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Record W4392621825 · doi:10.62608/2164-1102.1067

Teacher Candidates Learn to Create Teacher Websites to Support Student Success Through Student, Parent and Community Engagement

2015· article· en· W4392621825 on OpenAlexaboutno aff
Jennifer Laffier, Meaghan Clarke, Alanna Houston

Bibliographic record

VenueJournal of Educational Leadership in Action · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsnot available
Fundersnot available
KeywordsStudent engagementStudent teacherPsychologyMathematics educationPedagogyMedical educationTeacher educationMedicine

Abstract

fetched live from OpenAlex

‘Theory to practice’ based learning activities are important for teachers in training. Therefore, as part of a course in a Bachelor of Education program in Ontario, Canada teacher candidates learned how to create effective teacher websites to promote the healthy development of students as well as engage students, parents, and the community in the learning environment. Student, parent, and community engagement are considered key elements of student success. This learning activity and how teacher candidates developed effective websites based on theories of healthy development and student success were explored in this paper. First, the research related to teacher websites is reviewed. Next, two case studies are presented to illustrate how components of teacher websites can engage students, their parents and the community. The case illustrations also demonstrated how educational and child development theories can be transferred to ‘practice’ for teacher candidates in their program of studies. Finally, recommendations for including teacher websites into teacher training programs and ways to engage students and parents in websites are included.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.516
GPT teacher head0.494
Teacher spread0.022 · 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 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
Published2015
Admission routes1
Has abstractyes

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