A systematic review for identifying instructional design strategies and principles in extended massive open online courses (xMOOCs)
Bibliographic record
Abstract
Extended massive open online courses (xMOOCs), which follow traditional university learning models, have become a popular platform for structured learning over the past few years. Despite this popularity, xMOOCs are generally poorly designed, which has caused dramatic drop-out rates; therefore, they require extensive revision using instructional design strategies and principles. Hence, the purpose of this study is to conduct a systematic review of empirical studies that present best practices in the instructional design of xMOOCs and inductively identify effective instructional principles with the aim of improving the effectiveness of xMOOC instruction. We used the Population, Intervention, Comparison, Outcome, and Study Design (PICOS) framework as a guide to design our research question, we followed the PRISMA framework to conduct a robust systematic review through Covidence, and we finalized 16 related articles to be included in this study. There are four main findings: 1) this study explores effective xMOOC instruction-related research design methods and approaches and will provide future researchers deep insight into conducting empirical research on xMOOC instructional design; 2) this study provides insight into xMOOC participants and intervention characteristics for future researchers and instructors to understand the most common course design patterns and participant features; 3) this study identifies xMOOC typical instructional design strategies to promote the understanding of the best practices for designing high-quality xMOOCs; and 4) instructional design principles are inductively extracted from instructional best practices to provide researchers, policymakers, instructors and xMOOC platform providers with deep insights for promoting effective teaching and learning as well as for offering recommendations for future designers to avoid design drawbacks and promote design strengths.
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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 | 0.102 | 0.287 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.037 | 0.025 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".