On the Effect of Goal Setting on Self-Directed Learning, Achievement Motivation, and Academic Achievement among Students
Bibliographic record
Abstract
This study aimed to investigate the effect of goal setting on self-directed learning, achievement motivation, and academic achievement in students. All secondary school students at eighth grade in Semnan city in 2015 constituted the population of the study. From among this population, the number of 40 students with the lowest scores in self-directed learning and achievement motivation was randomly selected as the sample. Then, these students were equally placed in two experimental and control groups. In the next stage, the pretest was administered to both groups and the experimental group received nine training sessions of self-directed learning. It is noteworthy that the control group received no intervention. In this study, pretest-posttest along with control group design was used. Fisher, King & Tague's Self-Directed Learning Readiness Scale and Herman's Questionnaire Measure of Achievement Motivation along with students' grade point average scores (first semester as the pretest and second semester as the posttest) were used for data collection purposes. Next, data analysis was performed using multivariate MANCOVA and univariate ANCOVA. The results showed that teaching of goal setting had a significant effect on the improvement of self-directed learning and achievement motivation; however, it had no significant effect on students' academic achievement. According to the obtained results, it is recommended that goal setting be taught to promote self-directed learning and achievement 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".