Between Knowing and Learning: New Instructors' Experiences in Active Learning Classrooms
Bibliographic record
Abstract
Over the past 20 years, interest in the impact of space on teaching and learning has grown, and higher education institutions have responded by creating Active Learning Classrooms (ALCs)—spaces designed to promote active, student-centred learning. While ALC research has explored teaching methods, student experience, and student learning, less is known about how teaching in these spaces affects instructors. We contribute to this discussion by investigating teachers’ educational development in these spaces. We asked new instructors to reflect on their ALC experiences, exploring their pre-course preparation and their perceptions about themselves, their students, and teaching and learning. Their reflections revealed key differences between knowing and learning: Although all participants knew about and were dedicated to student-centred pedagogy before teaching in the ALCs, teaching in these spaces prompted transformative learning through which they shifted both their behaviours and perceptions about student learning and about their own roles in the classroom. Au cours des 20 dernières années, l’intérêt consacré à l’impact de l’espace sur l’enseignement et l’apprentissage a augmenté et les établissements d’enseignement supérieur ont répondu en créant des classes d’apprentissage actif (CAA) – des espaces consacrés à la promotion de l’apprentissage actif centré sur l’étudiant. Alors que la recherche portant sur les CAA a exploré les méthodes d’enseignement, l’expérience des étudiants et l’apprentissage des étudiants, on s’est moins intéressé à la question de savoir comment le fait d’enseigner dans ces espaces affectait les instructeurs. Nous contribuons à cette discussion en examinant le développement éducationnel des enseignants dans ces espaces. Nous avons demandé à de nouveaux instructeurs de réfléchir à leurs expériences en CAA, d’explorer leurs préparations avant les cours et leurs perceptions sur eux-mêmes, sur leurs étudiants et sur l’enseignement et l’apprentissage. Leurs réflexions ont révélé des différences majeures entre savoir et apprendre : bien que tous les participants aient été au courant, avant d’enseigner dans une classe d’apprentissage actif, de la pédagogie centrée sur l’apprenant et y aient été dévoués, l’enseignement dans ces espaces a engendré un apprentissage transformateur qui a abouti à un changement à la fois dans leurs comportements et dans leurs perceptions sur l’apprentissage des étudiants ainsi que sur leurs propres rôles dans la salle de classe.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".