MétaCan
Menu
Back to cohort
Record W7015426594

A systematic review for identifying instructional design strategies and principles in extended massive open online courses (xMOOCs)

2021· article· en· W7015426594 on OpenAlexaff

Bibliographic record

VenueScholarship@Western (Western University) · 2021
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsWestern University
Fundersnot available
KeywordsInstructional designBest practiceSystematic reviewDesign elements and principlesResearch designEmpirical researchOnline learningInstructional simulation
DOInot available

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.102
metaresearch head score (Gemma)0.287
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.102
Threshold uncertainty score0.541

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1020.287
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0080.011
Bibliometrics0.0370.025
Science and technology studies0.0030.003
Scholarly communication0.0070.008
Open science0.0050.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.201
GPT teacher head0.388
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueScholarship@Western (Western University)Same topicOnline Learning and AnalyticsFrench-language works237,207