Academic Procrastination and Achievement Motivation: An Expectancy Value Approach to Measure Academic Procrastination in University Online Courses
Bibliographic record
Abstract
The purpose of this study was to investigate the relationship between students’ achievement motivation and their procrastination behaviour when learning online. Student achievement motivation was measured using Eccles and Wigfield’s (2020) Situated Expectancy Value Theory (SEVT), which conceptually understands a student’s achievement related choices and performance on a task, as influenced by the student’s valuation of that task, and their expectancy to succeed in completing that task. Within this theory, a student’s choices and performance on an academic task, are predicted by several dimensions of value such as interest, utility, cost, and attainment, as well as their expectancy to succeed at the task. A total of 171 students enrolled in online courses at a mid-sized Canadian university participated (29 male, 135 females, 5 other). Participants completed a 101-item questionnaire consisting of demographic questions, procrastination measures, and SEVT measures. Regression analysis indicated that values and expectancies were different across all three learning activities, but self-efficacy for time management was consistently the strongest predictor of procrastination. Further, trait procrastination as a predictor in the model, overpowered task values and decreased the strength of the self-efficacy measures. The results indicated that efficacy for time management and trait procrastination behaviour were the two strongest predictors of procrastination, indicating that time management strategies and personal tendency to procrastinate, best predict procrastination behaviour.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".