Approaches to learning, need for cognition, and strategic flexibility among university students
Bibliographic record
Abstract
BACKGROUND: Considerable research has described students' deep and surface approaches to learning. Other research has described individuals' self-regulated learning and need for cognition. There is a need for research examining the relationships among these constructs. AIMS: This study explored relationships among approaches to learning (deep, surface), need for cognition, and three types of control of learning (adaptive, inflexible, irresolute). Theory suggested similarities among the deep approach, need for cognition, and adaptive control (aspects of self-regulated learning); and among surface, inflexible, and irresolute control (aspects of an ineffective approach to learning). One-factor and two-factor models were proposed. SAMPLE: Participants were 226 Canadian military college students. METHOD: Participants completed the following questionnaires: the Study Process Questionnaire (Biggs, 1978), the Need for Cognition Scale (Cacioppo & Petty, 1982), and the Strategic Flexibility Questionnaire (Cantwell & Moore, 1996). RESULTS: Confirmatory factor analysis supported the identification of the six scale factors. Second order confirmatory factor analysis indicated three factors representing constructs underlying these factors. CONCLUSIONS: Neither the one- nor two-factor models accounted adequately for the data. Self-regulated learning was defined by measures of the deep approach to learning, need for cognition, and adaptive control of learning. The second factor divided into one factor consisting of irresolute control, the surface approach, and negative need for cognition; and another consisting of inflexible and negative adaptive control. Substantial relationships among scales support the need for further theory development.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".