Exploring School Psychology Graduate Training in the Mathematics Domain: A Content Analysis of Course Syllabi
Bibliographic record
Abstract
Mathematics learning disabilities are common, complex, and often co-occur with other disabilities in learning and mental health. Although a critical role of school psychologists is to support students’ academic learning and achievement in this area, what remains unclear is the specific mathematics training required of and provided to school psychologists which enable them to provide such support. To address this issue, a qualitative content analysis of syllabi was undertaken to explore the math training content embedded within school psychology graduate courses. A total of 64 syllabi across 32 programs were analyzed, reflecting course content from roughly one-tenth of the National Association of School Psychologists approved program population in the United States, and half of the Canadian (English-speaking) school psychology program population. Results revealed that school psychology training within the mathematics domain relates to school psychologists’ roles and involvement in both assessing and intervening in the math difficulties experienced by school-aged children. More specifically, 107 math topics were identified, which grouped into four training categories and 13 subcategories. Math training was most commonly delivered through assignments; however, over half of syllabi (59.37%) addressed math training in a comprehensive manner (i.e., across assignments, readings, and lectures). Several training gaps were identified with more minimal attention given to tiered systems frameworks, cognition, and consultation. Additionally, it was found that instructors commonly allow students to choose the academic subject focus of their assignments (identified in 74% of syllabi). The high emphasis given to student choice assignments provides a cautionary note to the training field as it has the potential to promote variability in mathematics training exposure. As the first study to-date to explore the mathematics training embedded within school psychology coursework, this research helps to shed light on the current status of math training in the field by identifying the topics addressed, how training is addressed and delivered, and the relative quantity of training across courses. Results of this study are of utility to both training and practice as they point to the math knowledge and skills school psychologists are likely and less likely to possess upon career entry.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| 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".