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Record W4388137140 · doi:10.5539/jedp.v14n1p1

Usability of an eLearning Professional Development Program for Elementary Classroom Teachers: ASSIST for Disruptive Classroom Behaviours

2023· article· en· W4388137140 on OpenAlexaffvenue
Matt Orr, Alzena Ilie, Christine T. Chambers, Isabel M. Smith, Penny Corkum

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2023
Typearticle
Languageen
FieldPsychology
TopicBehavioral and Psychological Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsUsabilityConstructivePsychological interventionClass (philosophy)PsychologyProfessional developmentClassroom managementMedical educationMathematics educationComputer scienceMultimediaPedagogyMedicineProcess (computing)Human–computer interaction

Abstract

fetched live from OpenAlex

An eLearning professional development (PD) program, ASSIST for Disruptive Classroom Behaviour, was developed using an iterative user-centred design approach. This program was designed to support teachers in the implementation of teacher-implemented in-class interventions for disruptive classroom behaviour (DCB). The objective of the current study was to determine the usability of this program. Overall, the results suggest that end-users (i.e., classroom teachers) and stakeholders (i.e., administrators, specialized teachers, school psychologists, and behaviour specialists) found the program to have high usability and reported that it was ready to be used by other teachers, that they found it flexible to adapt to their classroom setting; they provided high satisfaction ratings for this program. In addition to the positive findings, the primary constructive feedback was that tangible downloadable materials should be added to the ASSIST for Disruptive Classroom Behaviour program to meet classroom teachers’ needs better. Based on all results, the program, with a few minor modifications, was deemed ready for effectiveness testing.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.412
Threshold uncertainty score0.770

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.167
GPT teacher head0.456
Teacher spread0.288 · 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
Published2023
Admission routes2
Has abstractyes

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