The synergistic effect of affective factors on student learning outcomes
Bibliographic record
Abstract
Abstract This study investigates how affective and self‐related factors impact participation in science learning and environmental awareness and responsibility. Using PISA 2006 datasets from Taiwan and Canada having similar level of science competency, the model for this study verifies and expands an earlier model by examining the relationships among science‐related interest, enjoyment, self‐efficacy, self‐concept, leisure time engagement, and future intended interest in science and how these relationships synergistically interact with environmental awareness and responsibility. The most consistent finding revealed that students' science self‐concept in both groups was weakly associated with future intended interest and engagement in science learning and with their sense of environmental awareness and responsibility. Reasons for this phenomenon and possible causes underlying why students' science self‐concept was weakly connected to their future intended interest in science learning are also presented. Finally, how the results of this study are important to science education instruction and research are forwarded in which students' identity and beliefs about self in science need to part of the next generation of science education reforms. © 2014 Wiley Periodicals, Inc. J Res Sci Teach 51: 1084–1101, 2014
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".