Fostering University Students’ Motivation through Dialogic Classroom Practices: A Conceptual Model
Bibliographic record
Abstract
This conceptual paper presents a model for engaging students and promoting motivation through dialogic classroom practices. The author argues that fostering dialogic teaching and learning in the classroom can positively impact student motivation and learning outcomes. The proposed model highlights the importance of creating a classroom environment where students feel comfortable sharing their ideas and engaging in meaningful discussions. This conceptual article is lensed within transformative paradigm, presenting dialogic pedagogy as a concept analysed using conceptual analysis. Dialogic pedagogy was presented, and the key assumptions, such as promoting classroom dialogue, valuing all voices, emphasising social knowledge construction, and instructors acting as facilitators of learning, were discussed vis-à-vis its correctional tendencies with student motivation and learning outcomes. The paper also highlights the potential benefits of dialogic classroom practices for diverse and borderless student populations. The conclusion is that promoting classroom dialogue, valuing all voices, emphasising social knowledge construction, and acting as facilitators of learning are dimensions to enhance student motivation towards learning outcomes. The paper offers a valuable contribution to the literature by providing a comprehensive framework for educators to effectively engage and motivate their students through dialogic practices.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".