Exploring Mathematics Teachers’ Noticing as Pedagogical Discourse Through an Adapted Lesson Study
Bibliographic record
Abstract
Although positive effects of lesson study on teachers learning are reported, only some studies have investigated teacher noticing as an analytical tool for supporting teachers with an explicit focus, as in LS, and more empirical evidence is needed. This qualitative interpretive case study design aims to investigate the noticing processes of a group of mathematics teachers conducting a lesson study cycle focused on teaching algebraic expressions using manipulatives in middle school. Data is collected through the audio recordings of the participants’ lesson study meetings. Participants were six elementary mathematics teachers who attended a graduate course selected based on voluntariness. This study aims to incorporate Lee and Choy’s (2019) teacher noticing framework with Sfard’s (2008) commognitive theory, which views learning as changes in discourse and noticing as a discourse structure covering observation, interpretation, and reasoning processes (van Es, 2011). Results showed that teachers focused more on aspects of students’ learning than issues of their instructional practice. However, their noticing was mostly related to future decisions and actions regarding issues of teaching methods and sequencing of the lesson, whereas teachers’ dominant noticing form related to students learning was interpretive. Results illustrate the applicability of these noticing frameworks as an analytic tool where noticing is conceptualized as a pedagogical discourse for the analysis of a lesson study review discussion by a group of mathematics teachers who focus on teaching algebraic expressions.
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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 | 0.004 | 0.015 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".