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Record W2780620206

Are Active Learning Classrooms Authentic Learning Environments? An Examination of Students’ and an Instructor’s Lived Experiences in an Active Learning Classroom

2017· dissertation· en· W2780620206 on OpenAlexfundno aff
Victoria Chen

Bibliographic record

VenueQSpace (Queen's University Library) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsActive learning (machine learning)Mathematics educationAuthentic learningPedagogyExperiential learningPsychologyComputer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

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. Building 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? Data 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.007
Scholarly communication0.0070.005
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.286
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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