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Record W2791309469 · doi:10.1109/eitt.2017.30

A Case Study of American STEM Program Based on Canada/USA Mathcamp

2017· article· en· W2791309469 on OpenAlexaboutno aff
Lyu Xiaohong, MA Xiu-fang, Zhang Jieqi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Public relationsMathematics educationComputer sciencePolitical sciencePsychology

Abstract

fetched live from OpenAlex

The word "STEM" has come into public in recent years. And as the delivery of a series of policies on promoting STEM education in the USA, it has drawn the government and the public's attention to area of STEM education. Lots of scholars and experts are working hard on STEM education, aiming at finding out how can it be performed efficiently. In this research, a case study will be conducted over the Canada/USA Mathcamp, analyzing its mission, features and evaluation so that to make clear these research questions:(1) what STEM education focuses on;(2)what the case characterize;(3)how the case organizes efficiently;(4)how the case works in the STEM area. By analyzing the case, the research finally figure out that Canada/USA Mathcamp focuses on the design of the academic and residential life activities, features as lots of choices in learning topics, learning activities, classes for students and giving students freedom to choose. The mathcamp has organized qualified staff to support the camp and the camp has made great success. Taking example by Canada/USA mathcamp, the research also gives out some suggestions for those who are operating a STEM program.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.327

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 head0.096
GPT teacher head0.310
Teacher spread0.214 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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