Resilience as a Contributor to Novice Teacher Success, Commitment, and Retention
Bibliographic record
Abstract
Novice teachers often struggle in their first year. Some succumb to illness, depression, or burnout, and some even decide to abandon teaching as a career option. The classic stressors identified by new teachers have been remarkably consistent over the years, and their challenges have been well chronicled (Corcoran, 1981; Gordon & Maxey, 2000; Gratch, 1998; Huberman, 1989; McIntyre, 2003; Veenman, 1984). Less has been written, however, about the strengths (Aspinwall & Staudinger, 2003, p. 13) that novice teachers demonstrate when they confront and overcome the stress of first-year teaching. In this article, I will focus on novice teachers' resilience as one of the assets that many beginners bring to their first teaching position and on the relationship between resilience and two related human strengths, personal efficacy and emotional competence. I will briefly review the literature on resilience, personal efficacy, and emotional intelligence, and these three concepts will be compared and contrasted within the context of novice teacher success and retention. I will use the profile of a novice teacher in Toronto, Ontario, to illustrate how beginning teachers cope with problems and overcome difficulties, remain optimistic about their choice of profession, and commit to teaching over the long term. I will discuss possible implications for faculties of education, school boards, and schools. Finally, I will make several suggestions for future research. Novice Teacher Attrition Teaching is one of the few professions in which beginners have as much responsibility as their experienced colleagues. New teachers carry full teaching loads and handle just as many other duties (supervision, extra-curricular, paper work, parent interviews, and report cards for example) as their higher paid co-workers. They often have more difficult subject combinations and more challenging students to manage (Gordon & Maxey, 2000; Kosnik & Beck, 2005; McIntyre, 2003). In addition, many novice teachers fear that if they ask for assistance, they will appear incompetent or poorly prepared (Glickman, Gordon, & Ross-Gordon, 1998; Gold, 1996; Scherer, 1999). The shocking attrition rate among new teachers is a persistent and pervasive problem in many jurisdictions. In Ontario, Canada, where the current study was conducted, McIntyre (2003) predicted that, by the second year of teaching, about 18% of new Ontario teachers would be at risk of leaving the profession. Studies conducted in the United States, Australia, and Great Britain confirm similar or higher early teaching attrition rates (Darling-Hammond, 2003; Feiman-Nemser, Carver, Schwille & Yusko, 1999; Howard & Johnson, 2004; Ingersoll & Smith, 2003; Stoel & Thant, 2002). According to McIntyre (2003), new teachers at risk of leaving the profession express strong dissatisfaction with their teaching assignments, and frustration with the politics of their profession, the lack of adequate resources, and inadequate mentoring support. These findings echo those in other North American studies, which report that novice teachers' initial optimism can turn to pessimism as the year progresses and the reality of teaching sets in (Brock & Grady, 2001; Darling-Hammond, 1997; Gold & Roth, 1999; Hargreaves & Fullan, 1999; Moir, 1999). Novice teacher resilience, bolstered by personal efficacy and emotional competence, may be key to helping beginning teachers become more capable, more confident, and more committed to teaching over the long term. Resilience, Personal Efficacy, Emotional Intelligence, and Novice Teaching Resilience, personal efficacy, and emotional intelligence are terms that describe similar dimensions of human behavior. Resilience is a mode of interacting with events in the environment that is activated and nurtured in times of stress. Grotberg (1997) defines resilience as the capacity to face, overcome, and even be strengthened by experiences of adversity (p. …
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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".