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Record W2179337464 · doi:10.5296/jei.v1i2.8381

A Smart Way of Coping with Common Core Challenges - Introduction to CAFA SmartWorkbook

2015· article· en· W2179337464 on OpenAlexaff
Jaehwa Choi, Mi-Seon Kang, Najung Kim, William Dardick, Xinxin Zhang

Bibliographic record

VenueJournal of Educational Issues · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFormative assessmentCoping (psychology)Information and Communications TechnologyMathematics educationPedagogyComputer sciencePsychology

Abstract

fetched live from OpenAlex

<p>The Common Core State Standards (CCSS) in mathematics are currently adopted in most U.S. states. Nonetheless, most math teachers across the country are still experiencing difficulties in putting these standards into practice. Teachers and local school administrators are faced with a challenge of adapting methodologies in instruction and assessment to ensure that students master the knowledge and skills required in the new standards. This leads to an urgent need for well-designed teaching and assessment tools for math education that are aligned to the CCSS.</p><p>The purpose of this paper is to illustrate the Computer Adaptive Formative Assessment (CAFA) SmartWorkbook which is an Information and Communication Technology (ICT) based teaching and assessment tool specially designed for coping with challenges in implementing the CCSS in mathematics. The CAFA SmartWorkbook represents a new stage in exploring opportunities in educational innovation, capitalizing on advances in assessment and technology. This system can be an effective solution to cope with CCSS challenges in both theoretical and practical points of view for students, teachers, parents, and educational administrators.</p>

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.355
Threshold uncertainty score0.250

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 studies0.0000.000
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 head0.089
GPT teacher head0.408
Teacher spread0.318 · 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 designNot applicable
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

Citations2
Published2015
Admission routes1
Has abstractyes

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