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Record W2621390982 · doi:10.19030/jier.v13i1.9963

A Case Study Of The Integration Of Information And Communication Technology In A Northern Ontario First Nation Community High School: Challenges And Benefits

2017· article· en· W2621390982 on OpenAlexaffabout
Gerald Laronde, Katarin MacLeod, Lorraine Frost, Ken Waller

Bibliographic record

VenueJournal of International Education Research (JIER) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsSt. Francis Xavier UniversityNipissing University
Fundersnot available
KeywordsInformation and Communications TechnologyMainstreamTechnology integrationThe InternetIndigenousPublic relationsProfessional developmentQualitative researchPedagogyEducational technologySociologyPsychologyPolitical scienceComputer scienceSocial scienceWorld Wide Web

Abstract

fetched live from OpenAlex

A case study approach was used in examining Information and Communication Technology (ICT) use within a small First Nation high school in Northern Ontario. Quantitative and qualitative data was gathered from students, teacher, and the administrator, who participated in an online survey, followed by interviews on their use of ICT in education. How ICT was used in the classroom was examined as well as identifying the challenges and benefits. The students’ benefits included easier access to research through the Internet, facilitated organization through the use of Google drive, and the use of social media. Challenges were similar to those found in in mainstream schools with concerns of technical problems, off task behavior, and improper referencing. The teacher and administrator identified barriers preventing the increased use of ICT, including the lack of professional development, resources, and Indigenous language software. The administrator recognized there was a wide skill set range among teachers in the adoption of ICT integration into their teaching. Recommendations include more professional development in ICT for teachers, additional resources for ICT, and more development of Aboriginal language software.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.720
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.163
GPT teacher head0.410
Teacher spread0.246 · 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.

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
Published2017
Admission routes2
Has abstractyes

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