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Record W3144598632 · doi:10.29173/iasl7785

Building Communities Through Online Spaces

2021· article· en· W3144598632 on OpenAlexaffvenueabout
Joanne De Groot, Jennifer Branch, Kandise Salerno

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPerceptionOnline learningSense of communityPsychologyFace (sociological concept)Distance educationOnline communityLearning communityOnline participationMedical educationMathematics educationPedagogySociologyComputer scienceWorld Wide WebThe InternetSocial psychologyMedicine

Abstract

fetched live from OpenAlex

This paper explores the perceived information learning needs of students registered in the Teacher-Librarianship by Distance Learning (TLDL) program at the University of Alberta. This paper reports on the findings related to two main questions: 1. To understand the perceived information needs of students who are completing a Master of Education degree completely online. 2. To understand students’ perceptions of “community” in online spaces. To address these questions, an online survey was distributed to current and former students of this online teacher librarianship education program. Respondents indicated that they had a strong sense of community through the program and the online courses. Community within the TLDL program is built through student-to-student and instructor-to-student interactions. Respondents’ perceived sense of community aligned with the existing literature about building online communities. This study indicates that as more students choose to take courses online, instructors need to carefully consider how to make rich learning experiences that are as good as, or even better than face-to-face learning experiences.

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)0.000
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 score0.377
Threshold uncertainty score0.697

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.354
Teacher spread0.299 · 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

Citations0
Published2021
Admission routes3
Has abstractyes

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