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Record W1568471897 · doi:10.21432/t2nc7b

Digital Learners in Higher Education: Generation is Not the Issue

2011· article· en· W1568471897 on OpenAlexaffvenueabout
Mark Bullen, Tannis Morgan, Adnan Qayyum

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsUniversity of OttawaBritish Columbia Institute of Technology
Fundersnot available
KeywordsImmediacyInstitutionHigher educationInformation and Communications TechnologyEmpirical researchPsychologySet (abstract data type)Focus groupSample (material)Empirical evidenceICTSPedagogyLikert scaleMedical educationSociologyPolitical scienceSocial scienceComputer science

Abstract

fetched live from OpenAlex

Generation is often used to explain and rationalize the use of information and communication technologies (ICTs) in higher education. However, a comprehensive review of the research and popular literature on the topic and an empirical study at one postsecondary institution in Canada suggest there are no meaningful generational differences in how learners say they use ICTs or their perceived behavioural characteristics. The study also concluded that the post-secondary students at the institution in question use a limited set of ICTs and their use is driven by three key issues: familiarity, cost, and immediacy. The findings are based on focus group interviews with 69 students and survey responses from a random sample of 438 second year students in 14 different programs in five schools in the institution. The results of this investigation add to a growing body of research that questions the popular view that generation can be used to explain the use of ICTs in higher education.

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)0.005
metaresearch head score (Gemma)0.017
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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0050.005
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.036
GPT teacher head0.283
Teacher spread0.247 · 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

Citations222
Published2011
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Learning and TechnologySame topicImpact of Technology on AdolescentsFrench-language works237,207