Bibliographic record
Abstract
Personalized Learning (PL) is an educational approach that tailors instruction to the academic needs of each student. Most research on PL focuses on student achievement, technology, and implementation challenges. Little research has been conducted on the actual practices that teachers use to personalize instruction and on students’ and teachers’ feelings about being in a school that implements PL. I conducted a case study at a recently opened rural elementary charter school in the southern United States, which was implementing PL schoolwide. After attending a professional development workshop on PL hosted by the State Department of Education, I conducted classroom observations in a first-grade and a fifth/sixth-grade classroom. I interviewed the teachers of these classes, the school principal, and three students. Three themes emerged from my analysis of this material, relating to student engagement, teacher behaviors and dispositions, and student outcomes. Overall, I concluded that PL is not a quick or easy transition for a school to make, nor does it involve just changing the curriculum to individualize instruction for students. Personalized Learning requires an adaption of teacher and student mindsets and the development of a school culture that fosters both academic and social-emotional growth among the students.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".