Twenty‐first century adaptive teaching and individualized learning operationalized as specific blends of student‐centered instructional events: A systematic review and meta‐analysis
Bibliographic record
Abstract
Adaptive teaching and individualization for K-12 students improve academic achievement 1.1 | The review in brief Teaching methods that individualize and adapt instructional conditions to K-12 learners' needs, abilities, and interests help improve learning achievement.The most important variables are the teacher's role in the classroom as a guide and mentor and the adaptability of learning activities and materials.What is the aim of this review?This Campbell systematic review assesses the overall impact on student achievement of processes and methods that are more student-centered versus less student-centered.It also considers the strength of student-centered practices in four teaching domains.Flexibility: Degree to which students can contribute to course design, selecting study materials, and stating learning objectives.Pacing of instruction: Students can decide how fast to progress through course content and whether this progression is linear or iterative.Teacher's role: Ranging from authority figure and sole source of information, to teacher as equal partner in the learning process.Adaptability: Degrees of manipulating learning environments, materials, and activities to make them more student-centered. | What is this review about?Teaching in K-12 classrooms involves many decisions about the appropriateness of methods and materials that both provide content and encourage learning.This review assesses the overall impact on student achievement of processes and methods that are more student-centered versus less ------------------------------
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 | 0.019 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".