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Record W4387808734 · doi:10.5539/jedp.v13n2p78

Listening to the Experts: A Needs Assessment of ASSIST for Disruptive Classroom Behaviour an eLearning Professional Development Program for Classroom Teachers

2023· article· en· W4387808734 on OpenAlexaffvenue
Matt Orr, Jacob Belliveau, 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 UniversityAcadia University
Fundersnot available
KeywordsPsychological interventionPsychologyContextualizationActive listeningMedical educationClass (philosophy)Classroom managementApplied psychologyPedagogyComputer scienceMedicine

Abstract

fetched live from OpenAlex

Teachers have limited access to training in in-class interventions for disruptive classroom behaviour (DCB). The goal of the current study was to understand the needs of end-users and stakeholders for teacher-implemented in-class interventions for DCB and their perspectives on eLearning about behaviour management. The needs assessment involved a mixed methods design using a structured interview and an online survey. Descriptive statistics were used to summarize survey responses, with open-ended data used for contextualization. The results revealed: (a) end-users and stakeholders were aware of and reported using many of the interventions that have been assessed in the literature, (b) more frequently used interventions were perceived as more effective, (c) interventions were inconsistently implemented and inconsistently effective, and (d) the implementation of interventions was influenced by student-teacher relationships. Results also indicated that while the participants perceived many positives of using eLearning, there were also some perceived barriers.

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 score0.311
Threshold uncertainty score0.619

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)0.0000.000

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 head0.163
GPT teacher head0.473
Teacher spread0.310 · 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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