Teaching Science for Social Justice: An Examination of Elementary Preservice Teachers' Beliefs
Bibliographic record
Abstract
This qualitative study examines the beliefs and belief changes of eleven elementary preservice teachers about teaching science for social justice. Using constructivist grounded theory, it forwards a new theory of belief change about teaching science for social justice. The theory posits that three teaching and learning conditions may facilitate belief change: preservice teachers need to recognize (1) the relationship between science and society; (2) the relationship between individuals and society; and (3) the importance of taking action on socioscientific issues. This research responds to calls by critical scholars of teacher education who contend that beliefs in relation to equity, diversity, and multiculturalism need to be explored. They have found that many preservice teachers hold beliefs that are antithetical to social justice tenets. Since beliefs are generally considered to be precursors to actions, identifying and promoting change in beliefs are important to teaching science for social justice. Such a move may lead to the advancement of curricular and pedagogical efforts to promote the academic participation and success in elementary science of Aboriginal and racialized minority students. The study was undertaken in a year-long science methods course taught by the researcher. It was centered on the preservice teachers – their beliefs, their belief changes, and the course pedagogies that they identified as crucial to their changes. However, the course was based on the researcher-instructor’s review of the scholarly literature on science education, teacher education, and social justice. It utilized a critical – cultural theoretical framework, and was aligned to the three dimensions of critical nature of science, critical knowledge and pedagogy, and sociopolitical action. Findings indicate that, at the beginning of the year, preservice teachers held two types of beliefs (liberal and critical) and, by the end of the course, they experienced three kinds of shifts in beliefs (minimal, substantial, and refined). The shifts in beliefs were attributed by preservice teachers to specific pedagogies. Yet their initial beliefs also served as filters to the pedagogies, consequently impacting their degrees of belief change. Therefore, this study reveals elements of unpredictability when engaging in teaching science for social justice.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".