The relationship of work avoidance and learning goals to perceived competence, externality and meaning
Bibliographic record
Abstract
BACKGROUND: Motivational researchers have suggested that work avoidance may be an academic goal in which students seek to minimise the amount of work they do in school. Additionally, research has also suggested that emotions may be catalysts for goals. AIM: This study examined the relationship between emotions and learning or work avoidance goals. Do emotions explain goals? SAMPLE: The participants were 512 senior high school students in Eastern Canada. METHOD: Students completed a survey assessing motivation related constructs. A structural equation model was postulated in which students' affect predicted learning goals and work avoidant goals. A cluster analysis of affect scores was performed followed by between-group and within-group contrasts of goal scores. RESULTS: The structural equation model suggested that a sense of competence and control were predictive of a learning goal while lack of meaning was related to work avoidance. The cluster analysis showed that confidence and control were associated with a learning goal but that a sense of inadequacy, lack of control or lack of meaning could give rise to work avoidance. CONCLUSIONS: Emotions seem to be directly linked to goals. Teachers who foster feelings of self-assuredness will be helping students develop learning goals. Students who feel less competent, bored or have little control will adopt work avoidant goals.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".