INVERTED CLASSROOM TO ENHANCE ENGAGEMENT AND CRITICAL THINKING
Bibliographic record
Abstract
The Flipped Classroom or Inverted Classroom Model (ICM) has been gaining increasing attention in recent years.This student-centred pedagogical approach has been practiced in various educational fields, but minimally in design pedagogy.The aim of ICM is to disrupt the passive approach of conventional learning.The model seeks to engage students actively in their learning experience and transform the classroom setting into a participative, creative and dynamic environment which, in return, can regenerate critical and innovative ways of thinking.The paper aims to explore the implementation of ICM within a graduate design course.More precisely, it seeks to understand the implications and influence of ICM on students' learning experience, engagement, and critical thinking.By analysing students' answer to a short online questionnaire, we discuss the challenges and benefits related to the organisational dynamics of an ICM course, some consequences on learning outcomes and teamwork, as well as specifics related to the teaching approach.These will eventually help in finding ways to improve ICM as an innovative pedagogical strategy for future graduate design courses.In the end, the study suggests that an ICM-inspired seminar can not only help foster critical thinking, and class engagement, but also help students to develop collaborative skills.The learning experience shared in this paper is an attempt to establish a framework for future design educational practices, coupling the teaching of theoretical notions with active learning experience -most typical to designers.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".