A Social Constructivist View of Neoliberalism as it Pertains to the Education Quality and Accountability Office Testing and Howard Gardner's Theory of Multiple Intelligences
Bibliographic record
Abstract
Ontario teachers are encouraged to recognize all eight multiple intelligences of their students through the utilization of authentic assessment practices rather than paper and pencil tasks which allow fewer students to excel. Despite the push for authentic learning experiences, teachers are responsible for preparing students for the annual Education Quality and Accountability Office (EQAO) testing, a paper and pencil test which assesses only linguistic and mathematical intelligences of students. The amplified importance of scores due to outside pressure from parents, taxpayers and politicians as a result of global competition and neoliberal politics, is negatively impacting teachers' abilities to recognize all of their students' intelligences. The majority of standardized testing literature criticizes the test rather than offering pragmatic ways to work with the current neoliberal system. This study adds to the extant literature by focusing on current realities for teachers as they work with the test in today's classrooms. Using a qualitative, semi-structured interview process, three elementary educators were interviewed to discuss the impacts that the EQAO testing had on their abilities to cater to students' multiple intelligences. Interview data was analyzed and coded using a qualitative case study method. Additionally, the use of a social constructivist lens, which valued the personal stories of three educators, revealed hidden impacts of EQAO scores on teacher instruction. Less focus on authentic assessment resulted in fewer opportunities for students of all intelligences to identify and hone their talents. This points to a possible conflict within Ontario's educational vision outlined in the ministry's Achieving Excellence policy.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.115 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".