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Record W6986253373

Organically created project space: an evaluation of project space as a learning environment for young adult learners with learning disabilities

2020· dissertation· en· W6986253373 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2020
Typedissertation
Languageen
FieldSocial Sciences
TopicEducational Environments and Student Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsLearning environmentSpace (punctuation)Learning disabilityAmbiguityActive learning (machine learning)Control (management)Experiential learningMeaningful learningLearning sciencesCooperative learning
DOInot available

Abstract

fetched live from OpenAlex

This thesis provides an examination of the effectiveness of a Project Space as a learning environment for students with learning disabilities. Research was conducted through precedent studies and a case study of two Ontario secondary schools. Photo elicitation interviews with staff and students with learning disabilities were conducted to provide insight from users of the learning environment. Within each of the case study schools’ there was an example of an organically created Project Space. These rooms were ambiguous in their design as they serve multiple purposes with each of the schools. This provided evidence that Project Space which allows control and ambiguity within a larger footprint is a more effective learning environment for students with learning disabilities. According to the Hamilton-Wentworth District School Board’s guidelines for secondary school design a learning environment must be comfortable, flexible/adaptable, and provide extended learning environments to be inclusive. The primary goal of this study was to develop recommendations to the design problems evidenced in the precedent and case studies through the insights provided by students with learning disabilities and staff that use the space every day for learning.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.301
Teacher spread0.267 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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