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Record W3101724804 · doi:10.22329/celt.v13i0.6011

Online Instructor Development: A COOL story

2020· article· en· W3101724804 on OpenAlexaffvenue
Nick Baker, Freer John, Nobuko Fujita, Higgison Alicia, Mark Lubrick, Brandon M. Sabourin, Sylvester Jane, Van Wyk Paula

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2020
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsSt. Clair CollegeUniversity of Windsor
Fundersnot available
KeywordsMathematics educationHigher educationPsychologyPedagogy

Abstract

fetched live from OpenAlex

This paper explores the development of a flexible, free, online certificate program built on open educational resources to support instructors transitioning to online and non-traditional teaching modes. The program offers multiple pathways to completion, including recognition of prior learning and immersing participants in the online learning environment. We describe the challenges learners had to overcome to engage in the program and how, in doing so, they were able to embrace constructivist and connectivist approaches. These, in turn, afforded them ongoing connections, broke the mold of preconceptions and myths when preparing to engage future online learners, and shaped their practice through exposure to learning theories and evidence-based practices. In this paper, we explore the initial design of the program through the lens of the program facilitators and learners from the first cohort, and share our collective learning and reflections from this process.
 
 Nous nous penchons ici sur l’élaboration d’un programme de certificat gratuit, souple et basé sur des ressources éducationnelles ouvertes. Offert en ligne, ce programme est conçu pour aider les professeurs à faire la transition vers l’enseignement en ligne et vers des méthodes d’enseignement non traditionnelles. Il existe de nombreuses manières de satisfaire aux exigences du programme, y compris la reconnaissance de l’apprentissage et de l’immersion dans un environnement d’apprentissage en ligne. Dans notre article, nous faisons état des difficultés auxquelles les apprenants ont été confrontés lors de leur participation au programme. Nous montrons comment, en surmontant ces obstacles, ils se sont approprié des approches constructivistes et connectées, lesquelles leur ont permis d’établir des connexions et de déconstruire certains mythes et préjugés au bénéfice des futurs apprenants en ligne, tout en les aidant à façonner leur pratique au moyen de théories de l’apprentissage et de pratiques fondées sur des données probantes. Notre étude examine la conception initiale du programme à partir du point de vue des animateurs et des apprenants de la première cohorte. Nous présentons également les réflexions et les leçons que nous avons tirées, collectivement, de cette expérience.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.002
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 head0.015
GPT teacher head0.250
Teacher spread0.234 · 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 designOther design
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".

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Citations0
Published2020
Admission routes2
Has abstractyes

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