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Record W2766534576 · doi:10.28945/3435

Evidence Based Management for Learning: An Experiment

2016· article· en· W2766534576 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

VenueInforming Science and IT Education Conference · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting Education and Careers
Canadian institutionsConcordia University
Fundersnot available
KeywordsMindsetKnowledge managementComputer sciencePersonal knowledge managementSubject (documents)Outcome (game theory)Subject matterLearning environmentOrganizational learningPsychologyMathematics educationPedagogyWorld Wide WebArtificial intelligenceCurriculum

Abstract

fetched live from OpenAlex

In this study we combine an immersive learning environment, an evidence based management method and the knowledge management SECI mindset to investigate students’ learning from scientific journal articles. The study entailed the use of a web-based peer to peer system (P2PS) that, gives an identified subject matter, engages students in extracting knowledge from a source, processes that knowledge to create new knowledge, assesses each other’s works, and then creates a test on the subject matter. We found that the immersive learning environment engaged students and improved their examination performance. However, comparing two groups, exposed versus not exposed to scientific journal article, both focused on keywords alone for the knowledge processing. This was not a desirable outcome from the knowledge management process and the tool. We believe this outcome is a result of engrained traditional learning and driven by our wish to make a change in educational practice, we propose our e-pedagogy methodology as a learning foundation for knowledge processing.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.918
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.005
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 head0.067
GPT teacher head0.319
Teacher spread0.252 · 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