Bibliographic record
Abstract
This in-progress study presents aspects of using educational technology in teaching mathematics education. More exactly, it explores ways in which educational technology might be used in order to improve teachers' cultural awareness and social activism. A rationale for a qualitative research study is presented by using multiple methods combining action research and multiple case studies. Three high school mathematics teachers from Greater Toronto Area are selected to participate in this research. Actor Network Theory (ANT) was considered as research paradigm for this study. Canadian learners and teachers continue to struggle with issues related to social justice and activism. These issues are further complicated by attempts to enhance pedagogical effectiveness of various disciplines with new technologies. For example, where questions of social justice were previously considered totally separated from mathematics education, since former aims of mathematics teachers were restricted to abstract processes that ignored social processes and knowledge transfer, when advent of technologies applicable to classroom were introduced, a socio-political element to teaching of mathematics has been developed. This movement is most apparent in various attempts to incorporate educational technologies, and mathematics education is one of them. Educational technology has been an important mediator that significantly modifies learning environment and teaching approaches and, therefore, among constructivists and others there have been ongoing debates about whether mathematics teachers' use of technology in classrooms can actually help students and teachers to perform better. The recent Ontario Curriculum for Education in Mathematics (Ministry of Education, 2005, 2007) offers some orientations to study mathematics considering various social and cultural aspects, has some guidance for social justice, and gives directions for learning considering multidisciplinary approaches. However, these reforms cannot be implemented easily. As Fullan (1993) mentioned, when new educational programs are implemented, results take directions that are fairly different from initial intentions. Therefore, these recent Ontario reforms in mathematics curriculum are expected to encounter difficulties when they are implemented in a wide variety of schools. Context of Problem It is my premise that part of solution to this problem lies in re-examining ways of teaching mathematics by using educational technology in order to promote social activism, and also, renegotiating relations between teachers and students in mathematics classrooms. Freire (1973) mentioned that the role of educator is not to 'fill' educate with 'knowledge,' technical or otherwise. It is rather to attempt to move towards a new way of thinking in educator and educatee, through dialogical relationship between both (p.125). As such, goals of this research are:
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".