Enhancing the Teaching and Learning of Basic Arithmetic Through Subitizing
Bibliographic record
Abstract
This paper examines how subitizing (recognizing a quantity and naming it without having to count the objects individually) can help develop students’ understanding of basic arithmetic and how teachers’ and students’ actions can inform one another. Studies suggest that subitizing is underused in teaching and can be harnessed to enhance the learning of subtraction and addition because of its deep links to visualization. Pairing research that points to possibilities of using subitizing to teach arithmetic with an enactivist view of teaching, this research examines how teachers’ and students’ actions co-adapt. Data from short, seven- to eight-minute addition and subtraction lessons in a Grade 2 classroom were collected and analyzed with an enactivist view of teaching actions as triggers. Actions included verbal prompts, movement of small circular objects that represent numbers and hand gestures above the objects. In these lessons, teachers and students arranged small circular objects to guess/identify one another’s computational strategies to arithmetic questions. Our findings suggest that teacher actions triggered and were triggered by students’ subitizing capabilities and occasioned making connections between number (de)composition and operations. Teachers’ actions were contingent on students’ actions as they repeated, enhanced or changed their actions. Triggered by teachers’ actions, students were able to use subitizing to describe their computation strategies instead of counting to combine and/or form large values and to (de)compose, add or subtract values. This research recommends the use of subitizing to make arithmetic strategies visual and calls for more research on the co-emergence of teaching and learning in mathematics classrooms.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".