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Record W2974549977 · doi:10.52041/serj.v18i1.152

THREE PATHWAYS FROM ACHIEVEMENT GOALS TO ACADEMIC PERFORMANCE IN AN UNDERGRADUATE STATISTICS COURSE

2019· article· en· W2974549977 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStatistics Education Research Journal · 2019
Typearticle
Languageen
FieldMathematics
TopicStatistics Education and Methodologies
Canadian institutionsUniversity of OttawaUniversité du Québec à Trois-RivièresUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsAcademic achievementMathematics educationPsychologyContext (archaeology)Structural equation modelingCourse (navigation)Statistics educationMastery learningStatisticsMathematics

Abstract

fetched live from OpenAlex

The purpose of this study was to test three pathways from achievement goals to academic performance in statistics classes. Participants were 247 undergraduate students in psychology taking an introductory course on statistics. They completed questionnaires shortly after the mid-term, and their final grades were provided by their professors at the end of the semester. Structural equation modeling results reveal three distinct paths from achievement goals to academic performance. Results suggest that the more participants adopted mastery goals in the context of their statistics course, the less they experienced anxiety and the better they performed in the course at the end of the semester. First published May 2019 at Statistics Education Research Journal Archives

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.

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)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.352
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.317
GPT teacher head0.528
Teacher spread0.211 · 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