Community College Professors' Engagement and Perceptions of Professional Development in Remote Environments
Bibliographic record
Abstract
Research shows that consistent, supportive, and relevant professional development (PD) is an action that empowers educators to change. In the east central region of Canada, the sustainability of community colleges had been linked to program collaboration and internationalization which requires implementation of high-quality PD for its professors. Guided by Mezirow's adult transformative learning theory, the purpose of this study was to understand the PD practices of full-time professors by investigating the connections between professors' level of engagement in PD and their perceptions of the resources and processes that may improve their instructional practices. This single-phased, concurrent mixed-methods study was conducted using a self-designed 39-item web-based survey to collect both quantitative and qualitative data. From a population of 600 full-time professors at 5 colleges, 120 surveys were completed. Descriptive statistics, chi-square test, and Fisher's exact test were used for quantitative analyses. Findings indicated that professors with higher levels of engagement in PD perceived the importance of making use of online and face-to-face networking strategies as well as available online resources to improve instructional practices. Emergent themes from the qualitative data were coded and confirmed the value of networking in PD. Results were used to design a workshop to help community college professors in planning and supporting their PD activities via enhanced networking strategies and implementation of online resources for instruction. By providing college professors needed opportunities to engage in customized PD focused on networking and maximizing the use of available online resources, colleges may be better prepared to foster active, engaged, and highly trained faculty capable of producing positive social change benefitting the institutions and students they serve.
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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 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".