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Record W2076180180 · doi:10.3200/jexe.74.2.107-136

Teaching and Assessing Deductive Reasoning Skills

2006· article· en· W2076180180 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

VenueThe Journal of Experimental Education · 2006
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSyllogismCategorical variableDeductive reasoningMathematics educationLogical reasoningPsychologyMental modelComputer scienceMathematicsArtificial intelligenceStatisticsEpistemologyCognitive science

Abstract

fetched live from OpenAlex

Abstract Abstract. The author examined the effectiveness of training in symbolic logic for improving students' deductive reasoning. A total of 116 undergraduate students (approximately equal numbers of men and women) enrolled in 1st-year university philosophy courses in symbolic logic participated in 2 studies. In both studies, students completed booklets of categorical and conditional syllogisms at the beginning of the course and again at the end of the course. In Study 2, students also specified their reasoning strategies. Results indicated that students' strategies changed with training (students increased their use of mental models and mental rules with categorical and conditional syllogisms, respectively), but their reasoning performance improved only moderately. The educational implications of these results are explored. Keywords: assessmentcognitive processesreasoningstrategiestransfer

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.397
Teacher spread0.383 · 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