The Purpose of Teaching and Teaching Experience: A Preliminary Analysis of a Qualitative Research in Progress
Bibliographic record
Abstract
This research examines the negative feelings of teachers in Hong Kong toward teaching in order to provide recommendations to improve teachers' emotional experience in professional settings.Many teachers in Hong Kong have been reported as being stressed, anxious, and depressed while teaching (Chan, 2011).These emotional experiences significantly affect both the teachers' well-being and quality of teaching (Sutton, 2005).Drawing on sociological perspectives of emotions (Turner & Stets, 2005), teachers' emotional experience is regarded as socially constructed, meaning that teachers' emotional experiences are shaped by how the teachers think they can accomplish their career goals of teaching (Saunders, 2013).Research shows that if teachers perceive that they successfully accomplish their career goals they have positive feelings, conversely, they report negative feelings when they do not reach their career goals (Santoro, 2011).Moreover, teachers may have different career goals and different degrees of the accomplishment of the career goals in different career stages (Goodson, 2008).In other words, understanding why teachers in Hong Kong feel negative about teaching requires explorations of their values, their perceptions of how successfully they accomplish their career goals in different career stages.Since the research is still in progress, this paper only presents a preliminary data analysis in order to inform the future direction of the study.In particular, this article only presents the findings about the participants' career goals of teaching in different career stages.This study adopted a qualitative interview method since the method empowers the researcher to explore the meanings and social process.This was achieved through an in-depth investigation of participants' perspectives, feelings, and social interactions, with reference to social contexts (Seidman, 2006).By using maximum variation sampling and snowballing sampling strategies, the study interviewed 21 Hong Kong secondary schoolteachers who had different teaching experiences and taught different subjects and grades in 10 different Hong Kong secondary schools.The interviews were conducted between February and June 2012.According to the findings, the career goals of teaching among the participants could be categorized into intrinsic, including subject-matter, working with children, and making a difference, and extrinsic, including job security, salary, and safe haven.Most of the participants taught because of the intrinsic career goals rather than the extrinsic ones, although some of them aimed at both types of career goals (see Table 1).The analysis explored whether the career goals differed between the participants who had different amounts of teaching experience.Table 1 illustrates the teachers having 9-12 years of
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".