Bibliographic record
Abstract
Does nonverbal communication exist in asynchronous, text-based online education? It is commonly believed that it does not due to the absence of body language and paralanguage. An examination of the definitions and forms of nonverbal cues suggests the possibility for some of them to be transmitted through asynchronous, text-based online human interactions. To explore the presence, type, and potential impact of electronic nonverbal cues (eNVC), I conducted this research using the Exploratory Sequential Mixed-Method Design. Phase 1 constituted the preliminary, qualitative stage of this research, during which participants completed an online questionnaire to identify what actions, if any, could speak louder than words in discussion-based courses. Thematic analysis of the questionnaire answers revealed the potential existence and influence of several eNVC categories. Phase 2 constituted the quantitative stage, and served to validate Phase 1 findings through the data collection and analysis of two versions of an online survey: one for professors and another for learners. The collated research findings confirmed that eNVC exist and communicate messages beyond those sent and received through printed words in the asynchronous, online learning environment. According to study participants, these types of electronic cues contributed to the social and teaching presences, and therefore carried the potential to influence students’ levels of engagement and motivation.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".