Exploring Motivation in an Online Context: A Case Study
Bibliographic record
Abstract
With the increasing ubiquity of new technologies, many claims are being made about their potential to transform tertiary education. In order for this transformation to be realized, however, a range of issues needs to be addressed. Research evidence suggests that motivation is an important consideration for online learners. This paper reports on one aspect of a case study situated within a larger study that investigates the nature of motivation to learn of preservice teachers in an online environment. Using self-determination theory as an analytical framework, the focus here is on the underlying concepts of autonomy, competence, and relatedness. The ways in which certain social and contextual factors can foster perceptions of these needs being met are explored. These factors are known to have a supportive effect on learner motivation. Most prominent among these were the relevance of the learning activity, the provision of clear guidelines, and the ongoing support and feedback from the lecturer that was responsive to learners' needs. Supportive, caring relationships were also important.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".