Bibliographic record
Abstract
This two-phase study examined the educative features of upper elementary Eureka Math curriculum as well as examined take-up of those educative features in two classrooms at each grade level. Using an analytical framework based on Males (2011) and Quebec Fuentes and Ma (2018) in the first phase, I coded the educative features of a module at both the third- and fourth-grade levels for educative features for the category of content knowledge (Shulman, 1986) being addressed, the location of the feature in the lesson, and the type of guidance (Enactment or Rationale) being provided. An examination of the data in this phase revealed that most educative features addressed Pedagogical Content Knowledge for Mathematics Topics, was located in the Concept Development of the lesson, and provided guidance for enacting the feature. In the second phase, using two target lessons at each grade level, I identified key educative features to follow through the phases of curriculum use (Stein, Remillard, and Smith, 2007) in order to examine the take-up of those educative features by practicing teachers. An examination of the data in this phase revealed differences and similarities in take-up of educative features. In particular, differences in take-up of Enactment Guidance for Experiences seemed to heavily influence differences in take-up of Enactment Guidance for Facilitating Discourse and Enactment Guidance for Participation Structures. Additionally, similarities in take-up of Enactment Guidance for Representations and Enactment Guidance for Developing Mathematical Terminology were revealed across teachers at the same grade level. Implications for curriculum development and recommendations for further research are offered.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".