Exploring facets of student motivation using a Bass Ackward strategy and the conceptual lens of self-determination theory
Bibliographic record
Abstract
Motivational constructs have proliferated in educational psychology, reflecting the complexity of what moves people to engage and learn. In this exploratory research, we focused on students’ motivation for higher education. Our goal was to understand how a wide range of motives are empirically and conceptually related. We also examined how this diversity of motivational content relates to the motivational typology postulated by Self-Determination Theory (SDT). In Study 1, we extracted items from a broad collection of measures, formatted them with a common set of instructions, and administered them to multiple samples of current and former U.S. college students. Using Goldberg's (2006) Bass Ackward factor-analytic method, we distilled twenty-six distinct facets that capture a wide variety of motivational contents. Multidimensional Scaling (MDS) suggested a dimension that resembled SDT's continuum of relative autonomy, with some facets similar to amotivation and others falling along a range from less to more autonomous or volitional forms of motivation. In Study 2, we administered these provisionally labelled motivational facets alongside SDT's regulatory styles and a set of external criteria covering multiple outcomes of interest in higher education. MDS analyses replicated the general pattern found in Study 1, recovering a dimension resembling SDT's continuum of autonomy. Motivational facets were also associated with external criteria in a theoretically coherent manner. We discuss the implications of these exploratory findings for understanding the structure of self-reported motivation and for theory and measurement of student motivation.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".