Towards Equity in Science, Technology, Engineering and Math (STEM), in Kindergarten to Grade 12 Education
Bibliographic record
Abstract
In their quest to improve student outcomes and address the needs of all learners, school districts are now increasingly including a focus on Science, Technology, Engineering and Math (STEM) programming among their core commitments. Within school districts’ Multi-Year Strategic Plans (see definition on p. 18) are often included a number of equity-related commitments and core priorities, such as: providing equity of access to learning opportunities for all students; providing all students with equitable access to deep learning experiences enabled by technology; striving to close the opportunity gap so that students who have been historically and contemporarily underserved can achieve their full potential; and eliminating disproportionate outcomes for students. Yet my professional experience in the field and analyses of student learning data indicate persistent achievement gaps and underrepresentation for certain groups of students in STEM. In order to develop a framework for addressing disproportionate student outcomes in STEM, this study examined K-12 educator perceptions about the factors associated with maximizing learning conditions for students across classroom, teachers’ professional learning, school, and board contexts. In addition to collecting perceptual data from educators, this mixed methods research study conducted in-depth interviews with study participants to gain further insight on strategies that address the barriers encountered by students from equity deserving groups within STEM-focused classrooms. Grounded in a critical perspective, the conceptual framework used in this study leveraged educator perceptions about how features across four contexts impacted their professional capacity to influence student learning in STEM. This set the stage for developing a clearer understanding of the disproportionate student outcomes problem. The study identified barriers to equitable student outcomes and how they might be addressed to attain equitable outcomes for all students in STEM. Additionally, this study considered how learning conditions might be improved for all students and how historically stubborn disproportionate student outcomes might be mitigated by paying closer attention to a range of features across all four contexts, including teacher content and pedagogical knowledge in the implementation of STEM programming. The study also highlighted the most impactful strategies across all four contexts to address disproportionate student outcomes in STEM. The findings of this study and the resulting policy implications hold the potential to raise awareness among policy actors and lead to better-informed decision making processes, where the presence of barriers to equitable student outcomes in STEM are more readily identified; and historically and contemporarily persistent opportunity and achievement gaps are mitigated with greater intentionality.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".