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Record W2586809421 · doi:10.18260/1-2--6615

Infrastructure Materials: An Inquiry Based Design Sequence

2020· article· en· W2586809421 on OpenAlexaffabout
Yixin Shao, Laura Walhof, Joseph J. Biernacki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNanotechnology research and applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsSession (web analytics)Class (philosophy)HomogeneousFoundation (evidence)Computer scienceCivil engineeringWork (physics)EngineeringArchitectural engineeringWorld Wide WebMechanical engineeringArchaeologyArtificial intelligenceHistory

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 2525 Infrastructure Materials An Inquiry-Based Design Sequence Joseph J. Biernacki, Laura Walhof, Yixin Shao1 National Science Foundation Center for Science and Technology of Advanced Cement-Based Materials Northwestern University/ Glenbrook South High School/ McGill University Infrastructure materials are among the most used material on earth with concrete being used more than any other except water. Annually, over one ton of concrete is used per person on earth. The Infrastructure Materials module introduces students to the design of concrete materials and design with concrete materials for use in infrastructure applications: roads, bridges, buildings, dams, waterways, airport pavements, etc. The module includes a sequence of activities which enables students to discover factors critical to understanding how to design concrete and how to design with concrete. It guides students developing the realization that concrete is not a simple homogeneous material which is purchased in a sack at the hardware store, but rather a complex heterogeneous class of material with a wide range of design diversity. The activities include: a concrete hunt in which students try to identify objects which are made of concrete and the reasons why concrete was used for that application, an exploration activity which give students a chance to discover what concrete is made of and what the apparent characteristics of the material are, an activity in which students make concrete and discover that concrete gets hard because of a chemical reaction and concomitant physical changes, an introduction to fracture processes and concepts of brittle failure and reinforcing mechanics and finally a design activity which inspires the students to design, build, test and redesign a product and apply the principles of chemistry, physics and mathematics, which they explored in preceding activities. These and other activities have been modeled and field-tested in outreach programs by the Center for Advanced Cement- Based Materials (ACBM) at Northwestern University and at schools with teachers and students ranging from middle school to high school age. Infrastructure Materials is part of a larger National Science Foundation-funded program called Materials World Modules (MWM). MWM is a series of modules which introduce students to important contemporary topics in materials science. Each module is a sequence of self-contained activities which provide students with the background necessary for them to engage in inquiry through design. Background We are in the midst of major changes in both pre-college (K-12) and college level education. It is becoming increasingly evident that the traditional approach to teaching —wherein a teacher provides a set of stimuli and reinforcements in an effort to elicit desired responses from students— is unsuccessful in fostering understanding, synthesis, eventual application of knowledge and the ability to use information, Trowbridge and Bybee2. As an alternative, Trowbridge and Bybee2 suggest an inquiry-based approach which encourages student input of creative ideas, uses alternative sources of information,

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.776
Threshold uncertainty score0.635

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)0.0010.000

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 head0.087
GPT teacher head0.296
Teacher spread0.209 · 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 designBench or experimental
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

Citations3
Published2020
Admission routes2
Has abstractyes

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