Motivating the unmotivated: A self-study about engaging adolescent readers to read for joy before and during a pandemic
Bibliographic record
Abstract
This self-study examines my teaching practice related to adolescent reading motivation and engagement to read for joy and pleasure. Many of my adolescent readers did not like to read and found very sophisticated strategies to avoid reading or perform fake reading during class. In this study, I explore questions related to how I have been changing and studying my teaching practice to foster a love of reading among my teenaged students, and how my teaching practice can create a community of readers. Over a two-year period, I shifted my practice and engaged in practitioner inquiry using the lenses outlined by Buckelew and Ewing (2000). This self-study explores my inquiry over those two years, beginning in September 2019, just prior to the start of the COVID-19 pandemic, and concluding in June 2021, while the pandemic was still ongoing. The findings of this study inform and create new thinking about how agency, self-efficacy, and relevance play a role in reading for joy. Findings are presented using the topics choice, relevance, and volume of reading material for independent reading; defining, and re-defining what, or who is a reader; developing students’ agency and self-efficacy; student stamina; re-discovering a lost love of reading; and contradictions in personal teaching beliefs and teaching practice. My findings will contribute to research in an urban Western Canadian context related to these topics.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".