The Abacus: Instruction by Teachers of Students with Visual Impairments
Bibliographic record
Abstract
Introduction This article, based on a study of 196 teachers of students with visual impairments, reports on the experiences with and opinions related to their decisions about instructing their students who are blind or have low vision in the abacus. Methods The participants completed an online survey on how they decide which students should be taught abacus computation skills and which skills they teach. Data were also gathered on those who reported that they did not teach computation with the abacus. Results The participants resided in the United States and Canada and had various numbers of years of teaching experience. More than two-thirds of those who reported that they taught abacus computation skills indicated that they began instruction when their students were between preschool and the second grade. When students were provided with instruction in abacus computation, the most frequently taught skills were the operations of addition and subtraction. More than two-thirds of the participants reported that students were allowed to use an abacus on high-stakes tests in their state or province. Discussion Teachers of students with visual impairments are teaching students to compute using the Cranmer abacus. A small number of participants reported they did not teach computation with an abacus to their students because of their own lack of knowledge. Implications for practitioners The abacus has a role in the toolbox of today's students with visual impairments. Among other implications for educational practice, further studies are needed to examine more closely how teachers of students with visual impairments are instructing their students in computation with an abacus. Topics to examine include the frequency of instruction, the age at which instruction begins, how instruction is provided to children with multiple disabilities, whether instruction is provided in the general education classroom or via pullouts, the role of math teachers and paraeducators in instruction, and how the abacus could be used collaboratively with technology.
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".