Utilizing Teacher Experiences from the Past to Promote Reflection in Contemporary Teacher Education: A Grounded Theory Study on the Project Yesteryear Questionnaires of Robert S. Patterson
Bibliographic record
Abstract
By analyzing archival data of the responses of veteran teachers who worked in British Columbia in the 20th century, this project seeks to (a) determine the usefulness of this body of data in the instruction of 21st century teacher candidates; and (b) explore further insights into effective ideas to engage students with the history of education. This study builds on literature in the history of education and teacher education. Data came from the private archive of the late professor of educational foundations and former dean of the University of Alberta’s Faculty of Education, Dr. Robert S. Patterson. Patterson surveyed retired teachers in Western Canada on numerous aspects of their educational careers, compiling an extensive archive of responses. Utilizing coding techniques espoused from constructivist grounded theory, I established three focused codes from my reading of the questionnaires titled: Teacher Preparation, Concern with Education, and the Grand Historical Narrative. My findings reveal that the questionnaire became a method for retired teachers to share their knowledge and expertise, and I argue that this sharing is of value to contemporary teacher candidates. One sees an ability to reflect on the part of the respondents. In this pattern of reflection, discussions regarding teacher education, critical responses to educational reform, and inclusion of experiences for the preservation of their knowledge arose. This pattern suggests that retired teachers used the questionnaire space to share their expertise with readers, many whom they thought would be teacher candidates. If 21st century teacher candidates can appreciate this reflection and engage with this data, they can begin to understand how the history of education can induct them into the profession. The stories the respondents share are an opportunity for current education students to learn of past events and their current impacts on education and engage with effective reflection on their own assumptions and practices as future teachers. In this manner, contemporary teacher candidates can effectively engage with the history of education, develop habits of reflection, and learn of issues that are still prevalent in education today.
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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 | 0.031 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| 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 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".