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Selected and Constructed Response Systems in Mathematics Classrooms

2006· book-chapter· en· W2480427072 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIGI Global eBooks · 2006
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsClass (philosophy)Variety (cybernetics)Mathematics educationMobile deviceComputer scienceMultimediaHuman–computer interactionPsychologyWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

This chapter examines two types of response technologies (selected and constructed) available to support discussion and participation in the classroom, and describes our experiences using and observing them in a variety of mathematics, science, and computer science classes at various educational levels. Selected response systems (a.k.a., clickers) display multiple-choice questions, and then collect and analyze student responses, and present distribution summaries to the class. Constructed response systems allow students to use handheld computers to generate free-form graphical responses to teacher prompts using various software applications. Once completed, students submit their responses to the instructor’s computer wirelessly. The instructor may then select and anonymously project these authentic student work samples or representations to promote classroom discussion. We review the purpose, design, and features of these two types of response systems, highlight some of the issues underlying their application, discuss our experiences using them in the classroom, and make recommendations.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.483
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.031
GPT teacher head0.318
Teacher spread0.288 · 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