A Systematic Review of Questionnaire-Based Quantitative Research on MOOCs
Bibliographic record
Abstract
Massive open online courses (MOOCs) have attracted much interest from educational researchers and practitioners around the world. There has been an increase in empirical studies about MOOCs in recent years, most of which used questionnaire surveys and quantitative methods to collect and analyze data. This study explored the research topics and paradigms of questionnaire-based quantitative research on MOOCs by reviewing 126 articles available in the Science Citation Index (SCI) and Social Sciences Citation Index (SSCI) databases from January 2015 to August 2020. This comprehensive overview showed that: (a) the top three MOOC research topics were the factors influencing learners’ performance, dropout rates and continuance intention to use MOOCs, and assessing MOOCs; (b) for these three topics, many studies designed questionnaires by adding new factors or adjustments to extant theoretical models or survey instruments; and (c) most researchers used descriptive statistics to analyze data, followed by the structural equation model, and reliability and validity analysis. This study elaborated on the relationship of research topics and key factors in the research models by building factors-goals (F-G) graphs. Finally, we proposed some directions and recommendations for future research on MOOCs.
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 | 0.038 | 0.152 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".