The path from instruction to inquiry: a narrative inquiry examining early childhood educators’ stories of change
Bibliographic record
Abstract
This dissertation is an interpretive research study that examines the change experiences of four early childhood educators in Ontario, Canada during the implementation of the mandated pedagogical framework How Does Learning Happen? (Ontario, 2014a). Since the beginning of the new millennium, Ontario has introduced a number of significant policy initiatives that increasingly recognises the value of the early learning for young children for success in school and later in life. This study is rooted in my belief that the experience of early childhood educators during the implementation of these initiatives hold lessons that may be useful for future policy initiatives. The theoretical underpinning of this study is based on the ancient Greek works of Heraclitus (Haxton, 2003) and Parmenides (Král, 2011) which I used as explanatory lens to consider key aspects of change. The theoretical basis was further augmented by modifying the Ölander & Thøgersen (1995) theory of behaviour change to present a model to illustrate how change concurrently happens at the individual, organisation, and systems levels. Using the narrative inquiry model developed by Jean Clandinin and Michael Connelly (2000) as a methodological approach, I collected the experiences of these early childhood educators through a series of walking interviews and re-storied them into what Michael Agar (1990) refers to as ‘creative non-fiction’ format (p75). The stories were then analysed in three phases. First, the personal, professional and societal significance of the stories were considered collectively through a temporal lens. Following this, the change processes of the participants as revealed through the stories were compared and contrasted using Kurt Lewin’s (1947) change model. Finally, a thematic analysis was conducted. These multiple forms of analysis revealed that professional identity, access to mentoring support, and reflective practices were important considerations for the participants. These findings led me to conclude that early childhood educators could benefit from a workforce support strategy that would accompany future policy initiatives. I conclude that future research is required to fully develop the strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".