Features of High-Quality Online Courses in Higher Education: A Scoping Review
Bibliographic record
Abstract
What are the features of high-quality online courses in higher education? In this scoping review, we explore peer-reviewed scholarship related to the features of online learning in postsecondary contexts. We searched ERIC (EBSCO), Education Research Complete, and SocINDEX with Fulltext to retrieve peer-reviewed literature from 2010-2022 pertaining to features of online learning in higher education. Two reviewers independently conducted the initial title and abstract screening (n = 1,574), full text review (n = 483), and data extraction of the included articles (n = 38). Using thematic content analysis to explore the data extracted from each article, we found that the literature predominately included scholarship related to quality online course design, instructor facilitation in online courses, quality assessment of online courses, and student engagement in online courses. The breadth of these themes included a multiplicity of strategies and approaches to consider when designing online learning experiences. We recommend that administrators, faculty members, and instructors responsible for designing online courses and programs for postsecondary contexts continue to incorporate these considerations to promote high-quality and consistent online offerings. We conclude the review by presenting four high-level considerations to guide these discussions.
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 | 0.023 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".