The Effects of Concept Mapping and Academic Self-Efficacy on Mastery Goals and Reading Comprehension Achievement
Bibliographic record
Abstract
<p class="apa">The purpose of the study is to investigate the effects of concept mapping on mastery goal orientation and academic self-efficacy in a collaborative learning environment. The current study employed a randomized controlled pretest-posttest group design to examine if learning strategies such as concept mapping can help students with both reading comprehension achievement and intrinsic motivation of wanting to master a task at a high level. A total of 42 5th grade students at Ilshin Elementary School in South Korea participated in this study. The experiment group (<em>n</em>=22) has undergone concept mapping training while the control group (<em>n</em>=20) has not. All students were required to fill out questionnaires based on mastery goals, performance goals and academic self-efficacy. The results indicated that concept-mapping did not increase mastery goals and mastery goals had no effect on test scores. In addition, the interaction effect between academic self-efficacy and condition did not increase mastery goals and had no effect on test scores. In conclusion, the reduced number of samples may have caused a potential source of instability considering the statistical procedure chosen.</p>
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".