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Record W1513078553

Secondary school teachers' evaluations of an online community portal

2010· dissertation· en· W1513078553 on OpenAlexaboutno aff
Tanya Ann Avrith

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2010
Typedissertation
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSession (web analytics)CurriculumPerceptionMathematics educationMedical educationPsychologyComputer sciencePedagogyMultimediaMedicineWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

If used effectively, computer supported learning environments (CSLE's) can be extremely useful communication and learning tools for both teachers and students. A template of an online community for the Lester B. Pearson School Boards computer supported learning environment, the Portal was created based on researched models. Secondary teachers at Riverdale High School in Pierrefonds, Quebec, were trained on the benefits and usages of the template so that they could integrate the design in their own curriculum. Teachers' attitudes and perceptions were examined prior to and after the training session. The template was tested out for a school year and suggestions for improvement of the design and training materials were documented. One of the teachers from the workshop was provided with follow-up training sessions where her perceptions, attitudes and feedback were examined. The researcher also actively participated in testing the template throughout the year in hopes of improving future template models and implementation strategies of the training itself

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.050
GPT teacher head0.376
Teacher spread0.326 · 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 source (direct Gemma or distilled Codex), 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
Published2010
Admission routes1
Has abstractyes

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