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Record W4308802389 · doi:10.24908/pceea.vi.15843

Student-Teacher Becomes the Teacher-Student: Educator Preparation for Post-Secondary Students to Enrich High School Student STEM Learning in the Discovery Educational Initiative

2022· article· en· W4308802389 on OpenAlexafffundvenueabout
Graeme S. Noble, Josh Mogyoros, Laura M. Roa, Theresa Frost, Nicolas Ivanov, Nhien Tran-Nguyen, Dawn M. Kilkenny

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsRubricDiscovery learningCurriculumMathematics educationGeneral partnershipActive learning (machine learning)PedagogyPsychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Secondary school curricula in Canada for subjects like science, technology, engineering, and math (STEM) often focus on surface-level learning. While frequently believed to be a precursor for later complex and in-depth studies, such approaches to teaching and learning fail to adequately prepare students for life outside of the classroom, including for future studies in their field of choice. In response to demands for STEM programming that inspires critical thought over rote standardizable knowledge, in 2016, Discovery was created. An interdisciplinary program operated out of the University of Toronto’s Faculty of Applied Science and Engineering, Discovery symbolizes a participatory partnership between the University of Toronto and local secondary schools to support inquiry-based learning for entire classrooms of high school students on a longitudinal basis. Discovery Instructors—consisting of multilevel post-secondary students—work alongside secondary school educators to devise problem-based projects that address key Ontario curriculum targets. Over the course of a semester, high school students are guided by university Instructors to engage in collaborative projects in biology, chemistry, and physics to expand their learning portfolios beyond the confines of a traditional classroom. However, while Discovery seeks to diversify learning for all involved, educator development for Discovery Instructors has remained largely implicit. In this study, we will introduce a teaching development course into Discovery’s Instructor preparation. Within a blended online learning environment across seven weekly modules, Instructor assessment will consist directly of discussion boards containing content- and reflection-based prompts using holistic rubrics and indirectly via mentored students’ performance. A pilot program is currently underway with a sample of enrolled Instructors with data to be collected as the program progresses.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0130.007

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 head0.010
GPT teacher head0.309
Teacher spread0.299 · 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 designQualitative
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
Published2022
Admission routes4
Has abstractyes

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