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Record W2892933618 · doi:10.15402/esj.v4i1.308

Community Service-Learning in a Large Introductory Sociology Course: Reflections on the Instructional Experience

2018· article· en· W2892933618 on OpenAlexfundvenueno aff
Jana Grekul, Wendy Aujla, Greg Eklics, Terra Manca, Ashley Elaine York, Laura Aylsworth

Bibliographic record

VenueEngaged Scholar Journal Community-Engaged Research Teaching and Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methodologies in Social Sciences
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsClass (philosophy)Service-learningGraduate studentsMathematics educationPedagogyMedical educationSociologyPsychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

This paper reports on a pilot project that involved the incorporation of Community Service-Learning (CSL) into a large Introductory Sociology class by drawing on the critical reflections of the six graduate student instructors and the primary instructor who taught the course. Graduate student instructors individually facilitated weekly seminars for about 30 undergraduate students, half of which participated in CSL, completing 20 hours of volunteer work with a local non-profit community organization. We discuss the benefits of incorporating CSL into a large Introductory Sociology class and speculate on the value of our particular course format for the professional development of graduate student instructors. A main finding was the critical importance to graduate students of formal and informal training and collaboration prior to and during the delivery of the course. Graduate students found useful exposure to CSL as pedagogical theory and practice, and appreciated the hands-on teaching experience. Challenges with this course structure include the difficulty of seamlessly incorporating CSL student experiences into the class, dealing with the “CSL”/ “non CSL” student division, and the nature of some of the CSL placements. We conclude by discussing possible methods for dealing with these challenges.

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 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)0.909
metaresearch head score (Gemma)0.860
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.9090.860
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.7460.005
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.730
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 head0.415
GPT teacher head0.532
Teacher spread0.118 · 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; both teacher heads agree on what is shown here.

Study designQualitative
DomainMethods
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

Citations1
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueEngaged Scholar Journal Community-Engaged Research Teaching and LearningSame topicInnovative Teaching Methodologies in Social SciencesFrench-language works237,207