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Record W1752508553 · doi:10.22329/celt.v2i0.3213

19. Encouraging Undergraduate Class Participation: A Student Perspective

2009· article· en· W1752508553 on OpenAlexaffvenue
Nichole S. Wright, Marcia N. Gragg, Kenneth M. Cramer

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2009
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsAttendanceClass (philosophy)PsychologyPerspective (graphical)Baseline (sea)Mathematics educationMedical educationPedagogyMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

Undergraduate classes typically involve a professor lecturing to 100 or more students. Too often, this results in minimal opportunities for student participation. Positive reinforcement was used to promote student participation (i.e., defined as relevant comments or questions) in a second-year psychology class (N = 97). Class participation was measured for five weeks in two 80-minute lectures per week. Baseline was collected in two lectures. In the unaware phase for two lectures, paper tickets were given without explanation to students who participated. Students were then informed that tickets were given for class participation, and would be entered into a draw for gift certificates. Data were collected for four lectures in this informed condition. Final baseline consisted of two lectures with no tickets distributed. Student attendance was recorded. Frequency of instructor questions remained relatively consistent. Class participation rose from 38 relevant comments and questions per week during initial baseline, to 47 during the unaware phase, 52.5 during the informed phase, and 60 for final baseline. Positive reinforcement was associated with increased class participation overall, but with little change for students with high initial participation. Students said they enjoyed and benefited from the class participation activity.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.751
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.436
Teacher spread0.402 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations4
Published2009
Admission routes2
Has abstractyes

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