Identifying New Jersey Teachers’ Assessment Literacy as Precondition for Implementing Student Growth Objectives
Bibliographic record
Abstract
The Student Growth Objectives are assessments created locally or by commercial educational organizations. The students’ scores from the Student Growth Objectives are included in teacher summative evaluation as one of the measures of teacher’s effectiveness. The high amplitude of the requirements in teacher evaluation raised a concern of whether New Jersey public school teachers were competent in assessment theory to effectively utilize the state mandated tests. The purpose of this quantitative study was to identify New Jersey teachers’ competence in student educational assessments. The researcher measured teachers’ assessment literacy level between different groups based on subject taught, years of experience, school assignment and educational degree attained. The data collection occurred via e-mail. Seven hundred ninety eight teachers received an Assessment Literacy Inventory survey developed by Mertler and Campbell. Eighty-two teachers fully completed the survey (N=82). The inferential analysis included an independent-sample t test, One-Way Analyses of Variances test, a post hoc, Tukey test and Welch and Brown-Forsythe tests. The results of this study indicated teachers’ overall scores of 51% on entire instrument. The highest overall score of 61% was for Standard 1, Choosing Appropriate Assessment Methods. The lowest overall score of 39% was for Standard 2, Developing Appropriate Assessment Methods. The conclusion of this study was that New Jersey teachers demonstrated a low level of competence in student educational assessments. In general, the teacher assessment literacy did not improve during the last two decades.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".