Are Active Learning Classrooms Authentic Learning Environments? An Examination of Students’ and an Instructor’s Lived Experiences in an Active Learning Classroom
Bibliographic record
Abstract
New reimagined higher education classrooms called Active Learning Classrooms (ALCs) have been increasingly implemented across the world in the past decade (SCALE-UP Site, 2011), providing a more engaging and lively learning environment for students as compared to traditional lecture halls (Baepler, Brooks, & Walker, 2014; Chiu & Cheng, 2017; Morrone, Ouimet, Siering, & Arthur, 2014), but are these experiences meaningful? In studies by Chen, Leger, and Riel (2015a) and Ravenscroft and Chen (2017), instructors and students have hinted that these rooms allow students to make connections between their learning and planned careers, in other words Authentic Learning could be occurring. This could mean that ALCs may not be only engaging learning environments but also authentic learning environments. \nBuilding on the existing literature of ALCs and theories on Authentic Learning presented in Chapter 1 and 2, this dissertation uses a qualitative approach to capture, retell, and analyze the learning experiences of an instructor and 10 students in a 3rd year elective ethics course. Three research questions are addressed: (1) From the instructor’s perspective, what reasons lead to a decision to teach in an ALCs and how was a course planned and enacted in this classroom environment? (2) From the students’ perspectives, what are their learning experiences in a course taught in an ALCs? (3) Do the learning experiences in an ALCs represent Authentic Learning? If so, what influence does the learning environment have on Authentic Learning? \nData was collected through semi-structured interviews, observations, and student artifacts, and analyzed using analysis of narrative and a combination of deductive analyses (see Chapter 3). Chapter 4 presents the instructor and students’ lived experiences in a series of cases, followed by a comparison of the data to factors of the Theory of Authentic Learning (Hill, in-press) in Chapter 5, which revealed the learning experiences of students in the course were indeed authentic. Chapter 6 concludes the dissertation with implications of the findings and recommendations for educators on how they too can foster an authentic learning environment in the ALCs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".