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Record W4389274041 · doi:10.5206/eei.v33i1.16706

Learning the Learning Stages: An Examination of an Online Module for Training Pre-service Teachers

2023· article· en· W4389274041 on OpenAlexaffvenueabout
Jordan Shurr, Bree Jimenez, Emily C. Bouck, Jenny R. Root, Alexandra Minuk

Bibliographic record

VenueExceptionality Education International · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedical educationPsychologyIdentification (biology)Set (abstract data type)Scope (computer science)Computer scienceMathematics educationPedagogyMedicine

Abstract

fetched live from OpenAlex

Teachers of students with disabilities require a vast set of knowledge and skills for effective practice. While preparation programs provide a broad array of content and experiences through course work and guided practice, the wide scope of knowledge and skills required for beginning teachers, in conjunction with increased accessibility to online learning tools, creates an opportunity to enhance and augment teacher education beyond traditional delivery. We investigated an online module using practice-based videos with embedded training and assessment on learning stages in special education. We assessed the skills of pre-service students from three universities in the United States and Canada before and after online training in the stages of learning, specifically in self-appraisal and accuracy of identification related to instructional planning and assessment. Participants’ high self-assessment of their knowledge and skills did not match their skills in identifying the learning stages or appropriate instructional approaches.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.138
GPT teacher head0.436
Teacher spread0.298 · 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 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

Citations1
Published2023
Admission routes3
Has abstractyes

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