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A Mixed Blessing? Students’ and Instructors’ Perspectives about Off-Task Technology Use in the Academic Classroom

2019· article· en· W2948290147 on OpenAlexaffvenueabout
Elena Neiterman, Christine Zaza

Bibliographic record

VenueThe Canadian Journal for the Scholarship of Teaching and Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBlessingClass (philosophy)Task (project management)AutonomyPsychologyMathematics educationPedagogyPerceptionComputer scienceEngineeringPolitical science

Abstract

fetched live from OpenAlex

The widespread use of technological devices in an academic classroom brought with it many learning opportunities, but also posed a challenge of handling the off-task technology use in class. The literature on this topic is growing, but we still know relatively little about students’ and instructors’ perceptions regarding the off-task technology use in class. This paper addressed this gap by examining (1) how do students and instructors perceive technology in the classroom, and (2) who do they believe should be responsible for minimizing off-task technology use in class? Analyzing data from a mixed-method study with students and instructors in a Canadian university, we show that while students acknowledged that the off-task technology use can be distracting, they considered it a matter of personal autonomy, which can only be regulated when it creates distractions for others. The instructors had a more complex view and posed some challenging questions about the relationship between student engagement and technological distractions, the impact of technology on learning process, and the responsibility of educators in higher education. In conclusion, we reflect on some of the questions that ought to be considered when handling the off-task technology in an academic classroom.

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)0.013
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.006
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 head0.044
GPT teacher head0.349
Teacher spread0.304 · 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.

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

Citations39
Published2019
Admission routes3
Has abstractyes

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