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Record W49539561

A Toolkit for Learning: Using Technology to Close the Gap

2011· article· en· W49539561 on OpenAlexaboutno aff
Philip C. Abrami

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipExcellenceSet (abstract data type)LiteracyNumeracyComputer scienceInstructional designMathematics educationPedagogyKnowledge managementPsychologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

The Centre for the Study of Learning and Performance (CSLP) is a Montreal-based research centre of excellence that focuses on the generation of new knowledge about education through research and the mobilization of knowledge, working in partnership with educational practitioners by collaborating around the tools, techniques, and strategies for effective teaching and learning. Despite the strong provincial and federal interest in e-learning, there is neither uniform nor substantial evidence its effectiveness – especially without careful attention to the importance of pedagogical features in the design of educational software. There are no quick or effortless technological panaceas for learning, but educational technology can be a powerful tool when it is well designed, carefully validated, and properly implemented. Researchers at CSLP have developed an initial set of state-of-the art knowledge tools as part of the Learning Toolkit (LTK), which promote the development of essential educational competencies, including literacy, numeracy, inquiry, and self-regulation. They are available without charge to supplement and support classroom instruction.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.936
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.084
GPT teacher head0.365
Teacher spread0.282 · 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 designNot applicable
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

Citations2
Published2011
Admission routes1
Has abstractyes

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