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Record W4200457640 · doi:10.1558/cj.19666

When “Blended” Becomes “Online”

2021· article· en· W4200457640 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.

Bibliographic record

VenueCALICO Journal · 2021
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPandemicStudent engagementBlended learningLearning analyticsCoronavirus disease 2019 (COVID-19)PsychologyAnalyticsMedical educationHigher educationMathematics educationComputer scienceEducational technologyPolitical scienceMedicineData science

Abstract

fetched live from OpenAlex

With the outbreak of COVID-19 in 2020, many universities shifted to online teaching. However, some online instruction had already been implemented well before the pandemic. This study investigates (1) how engagement in blended CALL activities differed during the pandemic, and (2) in what ways the assessment outcomes were associated with student engagement during the pandemic. The study was conducted in an English for academic purposes (EAP) course at a Hong Kong university that had already implemented blended learning for several years. Adopting an analytics-based approach, 469,286 data logs in a learning management system were analyzed to measure students’ engagement and their respective self-directed behavior. The retrieved student data covered the time both before and during the pandemic. Our findings reveal that students were primarily engaged for assessment purposes; however, those in the pandemic cohort demonstrated better self-directed behavior, such as early and regular engagement. Although the results indicated a relatively strong association between student engagement and course outcomes, the students during the pandemic seem to have managed their learning more effectively.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.593
Threshold uncertainty score0.379

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 studies0.0000.000
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 head0.020
GPT teacher head0.283
Teacher spread0.263 · 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