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Record W2187726401

On Teaching to the Masses and the Masters: Competing Requirements for Undergraduate Education

2006· article· en· W2187726401 on OpenAlexaff
David Callele, Dwight Makaroff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Assessment and Pedagogy
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCuriosityContradictionMathematics educationContext (archaeology)PsychologyPopulationPersonalityPedagogyTeaching methodSocial psychologySociologyEpistemology
DOInot available

Abstract

fetched live from OpenAlex

Generational changes in the preparation of students for entering university have been substantial. The 21 st century high-school experience is very dierent from that of the prior generation. In particular, the social and academic skills developed seem to be those of reaction to external stimuli and peer-group conformance rather than individuality, personal responsibility, and problem-solving. Responsibility and activity tend to be concentrated in severely restricted environments, where the skills of creative problem-solving are not suciently emphasized and/or developed. The “Nintendo generation” of students tend to be visual learners [10] and they expect significant external motivation. However, introductory university courses in the sciences assume that independent curiosity motivates the expected problem-solving approach, a significant contradiction between expected and actual personality traits. The greater mass of the student body is ill-served by traditional pedagogy geared to those who readily master the materials. In this paper, we present a strategy for introducing and reinforcing structured problem-solving strategies that are relevant to both the sciences and to the larger audience of the general student population. We include the motivation, teaching methods, and course topics. We stress active learning within a meaningful context that enables students to take the lessons from this course into the remainder of their undergraduate degrees and the rest of their lives.

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 categoriesnone
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 score0.758
Threshold uncertainty score0.751

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 integrity0.0000.000
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 head0.046
GPT teacher head0.415
Teacher spread0.369 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2006
Admission routes1
Has abstractyes

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