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Record W4383068548 · doi:10.35993/ijitl.v9i1.2717

Long Term Occasional Teaching and Mindfulness within the Pandemic

2023· article· en· W4383068548 on OpenAlexaffabout
Sarah Schouten, Prof. Dr. Thomas Ryan

Bibliographic record

VenueInternational Journal of Innovation in Teaching and Learning (IJITL) · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsNipissing University
Fundersnot available
KeywordsMindsetMindfulnessPandemicPsychologyCurriculumCoronavirus disease 2019 (COVID-19)PedagogyMedicineComputer sciencePsychotherapist

Abstract

fetched live from OpenAlex

This reflection focuses upon entry into the Ontario (Canada) teaching profession as a Long-Term Occasional (LTO) teacher who is a substitute for a permanent teacher on leave. The experiences within a grade four class during the Coronavirus pandemic are detailed herein. By implementing mindfulness into the curriculum, continuity and stability emerged for students, to reset every day during the Pandemic. In supporting the well-being of the whole student (which was initially sparked by COVID-19), its importance throughout all parts of learning was prominent. Navigating the role as an LTO (substitute) teacher during a Pandemic stimulated teachers and students, as learning how to teach in remote and in-person environments required a flexible and aware educator who was mindful. Support for the whole learner during a time was unprecedented and worrisome for many. As a result, mindfulness sessions supported students’ overall well-being and ensured that they were beginning their day with a clear, calm, and open mindset. This research has been cathartic and further developed a teaching philosophy that aligns itself with the needs for TFCL (Twenty-First Century Learners).
 Keywords: Long-Term Occasional Teaching, Mindfulness, Pandemic

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)0.010
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.035
GPT teacher head0.379
Teacher spread0.344 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

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