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Record W4321438123 · doi:10.19173/irrodl.v24i1.6684

An Online Physics Laboratory Delivered Through Live Broadcasting Media: A COVID-19 Teaching Experience

2023· article· en· W4321438123 on OpenAlexvenueno aff
Bayu Setiaji, Purwoko Haryadi Santoso

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicTechnology-Enhanced Education Studies
Canadian institutionsnot available
FundersNuclear PhysicsUniversitas Negeri Yogyakarta
KeywordsPhysics educationSession (web analytics)Coronavirus disease 2019 (COVID-19)Mathematics educationMultimediaPsychologyComputer scienceWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has constituted a sudden educational transformation around the world. It has disrupted instructors, including physics educators, forcing them to adjust to remote teaching. The hands-on laboratory, one of the components of physics instruction, has also had to rapidly go online in all branches of this science, including nuclear physics. In this study, live broadcasting media was designed to conduct a remote nuclear physics laboratory. We then evaluated the immediate impact of this new mode of lab instruction on students’ learning and attitude toward this type of instruction. Fifty-nine 3rd-year physics students at a public university in Indonesia participated in this study. The effectiveness of instruction was examined by analyzing both weekly reports and open-ended responses about students’ learning experiences. In summary, it was evident that live broadcasting media was an effective way to conduct an online nuclear physics laboratory. Accordingly, students’ attitudes demonstrated constructive behaviors about their remote laboratorial experiences. Our findings imply that online platforms are one way to offer the physics laboratory during unanticipated transitions such as the COVID-19 pandemic. Students’ preference for a hands-on laboratory and the technical issues reported during the broadcasting session should be further examined to help design a remote nuclear physics laboratory that is even more accessible and enjoyable.

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)0.006
metaresearch head score (Gemma)0.028
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.283
GPT teacher head0.564
Teacher spread0.281 · 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 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

Citations3
Published2023
Admission routes1
Has abstractyes

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