A Collaborative & Systematic Approach To Implementing An Effective Standards-Based Grading And Reporting System: A Change Leadership Plan
Bibliographic record
Abstract
This mixed methods study examined the collaborative efforts between the Standards-Based Grading and Reporting Committee (SBGRC) at Mountain West High School (MWHS) and the Leadership District Team (LDT), which consisted of the following stakeholders: the district superintendent, the chief business officer, the chief financial officer, multiple principals and assistant principals, students, and parents of Mountain West School District (MWSD). These groups researched effective grading and reporting policies and procedures for possible implementation, and worked toward developing a specific, measurable, attainable, realistic, and timely (SMART) goal. In addition, a multiyear action plan was cocreated to streamline the process (DuFour, 2006). This change/action plan was codesigned to examine how to implement an effective standards-based grading and reporting system that is appropriate and reasonable for MWHS and MWSD. This study examined how, after reviewing local and national data and researching effective grading and reporting policies and procedures, key stakeholders collaboratively decided on the best way to measure and report academic achievement that would best prepare all students for success in colleges and careers.\nIn addition, this study, strived to shed light on a possible correlation between grade point average (GPA) and standardized test scores. Results showed that a quarter of the students with a “good” GPA (defined as 3.0 or above), who were in the top 25% of their class, performed at or below the level of the top 50% of students nationally on the Northwest Examination Assessment (NWEA)/Measure of Academic Proficiency (MAP), Partnership for Assessment of Readiness for College and Careers (PARCC), and the American College of Testing (ACT) exam. This begs the questions, “How could so many students be earning high marks in school, yet have such mediocre performance on standardized tests?”\nWhat is more, the study revealed that nonacademic factors such as behavior, participation, attendance, and the ability to meet deadlines are included in local and national grading practices. These factors that distort students’ authentic academic performance.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".