An exploration of SDT need fulfillment in a synchronous online learning environment
Bibliographic record
Abstract
In this paper, I report results from a qualitative case study that explores SDT need fulfillment within a synchronous online learning environment. SDT addresses how the sociocultural conditions an individual experiences impact their motivation, development, and wellness. An individual who experiences sociocultural conditions that satisfy their needs for autonomy, competence, and relatedness will develop autonomous motivation. As course instruction moves online, it is important to understand if and how online learning spaces can impact learner motivation. My data analysis and resultant code network reveal the fulfillment of the SDT need fulfillment pathway within a synchronous online learning environment.\nAdditionally, the code network reveals that the structure of a course, the quality of communication and feedback, the provision of choice, and the instructor???s personability and commitment are all linked to need fulfillment. This study???s implications will inform course design within online learning environments and future research in online learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".