Towards Equity in Science, Technology, Engineering and Math (STEM), in Kindergarten to Grade 12 Education
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,005 | 0,011 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».