Bibliographic record
Abstract
Today, education is arguably one of the most important facets used to prepare and train students for the future. Society expects that students will acquire the requisite knowledge and competence in their respective fields to prepare them to successfully navigate the demands of today's competitive markets. This expectation has consequences on teachers at all levels of education across many domains. Teachers have a significant role: to prepare students for the future. Competent teachers spend a great deal of time reflecting on their own practices and beliefs, reviewing their teaching goals and evaluating if students have met these goals effectively. The process of reflection in teaching is vital in the preparation and training of students. The purpose of this dissertation therefore was to investigate how statistics professors reflect on their practice. The research questions were designed to access what statistics teachers thought about before giving their courses and before giving two of their classes (hypothesis testing, t-tests). Post class evaluation interviews were conducted to determine where professors thought they were effective and whether they considered a need for change based on student understanding. More specifically, the questions asked: 1) What are the main themes in teacher reflection? 2) How is the content of reflection similar or different between statistics teachers? 3) How is the content of teacher reflection defined in statistics? The design was based on a grounded theory approach whereby data collection consisted solely of interviews conducted throughout the semester: one pre-course interview and two sets of pre-class and post-class interviews. There were 13 participants in total. Participants were either statistics teachers from Quebec Cegeps or university professors. Participants were from the following departments: anthropology, economics, psychology, sociology, education, math, and biology. The analyses dealt with three data sources: pre class reflection, in class reflection, and post class reflection. Data analysis focused on defining the main themes of teacher reflection that emerged from the data, identifying the content of reflection between and within participants in terms of similarities or differences. The pre course interview revealed five main themes: the course (logistics), the teacher as 'self, teaching approaches (what do they say they do in the classroom?), teaching and learning influences, and evaluation of teaching. The pre and post class interviews addressed class planning. What did the professors foresee as any issues students might have in understanding hypothesis testing and t-tests? What changes would they make the next time they taught these concepts? Results showed that the focus of professor reflection centered around three main categories: the class, the student, and the teacher. For the main category, class, some professors reviewed lecture notes, added examples that emphasized authentic statistical problems, and others did no preparation. Student related themes addressed issues students had with understanding statistical content, learning associated difficulties, and student affect. The last category, the teacher, looked at self evaluation, their in-class strategies, methods of promoting and gauging student understanding, and decisions made in class and for future classes. Recommendations for future research include examining the role of experience in professor's level of reflection as well as defining the process of decision making and its role in reflection.
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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 | 0.034 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".