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Meaning Equivalence Reusable Learning Objects (MERLO) Access to Knowledge in Early Digital Era and Development of Pedagogy for Conceptual Thinking

2019· book-chapter· en· W2975959002 on OpenAlexaffabout
Uri Shafrir

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2019
Typebook-chapter
Languageen
FieldComputer Science
TopicOpen Education and E-Learning
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMeaning (existential)Mathematics educationFormative assessmentConceptual frameworkExcellenceDigital learningEngineeringEquivalence (formal languages)PedagogyComputer scienceSociologyMathematicsPsychologyPolitical science

Abstract

fetched live from OpenAlex

This chapter describes the effects of availability of digital knowledge on teaching, learning, and assessment, and the emergence of pedagogy for conceptual thinking with meaning equivalence in different knowledge domains in early digital era. It includes three proof-of-concept implementations of meaning equivalent reusable learning objects (MERLO) in three different contexts: 1) Course ‘Risk management in the Supply Chain' at Material and Manufacturing Ontario (MMO) Centre of Excellence, in 2002, to evaluate the potential of MERLO to assess and improve learning outcomes in workplace workshops to be offered jointly by MMO and University of Toronto Innovation Foundation; 2) in 2004, secondary school courses in mathematics, physics, and chemistry at Russian Academy of Sciences, Ioffe Physical-Technical Institute, Lycee ‘Physical-Technical High School' at St. Petersburg, to train teachers in administering MERLO formative assessments and evaluate learning outcomes in STEM courses (science, technology, engineering, and mathematics); 3) in 2006, implementing MERLO pedagogy, including development of MERLO databases for grades 9 – 12 mathematics courses at Independent Learning Center (ILC) of TVOntario.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0010.001
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 head0.037
GPT teacher head0.312
Teacher spread0.275 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations5
Published2019
Admission routes2
Has abstractyes

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