interview (Crotty, 1998; Husserl, 1931). Seven participants, four male, three female from the United States and Canada offered answers to the interview which resulted in rich data regarding their lived experiences. MOOCs can be extremely expensive and take an excessive amount of a professor’s time and energy to do well. Currently, MOOCs have not proved to be the educational panacea many had hoped however, MOOCs are likely here to stay for the foreseeable future as rapid changes become the new normal for higher education. Because of the emerging nature of this field of research numerous opportunities for future research are open. Institutional leaders need better understanding of costs and learning outcomes in MOOCs in order to evaluate the challenges and opportunities posed by MOOC initiatives in their respective institutions.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".