The Role of Motivation in Secondary Mathematics Instruction: Implications for RTI
Bibliographic record
Abstract
Response to Intervention--what do we know about it, why does it work, and how can teachers use it to ensure high-quality K-12 math instruction? Find out in this definitive research volume, edited by National Math Panel veteran Russell Gersten with contributions by all of the country's leading researchers on RTI and math. Current and future RTI coordinator's curriculum developers, math specialists, and department heads will get the best, most up-to-date research and guidance on key facets of RTI in math: conducting valid and reliable universal screening in mathematics; using evidence-based practices to provide a strong general education curriculum for effective Tier 1 instruction; implementing explicit, research-based teaching practices for students who need Tier 2 and 3 instruction; monitoring students' progress with high-quality tools and measures; motivating and engaging struggling students receiving Tier 2 and 3 instruction; teaching students to use an array of visual representations to help them solve math problems; tailoring RTI for every grade level, from kindergarten through high school; and using RTI to target specific mathematical proficiencies and concepts, such as number sense, word problems, algebra, and ratios and proportions. Filled with vignettes, accessible summaries of research studies, and best-practice guidelines for making the most of RTI, this comprehensive volume is ideal for use as a textbook or as a key resource to guide both practitioners and decision makers. Readers will have the knowledge base they need to strengthen mathematics instruction with proven RTI practices--and help ensure better math outcomes for students at every grade level.
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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 | 0.047 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".