Your Presence is a Present: Fostering Community in LMS, Blended, and Online Environments
Bibliographic record
Abstract
The increase in online course offerings and instruction in higher education, coupled with the continued rise of MOOCS (Massive Open Online Courses), means that it is now more imperative than ever for instructors to reflect on their digital teaching practices. This rise has caused faculty members to not only adapt their current teaching methods to online environments, but simultaneously address attrition: “one of the biggest challenges to distance education” (Dueber and Misanchuk, 2001). Although the responsibilities of an instructor can vary based on the nature and format of the course (i.e. face-to-face vs. blended learning), developing a sense of community is particularly integral for students learning in an online environment. Allowing participants to feel connected to their fellow students, teachers, and even the course content is central to student success. However, we as educators need to resist token expressions of online community-building (e.g. using learning management systems like Sakai ‘passively’ rather than ‘actively’) and commit to being present for our students. In this workshop, participants will learn new ways to foster a sense of community in their online platforms and courses, while investigating how the creation of an online teaching presence can further engage participants.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".