Perspectives Of Beginning Teachers From Generation Z: A Narrative Study
Bibliographic record
Abstract
A wide range of ages in teachers exist in the field of education. New teachers from Generation Z is one specific category that has received little attention. Who are these new teachers? What experiences have they grown up with being from this generation? What might we learn about them that can help us offer better support, training, and guidance to ultimately create better learning environments for students? The purpose of this study was to gain a deeper understanding of the perspectives that new teachers from Generation Z have on teaching, learning, and teacher development. As the newest generation entering the teaching profession, it is important to ensure that schools, districts, and educational leaders learn to adapt in order to support the specific developmental needs of the newest teaching professionals. Working with new age teachers and learners requires innovative approaches that reflect their upbringing. This research recognizes specific qualities and distinctions that characterize Generation Z and ascertain how this might impact these beginning teachers. This study also involved the connection between the specific learning theories of Self-authorship and Self-determination and how these theories relate to the development of new teachers from Generation Z. A general recognition of the current practices in the Teacher Education Programs and Teacher Induction Programs in the province of Alberta serves as a reference point and provides context for this study. Being able to support teachers from Generation Z by understanding generational nuances should help to better personalize teacher development opportunities for Generation Z teachers and those who work with this specific cohort. School leaders, with evidence from this research, will be exposed to a deeper understanding of the necessary professional supports that Generation Z teachers suggest would help their age group. School districts will have current data to leverage the construction of teacher induction programs, as well as adopt more responsive professional development initiatives. Most importantly, K-12 students will benefit from having the most novice and vulnerable teachers supported by professional learning opportunities that appropriately align with their learning needs. The overall goal of this narrative qualitative research was to utilize the personal stories of Generation Z teachers in order to uncover both their general and specific learning needs during preservice training and during their first years of teaching.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".