Teacher moderating and student engagement in synchronous computer conferences
Bibliographic record
Abstract
Online learning has received a great deal of attention lately, especially in higher education. The bulk of research has focused on asynchronous environments (such as web-based bulletin boards, e-mail systems etc.). Synchronous communication, by contrast, despite its popularity, has received less research attention. Of particular interest is the manner in which instructors manage the ebb and flow of classroom discussion and how this affects student engagement. This dissertation study attempts to develop a deeper understanding of the relationship between teacher moderating and student engagement. The primary data source for the study was 44 transcripts collected from 4 groups of college students over 11 weeks of conferences in a moderated synchronous online course taught in a Canadian university. The study used a mixed method design where the results of the quantitative analysis were used to select cases for qualitative analysis to better understand the substantive processes of engaged collaborative discourse. An important part of the analysis was the development of new constructs and measurement methods to measure teacher moderating behaviors and a range of student engagement variables (behavioral, social-emotional and intellectual). The quantitative analysis revealed that student intellectual engagement was a function of both students' participation, and the number and quality of teacher postings. For the qualitative part of the research, the researcher applied discourse analysis techniques to transcripts of interest in order to discover specifically what was happening with teacher moderating. This provided a unified picture of the complex nature of the interaction process in synchronous learning environments as well as an opportunity to identify and present key themes and practices for effective online moderating. In summary, the methodologies and findings of this study contribute to a better understanding of how teachers can provide effective online mentoring and scaffolding to facilitate student engagement with each other and with the subject matter. It also contributes to a better understanding of whether and how a community of inquiry develops by means of synchronous computer conferencing and how students can become invested behaviorally, social-emotionally, and intellectually. This research also informs both research and practice related to the larger goal of improving the quality of online teaching and learning.
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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 | 0.012 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".