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Record W4413998695 · doi:10.3389/feduc.2025.1636446

A utility value intervention to support student engagement in online upper-level undergraduate courses

2025· article· en· W4413998695 on OpenAlexaff
Katie J. Shillington, Stanley M. Lo, Lawrence Hobbie

Bibliographic record

VenueFrontiers in Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsWilfrid Laurier University
FundersUniversity of California, San DiegoDirectorate for Biological SciencesAdelphi University
KeywordsStudent engagementValue (mathematics)Intervention (counseling)Computer scienceMathematics educationMultimediaPsychologyMachine learning

Abstract

fetched live from OpenAlex

Introduction Utility value interventions are assignments designed to increase student motivation by helping them find personal value in what they are learning. Previous studies have found utility value interventions to lead to improved student outcomes. The purpose of this study was to investigate the impact of a utility value (UV) intervention on the academic outcomes of undergraduate students in online courses, compared to students in a control condition. Methods A total of 1,243 students in upper-level online biology courses participated and were randomized either to UV ( n = 589) or control ( n = 654) conditions. Results The intervention had a positive effect, as UV participants engaged with and participated in their courses significantly more than students in the control condition. Utility value participants also wrote significantly longer essays, and used more pronouns, social process words, and cognitive process words than students in the control condition. However, UV participants did not have higher course grades than students in the control condition. Additionally, underrepresented minority (URM) status was a significant predictor of course grade; non-URM students had significantly higher course grades than URM students. Discussion The utility value intervention did have a positive effect on student course engagement but, contrary to most previous studies, did not lead to higher student course grades, perhaps because these classes were online, because they were upper-level instead of introductory, or because the study was conducted during the COVID-affected spring 2020 quarter. Findings from this study can help institutions decide whether to adopt UV interventions in online courses.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.433
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.412
Teacher spread0.379 · 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 teacher head, 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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