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Record W3139064064 · doi:10.26522/brocked.v30i1.850

Perspectives from Students: How to Tame the Chaos and Harness the Power of Technology for Learning

2021· article· en· W3139064064 on OpenAlexaffvenueabout
Jenny Ge, Rachael Smyth, Michelle Searle, Lori Kirkpatrick, Rebecca Evans, Alexa Elder, Heather M. Brown

Bibliographic record

VenueBrock Education Journal · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of AlbertaWestern UniversityQueen's University
Fundersnot available
KeywordsFeelingPower (physics)PerceptionValue (mathematics)Educational technologyPsychologyCHAOS (operating system)SociologyComputer sciencePedagogySocial psychologyComputer security

Abstract

fetched live from OpenAlex

Technology continues to form an important part of the educational landscape, although the value of portable devices as learning tools is still being explored and debated. In light of the technology-based teaching methods suddenly brought into effect in response to the COVID-19 pandemic, the deliberate use of technology for learning is increasingly significant. The purpose of this article is to highlight student perspectives of learning with portable devices to inform the use of portable technology in the Canadian school system going forward. To gather student perceptions, the research team surveyed 704 students in grades 6 to 9 about their use of iPads in the classroom during a 1:1 technology initiative. While students were enthusiastic about the presence of portable technology, they also shared mixed feelings about the use of such technology as a learning tool. Key themes fell into three categories—engagement, inclusivity, and learning—as students shared their insight into the academic, social, and physical barriers that exist as a result of the technology. In the discussion, we identify lessons learned, especially in the area of self-regulation, and make recommendations on how to harness the power of this multi-faceted learning tool and minimize the chaos it can create when not utilized deliberately and carefully.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.317
Teacher spread0.305 · 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 designQualitative
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
Published2021
Admission routes3
Has abstractyes

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