Unpacking the Dynamics of Online Motivation: A Study of Short-Term Changes in Task Value and Cost Utilizing Multiple Change Analyses
Bibliographic record
Abstract
Motivation is critical to student success in learning environments. However, changes in situation-specific motivation over time are rarely explored among online learners. Drawing from the Situated Expectancy Theory (SEVT), in the current study, I examined changes in situational task-specific task value and cost over the short term and tested the impact of these changes on achievement. I collected task value and cost over four time points from 68 students in an upper-division online Educational Psychology course. Grades on two discussion posts and one exam represented academic performance. I used multiple analytic techniques, scarcely used in the literature, to measure change across time, including task value-cost switching and group and individual-level task value-cost intensity. Group-level intensity analyses revealed task value and cost stability across most time point comparisons. However, individual-level analysis demonstrated variability in task value and cost across comparisons. There was no evidence of task value or cost switching. One significant increase in cost across tasks predicted academic performance in the subsequent assessment. The study highlights the importance of using various techniques to explore motivational changes among online learners and demonstrates the applicability of SEVT in online learning environments.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".