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Record W4366722520 · doi:10.21432/cjlt28357

Time Scarcity and Student Performance: Instructional Strategies for Busy Adult Online Students

2023· article· en· W4366722520 on OpenAlexvenueno aff
Melanie Holmes

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2023
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsnot available
Fundersnot available
KeywordsTimelineHuman multitaskingScarcityTask (project management)PsychologyFeelingTime managementFunction (biology)Mathematics educationSyllabusInstructional designComputer scienceSocial psychologyCognitive psychologyEngineering

Abstract

fetched live from OpenAlex

Adult online college students often suffer from time scarcity, which results in a drain on cognitive capacity and executive function, thus lowering their ability to plan, reason, and multitask. Busy students often engage in tunneling, ignoring everything but the most pressing concern. To support these students, educators should recommend timelines for task completion and divide larger assignments into smaller tasks. To reduce feelings of time scarcity, classrooms should have a predictable rhythm of regular assignments, a clear syllabus, meaningful assignments, and no busywork. Allocating points for preparatory tasks affirms their value and demonstrates respect for students’ time. Educators can help students build slack into their schedules so that they can better navigate disruptions by recommending multitasking strategies, suggesting rules of thumb, highlighting key points in instructions, and distinguishing essential from non-essential resources. Better understanding the challenges of the busy adult online student can help educators more effectively support their success.

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)0.000
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 score0.364
Threshold uncertainty score0.328

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.008
GPT teacher head0.273
Teacher spread0.266 · 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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

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