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Record W1980339299 · doi:10.2304/plat.2011.10.1.32

Incorporating Active Learning Techniques in an Introduction to Psychology Course

2011· article· en· W1980339299 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

VenuePsychology Learning & Teaching · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsNew York Institute of Technology
Fundersnot available
KeywordsSection (typography)Active learning (machine learning)Mathematics educationCourse (navigation)PsychologyComputer scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The impact of active learning techniques on student learning outcomes was assessed across two studies. Performance data from two parallel sections of Introduction to Psychology (one traditional and one redesigned in which active learning techniques as well as online activities were incorporated into each lecture) were compared. Data from the traditional and redesigned sections on three identical semester tests, final exam grades and overall course grades were compared. Study 1 results indicate that students in the redesigned section performed as well as students in the traditional section on the semester tests and the final exam. The final grade distribution did not differ significantly between the traditional and redesigned sections. However, in Study 2, when controlling for instructor and teaching style, the results indicate that the redesigned section performed significantly better on all measures compared to the traditional section.

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)0.016
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.005
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 head0.086
GPT teacher head0.474
Teacher spread0.388 · 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