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Record W2289152028 · doi:10.19173/irrodl.v17i2.2284

Preservice Teachers’ Perception and Use of Personal Learning Environments (PLEs)

2016· article· en· W2289152028 on OpenAlexvenueno aff
Sami Şahin, Çelebı Uluyol

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsSocializationInformal learningThe InternetPerceptionComputer scienceScale (ratio)PsychologyWorld Wide WebKnowledge managementMultimediaPedagogySocial psychology

Abstract

fetched live from OpenAlex

Personal learning environments (PLEs) are Web 2.0 tools and services by which users’ access, construct, manage, and share educational contents in order to meet their learning needs. These environments enable users to manage their learning according to their own personal preferences. They further promote socialization and collaboration with their broad user networks and interaction facilities. In this study, with a case sample from a public university in Turkey, student teachers’ PLE use and their perceptions regarding these environments are examined. For data collection, the PLE Perception Scale and PLE Use Scale developed by the researchers were used. It was observed that all participants used various PLEs and found them easy and practical on the whole. However, it was found that this utilization mostly had the aim of access and sharing knowledge in learning, while use of constructing and managing it remained limited. Emailing, social networking, file sharing, video sharing, Internet searching, and social encyclopedias were found most commonly used PLEs. Our findings also show that gender and grade level do not have an effect on the perception and use of PLEs.

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)0.005
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 score0.248
Threshold uncertainty score0.622

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.107
GPT teacher head0.441
Teacher spread0.334 · 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

Citations39
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe International Review of Research in Open and Distributed LearningSame topicImpact of Technology on AdolescentsFrench-language works237,207