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Record W4386498046 · doi:10.55375/joerp.2023.3.5

Perceptions of Chinese Students on Online Learning versus Face-to-Face Learning in the Bachelor of Science in Rehabilitation Science Program

2023· article· en· W4386498046 on OpenAlexfundno aff
Sai Meng, Tiantian Feng, Jingwen Li, Yuhao Zhou

Bibliographic record

VenueJournal of Educational Research Progress · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducation and Learning Interventions
Canadian institutionsnot available
FundersConcordia University
KeywordsFace-to-faceBachelorPsychologyMedical educationPerceptionModalitiesMathematics educationMedicineSociologyGeography

Abstract

fetched live from OpenAlex

Purpose: This research project aimed to investigate the perceptions of online learning versus face-to-face learning of Bachelor of Science in Rehabilitation Science (BSRS) Program students in Cohorts 2017, 2018 and 2019 in China. Methods: This quantitative study utilized a cross-sectional descriptive survey design involving 177 students from the BSRS Program. A link to the Qualtrics survey, which included a consent page, was sent to participants via social media platforms. Pilot testing of interviews was conducted to get an overlook of students’perceptions regarding both learning modalities. Survey questions were constructed according to the results of that pilot study. Results: Students preferred face-to-face to online learning (47% vs. 14%). Half of the respondents (45%) were equally motivated by face-to-face and online learning. Face-to-face learning was the most effective learning method (45%). Conclusion: Students gained more knowledge from face-to-face learning than online learning but concentrated well in both online and face-to-face classes. Face-to-face learning was the most popular teaching method and will continue to be in the near future, especially in Health Science Professions 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.

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)0.015
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.010
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0000.001
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 head0.118
GPT teacher head0.552
Teacher spread0.434 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

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