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Record W4319263700 · doi:10.5539/jedp.v13n1p41

Student Attention and Distraction in Community College

2023· article· en· W4319263700 on OpenAlexvenueno aff
Paris S. Strom, Robert D. Strom, Tricia Sindel-Arrington, Renée V. Rude

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience, Education and Cognitive Function
Canadian institutionsnot available
Fundersnot available
KeywordsDistractionHuman multitaskingPsychologyReading (process)Attention spanValue (mathematics)Medical educationCommunity collegeMathematics educationCognitionCognitive psychologyMedicine

Abstract

fetched live from OpenAlex

The attention span of students and their ability to shut out distractions are learning conditions that concern educators more than ever before. Faculty at a community college assessed the learning conditions of students related to attention and distraction. Students self-administered an online Selective Attention Poll consisting of 20 multiple-choice items. The 239 culturally-diverse volunteers were 161 females and 78 males. Results indicated that most students believe they can get more work done in less time by multitasking, and consider this practice as necessary to meet the demands of college. Teachers could help students improve achievement by arranging innovative cooperative learning practices and developmental reading procedures. The majority of students declared their home as the most difficult place to study. Parents should provide a quiet environment and recognize student need for continued emotional support in early adulthood. The challenges for community college faculty are to help students improve study habits so they become more able to concentrate on assignments, read in-depth, value reflective thinking, diminish distractions, and build skills to work in groups.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.395
Teacher spread0.322 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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