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Record W3182808732 · doi:10.24908/pceea.vi0.14864

INVESTIGATING THE IMPACT OF ONLINE LEARNING ON ENGINEERING STUDENTS’ SOCIALIZATION EXPERIENCES DURING THE PANDEMIC

2021· article· en· W3182808732 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2021
Typearticle
Languageen
FieldComputer Science
TopicInnovations in Education and Learning Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSocializationPandemicPerceptionEngineering educationPsychologyLearning environmentCoronavirus disease 2019 (COVID-19)PedagogyEngineeringSocial psychologyMedicineEngineering management

Abstract

fetched live from OpenAlex

The global shift to online learning prompted by the COVID-19 pandemic has accentuated how learningonline alters postsecondary students’ socialization experiences and learning outcomes. In December 2020, alarge Canadian engineering faculty surveyed its undergraduate students to assess their learning experiences in the exclusive online environment during the pandemic. This paper used qualitative data from the survey, as complemented by descriptive quantitative results, to explore how the online environment impacted engineering students’ socialization processes and their perception of learning. Using Weidman’s model of socialization, this paper contributes to better understandings of the individual and particularlyenvironmental factors that have influenced engineering students’ socialization processes while they learn online during the pandemic, and the importance of social interactions to student learning in engineering education.

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.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.159
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.268
Teacher spread0.257 · 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