Enhancing Self-directed Learning Readiness at Elementary Level; a Study from American Schools
Bibliographic record
Abstract
Being continual and future-oriented in all its levels, the field of education is turning towards a student-centered approach. One of the trends to investigate in lifelong learners who keep pace with the fast and unpredictable changing world is enhancing self-directed learning which is considered to be one of the most prominent phenomena for the 21st century learners. This study investigates possible ways of enhancing self-directed learning readiness in American K-12 schools. The literature highlights a three-dimension model to study the students’ self-directed learning readiness: learning resources and physical arrangements of the school dimension, diagnostic and evaluation dimension, and emotional and social environment dimension. This study aims to answer the research question of how to enhance self-directed learning readiness in American K-12 schools. For this purpose, semi-structured observations were conducted to observe a teacher with one group of third-grade students. An interview was conducted with the teacher as well in order to provide more perspective to the data. The study findings highlight the importance of all three dimensions in enhancing the students’ self-directed learning readiness. It is concluded by stating the crucial role of society represented in guardians, families, and private sector in boosting students’ self-directed learning readiness. In addition, it is found that to achieve an active and successful community partnership, the awareness of the role of society is to be raised. Consequently, the emotional and social dimension could impact on the other two dimensions positively. Finally, this study recommends the consideration of self-directed learning and its considerable implications on the Saudi context that could help in enhancing students’ self-directed learning readiness which goes in line with the Saudi vision 2030.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".