An exploration of British Columbia's TVET instructors' perceptions that influence their curriculum choices
Bibliographic record
Abstract
Instructors' perceptions, values, and belief structures influence their curriculum decisions and may fundamentally overlap, contradict, and/or conflict, leading to a confluence of curricula cultures within the classroom.This study investigated Trades and Vocational Education and Training (TVET) instructors' perceptions to gain a better understanding of how those perceptions give rise to cultures of curriculum, particularly those that inhabit postsecondary TVET in British Columbia (BC).A total of 37 TVET instructors from BC participated in this study.Collectively, the participants represented a total of 10 Red Seal trades.Joseph's (2000) conceptualization of curriculum as culture was used as the theoretical lens to investigate vocational instructors' general perceptions regarding (a) their role as a teacher, (b) the intellectual capacities of their students, and (c) the purpose and future needs of vocational education.Q Methodology (Stephenson, 1935) was selected as the optimal research approach.Q factor analysis resulted in a four-factor solution, revealing the correlation of participants' shared curricular beliefs and values as four statistically distinct perspectives.Factor array tables and interview transcripts were reviewed to interpret and name the viewpoints as expressed by the participants grouping together in each factor: Factor 1 -the constructivist crew, Factor 2 -the canonical cluster, Factor 3 -the experiential team, and Factor 4 -the 21st century progressives.Two major findings were gleaned from this study.First, tensions exist between the theoretical underpinnings of competency based education and training (CBET) and the curricular beliefs held by Factors 1, 2 and 4. Factor 3, however, is found to be in broad agreement with the goals and pedagogies associated with CBET.Second, distinct views held by each factor are theoretically opposed to those of other groupings, creating incompatibilities and divisions within the education system.The findings from this study have implications for future research, practice, policy, and theory and lend support to other curriculum studies in both mainstream education and TVET.My intention is for these findings to bring forth awareness of the largely unexamined theoretical confusion that I found to exist within the BC TVET system and to provide a reference point for stakeholders' discussions and future curricular decisions.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".