Student motivation to participate in asynchronous online discussions
Bibliographic record
Abstract
Participation in online learning environments, especially in asynchronous discussions, is a crucial component for student engagement in online learning. Learner motivation is associated with student success in the online learning environment. Intrinsic motivation, doing something because it is enjoyable or interesting, is associated with participation in discussion topic choices. External demands, both work and personal, can also affect a student’s intrinsic motivation through altering their control. The purpose of this study was to examine the relationships between motivating factors and student participation in online asynchronous discussions. Post-licensure undergraduate (RN-BSN/RN-MSN) and graduate (MSN) students (N = 350) were distributed an online anonymous survey consisting of ten questions. A response rate of 20% (N = 69) was achieved, with 49% MSN (graduate) students (n = 33) and 51% RN-BSN/RN-MSN (undergraduate) students (n = 36), participating in the survey. Seventy-nine percent of students were employed full-time. Graduate students (65%) and undergraduate students (49%) felt that their motivation to participate in discussions was related to their employment status. Seventy-nine percent of MSN students and 63% of undergraduate students reported that the current demands in their life affected their motivation to participate in online discussions. The majority of students felt that instructor participation in the discussion had no effect on their motivation to participate. When classified into undergraduate and graduate groups, graduate nursing students preferred a choice of discussion topics in which to participate (χ2 = 10.851, p = .004). Providing students with discussion topic choices is associated with intrinsic motivation and increased online discussion participation.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".