MétaCan
Menu
Back to cohort

Student Attention in the Modern Classroom: An Eye‐Tracking Field Study

2020· article· en· W3017328909 on OpenAlexaff
Kevin Santiago, Timothy D. Wilson

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsWestern University
Fundersnot available
KeywordsHuman multitaskingPsychologyTask (project management)Class (philosophy)GazeComprehensionEye trackingTracking (education)Mathematics educationApplied psychologyComputer scienceCognitive psychologyPedagogyEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Background Multitasking, commonly perceived to be a method of increasing productivity and maximizing time, is increasingly commonplace among student populations. However, considering recent work examining split attention and the cognitive demands of multitasking behaviour, multitasking is negatively associated with several key learning outcomes in laboratory environments such as information retention, comprehension and academic performance. To better characterize multitasking behaviours in modern classrooms, a field study was conducted using self‐reported student data coupled with gaze behaviour on attention during lectures. Specifically, this study contrasts graduate (GS) and undergraduate (UGS) students’ self‐assessments to in‐class attention to explore whether students are self‐aware of multitasking behaviours. Methods An online questionnaire was developed and distributed to a large mixed didactic anatomy class of GS and UGS. The survey was an amalgamation of three validated questionnaires, Media Use Questionnaire, Media Multitasking Class Index (MMCI), and the Attitudes Scale towards multitasking behaviours. In addition, a subset of students (GS & UGS) wore a single‐eye gaze tracking device during 2 lectures (Arrington Research Viewpoint) throughout the fall term. Students’ (n=4) visual fields were characterized as an index of attention to On‐task (course‐related) and Off‐task (non‐course related) activities. The gaze‐tracked location and duration of multitasking activities were identified off‐line and normalized to a percentage of class time to characterize the extent to which students engage in On‐ and Off‐task activities during lecture. Results Initial analysis of the MMCI survey section (n=105) indicates GS (n=69) estimated spending 7% less class time on on‐task activities than UGS (n=36) (74±0.2 vs 81±0.1, p < 0.02). Gaze tracking indicates GS (n=1) spent 37% more time engaging in multitasking (off‐task) behaviours than UGS (n=3) (45% vs 8% respectively). The UGS MMCI off‐task behaviours compared to that cohort’s gaze tracking suggests students estimate their multitasking behaviours accurately in the classroom (7±0.1% vs 8±0.0%). Through further analysis of attentional time dedicated to on‐task activities (75±0.0%), students largely focused on their notes (54±0.1%), multimedia presentation (11±0.1%), and then the professor (0.4±0.0%) of total class time. Of the time spent in off‐task activities (8±0.0%), distractors to attention included activities such as doodling, adjusting clothes and observing neighbours (7±0.0%), text messaging (0.3±0.0%), and consuming food and/or liquids (0.3±0.0%). This is the first field study of this kind indicating how multitasking is a part of student behaviour and how attention is allocated in the modern anatomy lecture hall through gaze‐tracking. As midterm grades become available, we will correlate both real and perceived behaviours to class performance.

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)0.003
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.053
GPT teacher head0.378
Teacher spread0.325 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topicImpact of Technology on AdolescentsFrench-language works237,207