Relationship between Student’s Self-Directed-Learning Readiness and Academic Self-Efficacy and Achievement Motivation in Students
Bibliographic record
Abstract
Self-directed learning readiness to expand and enhance learning, This is an important goal of higher education, Besides his academic self-efficacy can be improved efficiency and Achievement Motivation, so understanding how to use these strategies by students is very important. Because the purpose this study is determination of relationship between students self-directed learning and academic self-efficacy and Achievement Motivation in Payamnoor students (2012-2013). In a correlation-descriptive study 322 bachelor students were selected from Payamnoor University of Rafsanjan (2014-2015) through a Simple random sampling. Data collection was SDL questionnaire, academic self-efficacy questionnaire and Achievement Motivation questionnaire. Data were analyzed by multiple regression, simple regression, variance analysis and T-test. The obtained findings from this research showed that there is a relation between student’s Self-directed learning readiness and academic self-efficacy and academic motivation in Students University of Payamnoor. Also Independence in learning and Study skills and problem solving has the most ability for academic self-efficacy and academic motivation prediction and there was the most correlation.According to results and that self-directed learning readiness to enhance self-efficacy and academic motivation, it is necessary to teach strategies to students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".