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Record W3104150520 · doi:10.6000/1929-4409.2020.09.121

Digitalization Trends in Education and Blended Learning

2020· article· en· W3104150520 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2020
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsnot available
FundersKazan Federal University
KeywordsBlended learningProcess (computing)Computer scienceVisualizationMultimediaPerceptionEducational technologyExperiential learningMathematics educationArtificial intelligencePsychology

Abstract

fetched live from OpenAlex

The article under review actualizes the problem of using digital computer technologies in the process of blended learning. The notion of «blended learning» is determined and specified according to various sources. Various models and ways of organizing this kind of work as an alternative to the standard form of learning are presented. The article presents a positive experience of using blended learning technology. Much attention is paid to visualization as one of the teaching tools to create educational materials for blended learning. The synthesis of verbal materials (especially spoken) and visual elements (pictures, graphic notes, animations, films, plots, and diagrams, etc.) within the limits of one text is an instrument to optimize the process of semantic perception and understanding text information. All the benefits of this modern technology allow students to establish a holistic adoption of this model and a positive direction for the development of blended learning as an innovative teaching technology

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.513
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.077
GPT teacher head0.350
Teacher spread0.274 · 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