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Record W2767602083

Education Technology, E-Learning, and the Classroom Experience

2017· article· en· W2767602083 on OpenAlexaboutno aff
Jeffrey Beau Daniels

Bibliographic record

VenueBrock University Digital Repository (Brock University) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPedagogyMathematics educationComputer scienceInternet privacyPsychologyMultimediaPublic relationsBusinessPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Many school districts have encouraged movement from traditional classrooms and teaching strategies to strategies that employ the Internet and educational technology (Ed Tech). The transition to Internet-based Ed Tech has many benefits, such as reduced costs for institutions and greater convenience for students and instructors alike. However, this convenience comes at great expense as Ed Tech is often implemented with little thought to students’ education. This study adopted a philosophical inquiry approach to address concerns related to the implementation of the Internet-based Ed Tech in teaching. It begins by critiquing Ontario’s public policy around the procurement of Ed Tech and the use of e-learning strategies with some reference to other educational jurisdictions. It then discusses privacy issues and risks surrounding the use of Internet-related technologies in education, as well as changes in the relationship between students and teachers as education moves from the traditional classroom to the e-learning environment. Finally, the study critiques theories of education that support e-learning and shows that their implementation limits the transformative nature of education as defined by Gert Biesta.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.996

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 studies0.0050.001
Scholarly communication0.0000.002
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 head0.009
GPT teacher head0.240
Teacher spread0.232 · 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 designObservational
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
Published2017
Admission routes1
Has abstractyes

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