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Record W3173150344 · doi:10.24908/pceea.vi0.14930

DEVELOPING A SELF-PACED COURSE MODULE IN INFORMATION-FINDING AND LITERACY SKILLS FOR COURSEBASED MASTERS' STUDENTS

2021· article· en· W3173150344 on OpenAlexafffundvenueabout
Evan Sterling, Jolene Hurtubise

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsInformation literacyClass (philosophy)Plan (archaeology)Process (computing)Medical educationUnit (ring theory)Computer scienceAsynchronous communicationMathematics educationLiteracyPsychologyPedagogyWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

Academic librarians in Canada often teach information research skills and information literacy tostudents, including engineering students, via class presentations. These skills include knowledge of the diverse technical information sources available, the use of specialised databases and free search tools, and assessing and properly citing information; they are important in graduate studies and in engineering practise. Course-based masters’ students are a growing demographic in engineering, however their particular needs have not as often been targeted by librarians. In this project, we developed a graded asynchronous course module in these skills, for a new course in professional skills for M.Eng. students. It uses text, images, videos, short assignments and quizzes to follow thegeneral research and writing process for a technical report, marking a significant increase in the contact time for these skills. To date the unit has been taught twice, to over 200 students, with overall feedback being positive. We plan to continue its development and make it openlyavailable.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.006
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.267
Teacher spread0.261 · 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 designObservational
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

Citations1
Published2021
Admission routes4
Has abstractyes

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