Faculty Perceptions on Contextual Factors as Affordances or Constraints to Innovative Teaching and Learning in Polytechnic Institutions
Bibliographic record
Abstract
The current global context and the ongoing disruptive nature of technological advancements continue to shape the narrative surrounding the relevance of traditional models of higher education. Although innovation in teaching has been identified as a priority for most institutions of higher education, there is limited research on the perception of innovative teaching directly from the innovators themselves – faculty members. Furthermore, there is a lack of empirical research on the institutional conditions conducive to innovative teaching practices in a polytechnic model of education in Ontario, Canada. Using an adaptation of Cognitive Work Analysis and the Human-tech Framework (Vicente, 2006), the purpose of this study was to investigate the nature of innovation from the perspective of college faculty members and to explore the contextual factors that enable or constrain the development and use of innovative teaching approaches. This inquiry utilized extreme case sampling to investigate the perceptions of five faculty members in Ontario Colleges offering a polytechnic model of education. Using Seidman’s (2013) three-series interview process, and an analysis of teaching artifacts and publicly available institutional documents, a detailed picture of the participants’ teaching experience and professional identities in the context of their institutions has been painted. Key affordances and constraints related to their innovative teaching practice are identified. The major findings emerging from the study are, first, a social justice identity was found to be a key factor in shaping the participants’ innovative teaching practice, as was their induction into the profession. Second, the participants identified that organizational culture factors, including negative relationships with peers and leadership, misaligned institutional priorities, and staffing models, were impacting their ability to innovate. Last, the participants highlighted the design of the higher-education system as a barrier to the progression of innovative teaching. That includes constraints associated with the evolution of Ontario colleges to polytechnic institutions. The results of this study contributes knowledge and provide insights into the contextual factors that impact the development and use of innovative teaching practices in polytechnic institutions. This information will be of interest to educational developers, policy makers and administrators interested in advancing innovative teaching practices at their institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".