Bibliographic record
Abstract
COVID-19 has impacted nearly every aspect of life and made more visible the myriad domestic, social, political, economic, and educational inequalities that have persisted all over the world. It has also illuminated the importance of teaching and learning, designing effective learning experiences, engaging students in the learning process, and in particular, supporting students—no matter where, how, or when they learn. This became even more evident as P–12 schools and institutions of higher education closed and shifted to emergency remote teaching and learning due to the COVID-19 global health pandemic during the spring of 2020 (Hodges et al., 2020; Milman, 2020a, 2020b). The response to COVID-19 required educators to quickly reconceptualize and redesign their instruction via diverse, remote learning contexts. This was also true for experienced online educators who had to adapt to an emerging and complex set of realities—and still have to as the global health crisis persists.Although the future successes of online educators may require greater creativity and reliance on technology-mediated instruction for the foreseeable future, as well as reflection on lessons learned during emergency remote teaching and learning, the community of inquiry (CoI) framework (see Figure 1) provides a well-known foundation for designing effective and successful online education. Developed during 1997–2001, the CoI framework (Garrison et al., 2001) emerged through a research project conducted by a group of Canadian researchers (Garrison et al., n.d.). This validated framework (Stenbom, 2018) consists of three major interrelated elements necessary for quality, effective online education, which are social presence, cognitive presence, and teaching presence. Each of these will be defined in the sections that follow.This special issue consists of several Ends and Means articles that I have written or coauthored and that I have organized using the three major elements of the CoI framework. The last section has articles written by other authors who incorporated CoI.Although the articles in the first three sections are organized according to the three different elements of the CoI framework, a key aspect of the framework is how these elements are interdependent (see Figure 1). Therefore, some articles could also be categorized in another CoI presence. Additionally, there are many resources that provide even more ideas about incorporating CoI (e.g., Garrison & Arbaugh, 2007; Garrison et al., 2010). For example, Fiock (2020) outlines several activities online educators might use to incorporate the CoI and Castellanos-Reyes (2020) wrote an article summarizing the past 20 years of this important framework for online education. It is my hope that these articles will offer readers several strategies and ideas for supporting, designing, and sustaining quality online education as well as for using the CoI framework.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.590 | 0.462 |
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".