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Record W4412991280 · doi:10.26443/msurj.v20i2.327

Users Perform Better in a First-Year Science Course: Assessment, Learning and Well-Being in the Undergraduate Context

2025· article· en· W4412991280 on OpenAlexaffabout
Noah Karabanow, Armin Yazdani, Tamara L. Western

Bibliographic record

VenueMcGill Science Undergraduate Research Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsMcGill University
Fundersnot available
KeywordsCourse (navigation)Context (archaeology)Mathematics educationComputer scienceMedical educationPsychologyData scienceEngineeringMedicineHistory

Abstract

fetched live from OpenAlex

The secondary to post-secondary transition is of critical importance for student learning and well-being. This transition is examined in the context of an undergraduate science course at a large Canadian university. A survey was completed by 20 students to assess different components of their mental health, as well as the study strategies they employ. A semi-structured interview that included a Q-sort activity was conducted following the survey. 12 students’ grades were included in the analysis (60%). A strong negative correlation was found between anxiety and task performance (r = -0.697, p = 0.012). Results from a Mann-Whitney U test found that students who preferentially studied using the repetition of information (e.g., flashcards, re-reading), a strategy termed rehearsal, had significantly higher task grades (p = 0.029) than those who didn’t. Themes that arose from the interviews echoed those found in similar studies: grouping assessments within close proximity and in a cumulative format contributes to assessment anxiety, and remote assessment formats include both positive and negative effects. Preliminary findings highlight the often overlooked benefits of rehearsal learning when used in the appropriate context. Increased sample size and a categorization scheme that better describes different learner profiles should be sought in future studies.

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 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)0.099
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0990.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0150.007
Scholarly communication0.0030.004
Open science0.0020.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.094
GPT teacher head0.495
Teacher spread0.401 · 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; both teacher heads agree on what is shown here.

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
Published2025
Admission routes2
Has abstractyes

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