META-ANALYSIS OF THE RESEARCH ABOUT MOOC DURING 2013-2014
Bibliographic record
Abstract
The first MOOC (Massive Open Online Course) was launched in 2008 in Canada. Since then these new model of online education has proliferated around the world and sparked many interesting and often heated discussions regarding their benefits and implications in the field of education. In order to understand and contribute to the debate surrounding MOOCs and their educational possibilities it is necessary to go beyond opinion, intuition or isolated experiences. It is necessary to have evidence that allows for systematic, detailed and contrastive evaluation. Following the methodology used in an earlier investigation that analyzed publications from the first five years of MOOC delivery, this article looks at studies that focus on MOOCs between 2013-2014. Through a systematic search of the available literature, we found 228 investigative works, published in peer reviewed journals. A quantitative and qualitative analysis of these publications is presented. Classification was based on the year of publication, the type of publication and eleven distinct categories we found of interest. We found that increases in the number of publications and, to a lesser extent, presentations at conferences. Pedagogical strategies are the most common focus as well as learner motivation, presentence and implications for higher education systems. The reach/scope of the MOOC phenomenon for online teaching has sparked and challenged both institutions (their structure, pedagogical model, management and business) as well as instructors (their roles and competencies). In order for answers to be able to settle in what the evidence in the investigation has been building, it is necessary to agree on a common set of topics and research methodologies.
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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 | 0.049 | 0.182 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".