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Record W4280545764 · doi:10.1177/0092055x221096657

Engaging Students Using an Arts-Based Pedagogy: Teaching and Learning Sociological Theory through Film, Art, and Music

2022· article· en· W4280545764 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTeaching Sociology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methodologies in Social Sciences
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsThe artsPedagogySociologyMathematics educationSociological theoryVisual arts educationPsychologyVisual artsSocial scienceArt

Abstract

fetched live from OpenAlex

In this study we explore how incorporating an arts-based pedagogical approach, specifically, the use of film, art, and music, into a second-year sociological theory course enhances students’ overall learning experiences. We report on data collected from a survey given to students enrolled in this course in 2020. Findings reveal that employing this arts-based pedagogy helps students to sustain an interest in the course material, understand the theoretical course material, engage in a higher level of thinking/analysis, feel more confident in their abilities to write about theories covered in the course, apply theory in the real world, contextualize historical content, and enhance their memory of theories and concepts. Findings are also compared with data collected from a similar survey conducted in 2009, revealing that the overall favorable responses to arts-based resources have remained consistent over time and that this pedagogy remains an enduring approach that contributes to positive student learning experiences.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.072
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0720.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0250.005
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0000.000

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 head0.263
GPT teacher head0.516
Teacher spread0.253 · 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