Massive Open Online Courses (MOOC) Evaluation Methods: Protocol for a Systematic Review
Bibliographic record
Abstract
BACKGROUND: Massive open online courses (MOOCs) have increased in popularity in recent years. They target a wide variety of learners and use novel teaching approaches, yet often exhibit low completion rates (10%). It is important to evaluate MOOCs to determine their impact and effectiveness, but little is known at this point about the methodologies that should be used for evaluation. OBJECTIVE: The purpose of this paper is to provide a protocol for a systematic review on MOOC evaluation methods. METHODS: We will use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) guidelines for reporting this protocol. We developed a population, intervention, comparator, and outcome (PICO) framework to guide the search strategy, based on the overarching question, "What methods have been used to evaluate MOOCs?" The review will follow six stages: 1) literature search, 2) article selection, 3) data extraction, 4) quality appraisal, 5) data analysis, and 6) data synthesis. RESULTS: The systematic review is ongoing. We completed the data searches and data abstraction in October and November 2018. We are now analyzing the data and expect to complete the systematic review by March 2019. CONCLUSIONS: This systematic review will provide a useful summary of the methods used for evaluation of MOOCs and the strengths and limitations of each approach. It will also identify gaps in the literature and areas for future work. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/12087.
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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 | 0.152 | 0.184 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.081 | 0.017 |
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".