MétaCan
Menu
Back to cohort
Record W2775116690 · doi:10.1080/15366367.2017.1388113

Tracing the Assessment Triangle With Learning Progression-Aligned Assessments in Mathematics

2017· article· en· W2775116690 on OpenAlexaff
Emily Lai, Jennifer L. Kobrin, Kristen E. DiCerbo, Laura Holland

Bibliographic record

VenueMeasurement Interdisciplinary Research and Perspectives · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMathematics Education and Teaching Techniques
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsTracingMathematics educationComputer sciencePsychology

Abstract

fetched live from OpenAlex

We describe an application of the assessment triangle, using a learning progression as the “cognition” vertex. We summarize two studies to evaluate whether evidence of student performance is consistent with our progression. In Study 1, we conducted think alouds using draft assessment activities and evaluated responses in relation to the sequencing of several key progression stages. In Study 2, we piloted revised activities and conducted latent class analysis to classify students into categories based on stage mastery. Results were mixed, with evidence generally suggesting that the progression is more of a loose network of concepts than a strict hierarchy.

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)0.013
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.683
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.315
GPT teacher head0.553
Teacher spread0.238 · 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.

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

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueMeasurement Interdisciplinary Research and PerspectivesSame topicMathematics Education and Teaching TechniquesFrench-language works237,207