Snapshots from the Lived World of Massive Open Online Courses (MOOCs)
Bibliographic record
Abstract
This paper reports on preliminary findings of a phenomenological study examining students’ everyday experiences of learning in a Massive Open Online Course (MOOC). The current discourse surrounding MOOCs is powerful, with promises of an epochal shift in post-secondary education, unprecedented openness, democratic pedagogies, less hierarchical knowledge creation, and unimagined scalability: all of which require critical examination. But with a brief five-year history, research has yet to confirm or refute these bold claims rationalizing the popularity and efficacy of these big virtual learning environments and their disruptive, game-changing potential for education. A swift and timely “counterbalance to some of the more hyperbolic elements of current discourse” is needed, in particular, through providing accounts of the complex realities of learners’ actual experiences (Selwyn 2009). The study collected and analysed experiential moments recollected by “completers” while learning in a Massive Open Online Course. For the purposes of this study, a “completer” (Kizilcec, Piech & Schneider, 2013, April) was defined as a student who enrolled in at least one MOOC and in which they accomplished the majority of activities, assignments, quizzes and/or examinations set out by the curriculum. Data was generated via two main sources: written self-protocols (daily journals maintained by four adults engaged in a self-chosen MOOC) as well as in-depth phenomenological interviews with six MOOC completers recruited via snowball sampling. Our study revealed several surprising results. The MOOC completers consistently described a unique and powerful sphere of intimacy that developed for them with their MOOC instructor, most especially in the context of the pre-recorded instructor videos. Too, our findings seem to confirm Cormier’s (2009) conjecture that “eventedness”—the sense of specialness characteristic of other “big”, shared events like a rock concert or major sporting event—may uniquely distinguish MOOCs from other online learning experiences. The paper provides several rich, experiential “snapshots” or textual descriptions of learning moments and recollected events in a MOOC. Through phenomenological analysis of these lived experience descriptions, we show how the virtual learning landscapes afforded by these large-scale online environments may create unique conditions, situations, and relations of pedagogical effect and influence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| 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".