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The Use of Web 2.0 Technologies in Formal and Informal Learning Settings

2013· book-chapter· en· W2490805693 on OpenAlexaff
Lisa A. Best, Diane N. Buhay, Katherine McGuire, Signe Gurholt, Shari Foley

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2013
Typebook-chapter
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsNew Brunswick Community CollegeUniversity of New Brunswick
Fundersnot available
KeywordsInformal learningThe InternetCompetence (human resources)PreferencePsychologyFormal learningSample (material)Knowledge managementComputer scienceMedical educationInternet privacyPedagogyWorld Wide WebSocial psychology

Abstract

fetched live from OpenAlex

It is often assumed that because the current generation of students is more technologically competent than previous generations, they would prefer to use technology for both formal and informal learning. The results of a series of empirical studies indicated that students in formal settings preferred face-to-face contact with their instructors and used Web 2.0 tools for communication and to complete specific class assignments; in their personal lives, these technologies were used for communication, music and video downloads, and online gaming. Although students did not use social networking in their classes, the use of these tools may provide educators with an alternative to course management systems. Results from a community sample indicated a preference towards using the Internet for information gathering, and even though respondents reported that the incorporation of social networking sites in informal education settings would be nice, it was not expected. Overall, both student and community participants utilized technology that was familiar to them. Thus, assuming technological competence in our students and implementing various technological applications in the classroom may be counterproductive if guidance and training are not provided.

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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.795
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.006
Scholarly communication0.0000.004
Open science0.0000.000
Research integrity0.0010.001
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 head0.019
GPT teacher head0.268
Teacher spread0.250 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations4
Published2013
Admission routes1
Has abstractyes

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