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Record W7011623551

MOVING FORWARD: Redefining Global Education through Curricular Innovation

2024· article· en· W7011623551 on OpenAlexaboutno aff

Bibliographic record

VenueSOURCE Sheridan's Institutional Repository (Sheridan College) · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationGovernment (linguistics)Work (physics)Circumstantial evidenceControl (management)Subject (documents)
DOInot available

Abstract

fetched live from OpenAlex

In 2020-2021, Sheridan received a Global Skills Opportunity Outbound Student Mobility Pilot Study grant to develop and reflect upon Collaborative Online International Learning (COIL) courses. With this funding, in Winter Sheridan launched its first three courses with COIL modules and undertook a report to better understand existing research on COIL methodologies as well as to learn from Sheridan’s pilot COIL experiences. In that report, Developing Global Education Experiences at Sheridan College Using COIL, we were particularly interested in understanding the efficacy of Sheridan’s COIL implementation for three students populations historically underrepresented in international education: low-income students, students with disabilities, and students who identify as Indigenous to Canada. We also provided a review of current research on the barriers to wider student access physical study abroad. While confirming the value of COIL for expanding access to global learning for all students, our report identified curriculum and cost – both direct and indirect - as two of the greatest barriers to accessing outbound education experiences, both among underrepresented groups and the general student population (Amaral & McLay, 2021, p.6-7). This raised the pressing question of how curricular innovations, including COIL, might be leveraged to support and expand access to physical outbound student mobility, an internationalization opportunity that continues to receive limited participation at Sheridan and other Canadian postsecondary education institutions (PSEIs). What follows here builds on our previous research as well as learning from the subsequent twenty-two COIL courses and four physical mobility trips undertaken by Sheridan from 2021-2024, all with the support of an additional CICan Global Skills Opportunity: Outbound Mobility Grant in Fall 2021. This report falls into two parts: The first is a literature review that looks closely at the relationship between virtual mobility and physical mobility, examining how PSEIs can seize the full potential of COIL to increase participation in outbound student mobility for all students while reducing barriers to high-impact international education practices, especially for historically underrepresented students. In an effort to hear directly from the students themselves about the potential relationship between these experiences, the second part of this report shares the results of qualitative interviews with post-secondary students who have experienced both COIL and physical mobility. It also asked students about the potential power of cost- and time-saving curricular designs for study abroad. Our sample size was small; however, the results point to the value of both “laddering” and course “bundling” approaches to COIL and study abroad and certainly warrant further investigation.While advancing key supporting actions laid out in Sheridan’s Internationalization & Global Connectivity Strategy 2021-2024, this scholarship, supported by the Government of Canada’s Innovation Fund administered by Colleges and Institutes Canada (CICan), contributes to knowledge production in an otherwise sparsely researched area to support policies and practices that encourage all Canadian students to benefit from the world of learning beyond our borders.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0070.016
Scholarly communication0.0150.013
Open science0.0030.019
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0080.001

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 head0.016
GPT teacher head0.313
Teacher spread0.297 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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