In-between Places: A Narrative, Arts-informed Exploration of the Knowledge, Values, and Beliefs of English Language Instructors
Bibliographic record
Abstract
This research study, written as a narrative, arts-informed multiple case study, provides a model of reflective practice for TESOL professionals. Based on a foundation of holistic education, this study is an in-depth exploration of the teacher-self, centred on the hearts, minds, and behaviours of four English as an Additional Language (EAL) instructors of adults at mid-career. The central question of this study was, In what ways do English as an Additional Language (EAL) instructors of adult learners describe transformations in their knowledge, values, and beliefs about teaching and learning over the course of time? Engaged in an 8-month long Professional Learning Community (PLC) using Kolb’s Experiential Learning Cycle as the framework for reflection and growth, participants completed creative and reflective writing tasks and participated in group discussions in order to investigate their knowledge, values, and beliefs and how these shaped their perspectives, impacted their decision-making, and influenced their behaviours both inside and outside the classroom. Through actively investigating and constructing meaning from their lived experiences, participants imagined and created new ways of being and teaching. Participants were interviewed 4 years and 13 years after their participation in the PLC in order to identify the most significant changes in their knowledge, values, and beliefs. Findings showed that peer-based, continued professional development (CPD) involving writing and dialogic reflection are powerful methods and means for mid- to late- career EAL instructors to uncover hidden assumptions, develop alternate perspectives on experiences, build upon their diverse knowledge, and develop and share their expertise. Findings also showed an impact on the participants’ knowledge, values, and beliefs as they each progressed through various life stages and career transitions. While certain values remained the same over time, others changed as a result of the participants’ personal and professional growth. Implications of this study address research and practice at three levels. First, at the individual level, focusing on educators and students, second, at the level of professional development practices, and third, at the policy level related to professional learning and development within the community college context.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".