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Record W4247175560 · doi:10.4018/9781591409472.ch012

Selected and Constructed Response Systems in Mathematics Classrooms

2011· book-chapter· en· W4247175560 on OpenAlexaff
Leslee Francis Pelton, Timothy Pelton

Bibliographic record

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsClass (philosophy)Variety (cybernetics)Mobile deviceComputer scienceMathematics educationMultimediaHuman–computer interactionWorld Wide WebPsychologyArtificial 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.Request access from your librarian to read this chapter's full text.

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)0.007
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0050.005
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0170.005

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 head0.049
GPT teacher head0.323
Teacher spread0.274 · 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 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".

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Citations1
Published2011
Admission routes1
Has abstractyes

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