An Analysis of Education Reforms and Assessment in the Core Subjects Using an Adapted Maslow’s Hierarchy: Pre and Post COVID-19
Bibliographic record
Abstract
Through the lens of an adapted Maslow’s hierarchy of needs, I have analyzed (1) the impact of the three main educational reforms of the 20th and 21st centuries on culturally and linguistically diverse (CLD)and low-socioeconomic (SES)students in the core subjects up to the COVID-19 pandemic; (2) the efficacy of current classroom assessment practices, and (3) a brief reimagining of how changing equity standards in teaching and assessment post-COVID-19 could aid in CLD and low-SES students achieving a higher self-esteem level. I contend that student success, or self-esteem, can only be achieved by first satisfying the needs at the lower hierarchy levels. By analyzing CLD and SES students’ school experiences, educators and policy-makers can extrapolate the requirements for inclusive, rigorous, and responsive assessments that recognize students’ needs and utilize their cultural and linguistic diversity. As states begin the shift from remote learning back to face-to-face in the fall, more significant considerations of CLD and low-SES students must be ensured.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".