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Record W2278277996 · doi:10.14288/1.0086575

Teachers’ preferred methods of learning about the topic of "student motivation"

2008· article· en· W2278277996 on OpenAlexaboutno aff
Lorraine Kay Leishman

Bibliographic record

VenuecIRcle (University of British Columbia) · 2008
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Teacher Development
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPsychologyPedagogyComputer science

Abstract

fetched live from OpenAlex

In 1991, a needs assessment was conducted by the Southern Alberta Professional Development Consortium to determine teachers preferences for topics of professional development. Eighty-eight percent of 3000 teachers chose "Student Motivation" as their number one choice. This study and its questionnaire were specifically designed to accomplish two objectives: (1) to refine the general request for "Student Motivation" topic into specific topics desired by teachers, and (2) to gain some clarity on how teachers (in what formats) want to have this professional development delivered. Teachers in this study revealed their general preference for learning about student motivation in a collaborative manner. However, a more formal (institutional) classroom-like format was desired for some specific problems. There was evidence that teachers were not just choosing learning methods based on patterns associated with adult learners, but also in response to specific problems (without seeming to adhere to adult norms). Recommendations for both facilitators of professional development as well as those who are to be receiving it are included.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.052
GPT teacher head0.271
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2008
Admission routes1
Has abstractyes

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