Identification of Enablers for Reducing Student Incivility in Classrooms-An Exploratory Investigation
Bibliographic record
Abstract
Objective: There is a growing concern about student incivility in classrooms among academicians and institutional leaders. This study humbly tried to identify various enablers for reducing student incivility in classrooms through an exploratory investigation.Methods: This study uses empirical qualitative methods of investigation in a higher educational institution based at Delhi, NCR, INDIA. The authors have conducted open-ended, unstructured interviews with 8 faculty members of various departments of the institution.Results: This study able to develop three major themes and twelve sub themes as enablers to reduce student incivility in the classroom.Implications: The thematic map shall help the faculties and policy makers to integrate various mechanisms to control student incivility in classrooms.Theoretical and Managerial Contributions: This research has several contributions which would add to the existing body of knowledge. Firstly, this study explores various enablers which can reduce student incivility behaviours in classrooms. It also develops various propositions along with the thematic map that can be empirically investigated further. Additionally, this study attempts to link course planning, course scheduling, course content, scope for participation, giving breaks in between, norms and rules policy, transparent evaluation, applications of the subject, mutual agreement, counselling, live/group based projects, human touch which can result in student-centric pedagogy development, deep engagement in learning process and social accountability which will be an important extension of existing literature on higher education. The research offers valuable insights to academicians, institutional leaders by providing various enablers which can be used to reduce student incivility behaviours in the classroom level and developing the students as good citizens of the country and nurturing the young brains with the true spirit of innovation and ideas. As a result, teachers would be able to develop and maintain healthy and cordial relationships with the students which can result in reducing uncivil behaviours, burn outs, counterproductive work behaviour, attrition rates at the classroom context.Key Words：student incivility, academicians, institutional leaders, qualitative
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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 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".