American PARCC and SBAC and Their Implications on the Construction of English Assessment System in China
Bibliographic record
Abstract
The Partnership for Assessment of Readiness for College and Careers (PARCC) and Smarter Balanced Assessment Systems (SBAC) started in the 2014-2015 academic year and has been regarded by many in the field as a radical effort to improve the American English Language Art (ELA) educational standards. These two consortia, being aligned with Common Core State Standards, aim to fulfill Common Core’s purpose of preparing students for college and career readiness. With the support of computer technology, diverse forms of testing are introduced into the new assessment systems, making the standard-based test comprehensive enough to evaluate academic literacy and deep learning capacity in an authentic way.This paper mainly discusses similarities and differences between the two assessment systems in terms of ELA standards. The similarities appear in the construction of well-balanced assessment structure, the application of advanced computer technology, adherence to an evidence-based design principle and emphasis upon educational equity. The key differences are presented in aspects of test forms and accommodation options. The analysis of PARCC and SBAC assessment systems also provides China with various thought provoking aspects to develop a sound English Language assessment system.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".