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Record W1494198775 · doi:10.21432/t2zw2d

The Role of Digital Technologies in Learning: Expectations of First Year University Students / Le rôle des technologies numériques dans l’apprentissage : les attentes des étudiants de première année universitaire

2012· article· en· W1494198775 on OpenAlexaffvenueabout
Martha A. Gabriel, Barbara Campbell, Sean Wiebe, Ronald J. MacDonald, A. McAuley

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Prince Edward Island
Fundersnot available
KeywordsSociologyHumanitiesLibrary scienceFocus groupPedagogyPsychologyArtComputer science

Abstract

fetched live from OpenAlex

A growing literature suggests that there is a disjuncture between the instructional practices of the education system and the student body it is expected to serve, particularly with respect to the roles of digital technologies. Based on surveys and focus group interviews of first-year students at a primarily undergraduate Canadian university and focus group interviews of professors at the same institution, this study explores the gaps and intersections between students’ uses and expectations for digital technologies while learning inside the classroom and socializing outside the classroom, and the instructional uses, expectations and concerns of their professors. It concludes with recommendations for uses of digital technologies that go beyond information transmission, the need for extended pedagogical discussions to harness the learning potentials of digital technologies, and for pedagogies that embrace the social construction of knowledge as well as individual acquisition. Des études de plus en plus nombreuses suggèrent qu’il existe un écart entre les pratiques d’enseignement dans le système de l’éducation et la population étudiante desservie, notamment en ce qui concerne le rôle des technologies numériques. La présente étude, fondée sur les résultats de sondages et d’entrevues de groupe auprès des étudiants de première année inscrits à une université canadienne principalement axée sur les études de premier cycle, ainsi que sur des entrevues de groupe auprès de professeurs du même établissement, explore les écarts et les concordances entre, d’une part, l’utilisation et les attentes des étudiants relativement aux technologies numériques dans l’apprentissage en classe et dans les relations sociales en dehors des classes, et, d’autre part, l’utilisation de ces technologies dans les pratiques d’enseignement, les préoccupation et les attentes des professeurs. L’étude se conclut par des recommandations concernant une utilisation des technologies numériques dépassant la transmission de l’information, et la nécessité de discussions pédagogiques poussées permettant d’exploiter le potentiel des technologies numériques dans le cadre de l’apprentissage ainsi que de méthodes pédagogiques adaptées à la construction sociale des connaissances et au mode individuel d’acquisition des connaissances.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.014
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0090.003
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative · Observational
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

Citations36
Published2012
Admission routes3
Has abstractyes

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