Bibliographic record
Abstract
This dissertation is a currere study of how five students and their teacher understand their mathematical learning inside a Grade 10 classroom in Quebec. More closely, this research examines how recollections of past, present, and future mathematizing are tied to one’s sense of identity. Through analysing the entries in a teacher journal and the autobiographical stories of former students, identifications with and against common tropes of what it means to be “good” at mathematics were examined. This dissertation thus asks, how do participants in mathematics teaching and learning read their experiences, and why does a study like this matter to the future of the subject or to education overall? Using the autobiographical Curriculum Studies method of currere, a psychoanalytic stylistic analysis, and a cultural studies component whereby participants were encouraged to respond to the characters in the popular sitcom The Big Bang Theory, responses were gathered through individual interviews. Insights were derived from psychoanalytic readings of both transference and countertransference taking place in the learning space and beyond. The researcher’s and participants’ responses were understood through the ways in which the teacher’s emotional world is transferred onto the act of teaching and how, reciprocally, the teacher is addressed through feelings, phantasies, defences, and anxieties. The former students were interviewed with the stages of currere in mind in order to elicit free associative responses that lent insight to the regressive, progressive, and analytic stages. The final, synthetical, stage of currere took place to unpack my identificatory work as a researcher and teacher in the mathematics classroom. The methodological considerations in this dissertation included outlining the significance of repetitions of language in interviewees’ responses, both individually and collectively. Participants’ responses began to indicate a complex emotional world whereby their categorization in a “lower” mathematics course in high school nevertheless did not trap their identities into common tropes of of negativity, difficulty, and anxiety. Rather, the types of language and frequency of word use signal how the emotional landscape of students’ mathematical lives is shaped by how students perceive teachers to see them as mathematical or not. This research reveals how mathematics concepts, but more often, pedagogical dynamics, lead to complicated psychological terrain traversed by both teachers and students. I argue that using currere as a methodology readily employable with high school students helps to uncover the complex worlds of mathematical identity formation including the role of societal stereotypes. Furthermore, if educators understand their own dynamics of love and hate in relation to mathematical competence, performance, and pedagogy, they might better foster mutuality between students and teachers overall.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".