Creating Enabling Environment for Student Engagement: Faculty Practices of Critical Thinking
Bibliographic record
Abstract
Critical thinking (CT) is considered an important attribute in practice disciplines and faculty members in nursing, medicine, and education are expected to facilitate the development of CT in their graduates so that these individuals can be critical, reflective, competent, and caring professionals and service providers (Distler, 2007; Shiau & Chen, 2008; Worell & Profetto-McGrath, 2007). When students are actively engaged in their learning, and classrooms have an enabling environment, critical thinking is promoted. Teachers must reflect upon their teaching pedagogy when students do not participate in stimulating discussions, or asks questions in class. Research suggests that lack of understanding of CT affects teachers’ CT practices in the classroom. Literature supports that teaching learning activities and opportunities that emphasize encouragement of students’ participation in classroom fosters communication, student engagement, creativity, self – directedness and critical thinking (Choy & Cheah, 2009). Thus it is vital to explore what CT practices can be performed by educators to influence students’ CT. The present study aimed to identify perceptions and practices of CT among educators from the disciplines of nursing, medicine, and education in higher education in Karachi, Pakistan. A descriptive exploratory design was used where 12 multidisciplinary educators participated in semi structured interviews and allowed classroom observations. Four major themes were identified, but this paper will explore multidisciplinary educators’ practices of CT in a classroom setting. The faculty needs to be aware of how their practices of critical thinking can create an enabling learning environment, and what factors in its physical, psychological and intellectual environment can affect critical thinking in students. Keywords: Enabling environment, Student engagement, Faculty practices, & Critical thinking
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".