MétaCan
Menu
Back to cohort
Record W2942600878 · doi:10.3102/0002831219843292

Why High School Grades Are Better Predictors of On-Time College Graduation Than Are Admissions Test Scores: The Roles of Self-Regulation and Cognitive Ability

2019· article· en· W2942600878 on OpenAlexaff
Brian M. Galla, Elizabeth P. Shulman, Benjamin D. Plummer, Margo Gardner, Stephen Hutt, J. Parker Goyer, Sidney K. D’Mello, Amy S. Finn, Angela Duckworth

Bibliographic record

VenueAmerican Educational Research Journal · 2019
Typearticle
Languageen
FieldPsychology
TopicGrit, Self-Efficacy, and Motivation
Canadian institutionsUniversity of TorontoBrock University
FundersDivision of Information and Intelligent SystemsNational Institute on AgingDivision of Research on Learning in Formal and Informal SettingsJohn Templeton FoundationBill and Melinda Gates Foundation
KeywordsGraduation (instrument)Test (biology)PsychologyPredictive validityAcademic achievementCognitionEntrance examSample (material)Cognitive testClinical psychologyDevelopmental psychologyPsychiatry

Abstract

fetched live from OpenAlex

Compared with admissions test scores, why are high school grades better at predicting college graduation? We argue that success in college requires not only cognitive ability but also self-regulatory competencies that are better indexed by high school grades. In a national sample of 47,303 students who applied to college for the 2009/2010 academic year, Study 1 affirmed that high school grades out-predicted test scores for 4-year college graduation. In a convenience sample of 1,622 high school seniors in the Class of 2013, Study 2 revealed that the incremental predictive validity of high school grades for college graduation was explained by composite measures of self-regulation, whereas the incremental predictive validity of test scores was explained by composite measures of cognitive ability.

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)0.003
metaresearch head score (Gemma)0.012
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
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 head0.033
GPT teacher head0.361
Teacher spread0.327 · 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

Citations125
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Educational Research JournalSame topicGrit, Self-Efficacy, and MotivationFrench-language works237,207