Enhancing learners’ sense of belonging in online threaded discussions
Bibliographic record
Abstract
Many different factors influence students’ sense of belonging in an online learning. One area of the learning experience in which a sense of “belonging” is critical is in online asynchronous discussions, and the degree to which students feel that their contributions to those discussions are valued. Unfortunately, the structure of threaded discussions is inherently limiting. It restricts learner engagement by only allowing students to reply to an individual note. This hampers students’ ability to discuss relationships between ideas from multiple participants, hindering synthesis and comprehensive understanding. This mixed methods explanatory sequential design research study examines the use of an innovative linking tool that allows students to create a rich set of linkages between notes. Findings suggest that students appreciated the linking tool as it facilitated idea integration and enhanced the flow and organization of discussions. Notes containing links were more extensive, written at a higher level, and received more peer recognition than notes without links. The linking tool created more connected and less repetitive discussions, while also increasing the amount of recognition students received for their notes. Despite the additional effort required, students continued to create links, motivated by the tool’s ability to organize ideas and the social recognition received.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".