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Record W1563578108

Conceptualization of Assessment as a Formative System in Teaching and Learning: A Cybernetic Model

2014· article· en· W1563578108 on OpenAlexvenueno aff
Yaping Chen, Rongping Cao

Bibliographic record

VenueStudies in literature and language · 2014
Typearticle
Languageen
FieldNeuroscience
TopicCognitive Science and Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsFormative assessmentCyberneticsConceptualizationPropositionComputer scienceCognitive sciencePsychologyArtificial intelligenceMathematics educationEpistemologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

A cybernetic model of assessment and learning is proposed. The basic proposition is that all assessment could be viewed as formative in learning, which has two implications: 1) assessment is a complex subsystem of learning, which is also a complex system by itself, as articulated in cybernetics; 2) this assessment subsystem has a formative nature in that regardless of its forms and approaches, it controls or regulates the learner’s learning by influencing his/her decision-making in learn recognition of assessment as a controlling or modeling system in learning may also help clarify the relationships between assessment and other subsystems in learning.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.181

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 communication0.0000.000
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 head0.034
GPT teacher head0.405
Teacher spread0.371 · 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 designQualitative
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

Citations0
Published2014
Admission routes1
Has abstractyes

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