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Record W4387665413 · doi:10.15173/ijsap.v7i2.5203

Equalizing student and teacher: Using COVID-19 to (re)imagine curriculum

2023· article· en· W4387665413 on OpenAlexvenueno aff
Megan Adams, Rachel Gaines, Anete Vásquez, Alyssa Noland, Faith Castellano, Sharae Gowdie, Paige Carter

Bibliographic record

VenueInternational Journal for Students as Partners · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumCapstoneCoronavirus disease 2019 (COVID-19)Flexibility (engineering)Medical educationPandemicMental healthInstitutionPsychologySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakPedagogyHigher educationSociologyMedicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

COVID-19 created an opportunity to (re)envision students as partners in curriculum development and the curriculum process. Understanding the design and delivery of courses as a flattened hierarchy, particularly with graduate students as partners, is the focus of this study. This article reports findings from research undertaken collaboratively with students as partners in developing a new approach for conducting a capstone course and project. This research was enacted at a research-intensive university in the United States in 2020 and 2021. We describe the need for the shift in stance to students as partners in our institution as well as what the findings indicate as imperatives for teachers in both K–12 settings and institutions of higher education. The findings indicate how teachers’ mental health and experiences of stress were affected by specific attributes of the pandemic and pandemic teaching (which aligns with the majority of COVID-19 research in education), as well as how some learned to cope with these demands. Findings also indicate the need for flexibility in all learning environments.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.220
GPT teacher head0.660
Teacher spread0.440 · 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 designNot applicable
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

Citations2
Published2023
Admission routes1
Has abstractyes

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