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Record W4233219492 · doi:10.18260/1-2--34793

Improving Persistence and Success for At-risk STEM Students Through a Summer Intervention Program at a Hispanic-serving Institution

2020· article· en· W4233219492 on OpenAlexfundno aff
Melissa Danforth, Charles Lam, Ronald Hughes

Bibliographic record

Venue2020 ASEE Virtual Annual Conference Content Access Proceedings · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsnot available
FundersCalifornia State University, BakersfieldUniversity of WaterlooU.S. Department of EducationDirectorate for STEM EducationNational Science Foundation
KeywordsGraduation (instrument)Remedial educationMedical educationIntervention (counseling)Mathematics educationPersistence (discontinuity)InstitutionPsychologyMedicineMathematicsEngineeringPolitical science

Abstract

fetched live from OpenAlex

This Complete Evidence-Based Practice paper describes a four-year summer intervention program for at-risk STEM students at California State University, Bakersfield (CSUB) that was supported by an NSF IUSE grant * .CSUB is a Hispanic Serving Institution (HSI) and Minority Serving Institution (MSI) located in a service region with historically low educational achievement.Students from the region lag behind their statewide peers in mathematical readiness for college, which affects their persistence and success in STEM fields at CSUB.The summer program paired small groups of students with faculty mentors to complete STEM projects designed to create a connection between mathematics and STEM disciplines.Analysis of retention and graduation rates, participant survey data, and interviews of participants and faculty mentors show that the program had a positive impact on the participants' persistence and success in STEM fields.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.146
GPT teacher head0.336
Teacher spread0.190 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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