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Enhancing Instructor Capacity Through the Redesign of Online Practicum Course Environments Using Universal Design for Learning

2022· book-chapter· en· W4300862126 on OpenAlexaff
Jennifer Lock, Carol Johnson, Noha Altowairiki, Amy Burns, Laurie Hill, Christopher Ostrowski

Bibliographic record

VenueIGI Global eBooks · 2022
Typebook-chapter
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsSt. Mary's UniversityUniversity of Calgary
Fundersnot available
KeywordsPracticumUniversal Design for LearningCompetence (human resources)BachelorCapacity developmentCoachingOnline learningCapacity buildingComputer scienceKnowledge managementEngineeringPsychologyPedagogyMultimediaPolitical science

Abstract

fetched live from OpenAlex

A current trend in practicum or field experience programs is online and blended learning approaches being implemented alongside traditional classroom experiences. Principles of Universal Design for Learning (UDL) should be integrated in the design of these online environments in order to better support learning needs of all students. Instructors must also have confidence and competence in designing and facilitating learning within technology-enabled environments. This chapter reports on research conducted using design-based research to support instructor capacity development within field experience in a Bachelor of Education program. Three strategies are identified and discussed to enhance instructor's capacity: scaffolded support, modeling UDL practice in the online environment, and coaching to foster developing capacity using UDL. The chapter concludes by reporting on a new study that emerged as a result of this work, along with recommendations for practice.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.112
GPT teacher head0.366
Teacher spread0.254 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations2
Published2022
Admission routes1
Has abstractyes

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