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Learning in the Digital Age with Meaning Equivalence Reusable Learning Objects (MERLO)

2015· book-chapter· en· W2493579668 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

VenueAdvances in educational technologies and instructional design book series · 2015
Typebook-chapter
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of TorontoToronto Metropolitan University
Fundersnot available
KeywordsMeaning (existential)Cognitive sciencePsychologyReciprocity (cultural anthropology)ComprehensionCognitionDigital learningSemioticsEducational psychologyCognitive psychologyComputer scienceMathematics educationEpistemologySocial psychologyPedagogyNeuroscience

Abstract

fetched live from OpenAlex

In this chapter we describe a novel pedagogy for conceptual thinking and peer cooperation with Meaning Equivalence Reusable Learning Objects (MERLO) that enhances higher-order thinking; deepen comprehension of conceptual content; and improves learning outcomes. The evolution of this instructional methodology follows insights from four recent developments: analysis of patterns of content and structure of labeled patterns in human experience, that led to the emergence of concept science; development of digital cyber-infrastructure of networked information; research in neuroscience and brain imaging, showing that exposure of learners to multi-semiotic inductive problems enhance cognitive control of inter-hemispheric attentional processing in the lateral brain, and increase higher-order thinking; research in evolutionary dynamics on peer cooperation and indirect reciprocity, that document the motivational effect of knowledge of being observed, a psychological imperative that motivate individuals to cooperate and to contribute to the common good.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.663
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
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 head0.047
GPT teacher head0.331
Teacher spread0.284 · 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