Integrative and Theoretical Reviews of Achievement Motivation for School Psychologists: Introduction to the Special Issue
Bibliographic record
Abstract
A theoretically grounded understanding of achievement motivation appears to be largely overlooked in both initial education programs and ongoing professional development of school psychologists. This is unfortunate because motivation constructs such as perceived control, value, and self-beliefs predict students’ academic performance in a way that complements the variance explained by intelligence tests. This empirical evidence is rooted in long-standing theorizing that motivation constructs hold an important role alongside cognitive constructs in understanding student learning. Moreover, because motivation constructs are viewed as malleable, they provide an untapped source of intervention for school psychologists in supporting students. For these reasons, this special issue presents five review articles that integrate discrete theories of achievement motivation to the practice of school psychology. Largely representing Canadian expertise, the articles describe the potential for the control-value theory of emotions, achievement goal theory, mindset theory, self-determination theory, and self-regulated learning in the work of school psychologists. The final commentary article explicates a whole-child framework to highlight the role motivation can hold alongside cognition and guide school psychologists to partner these constructs for the betterment of children.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".