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Building Research Skills: Course-Integrated Training Methods

2002· article· en· W2081326473 on OpenAlexaff
Adriana Popescu, Radu Popescu

Bibliographic record

VenueJournal of Professional Issues in Engineering Education and Practice · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCourse (navigation)Training (meteorology)Engineering managementEngineeringConstruction engineeringEngineering educationConstruction managementEngineering ethicsComputer scienceMedical educationCivil engineering

Abstract

fetched live from OpenAlex

An enriched syllabus has been experimentally introduced to undergraduate level students enrolled in a geotechnical engineering course. Two research assignments have been integrated in the course, which require students to find information on a geotechnical engineering topic using both print and electronic resources available at the university library and on the Internet. Aiming to foster the development of technical information literacy and communication skills, the students are required to prepare a report based on a specific set of guidelines, followed by oral presentations of the topics researched. Between the two assignments, a lecture on the subject of identifying and using information resources for geotechnical engineering is included in the syllabus. Preliminary results of the study are presented along with directions for designing and implementing an enriched curriculum for geotechnical engineering courses.

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)0.005
metaresearch head score (Gemma)0.007
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 score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0030.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.005

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 head0.102
GPT teacher head0.543
Teacher spread0.441 · 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".

Quick stats

Citations20
Published2002
Admission routes1
Has abstractyes

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