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Record W2999927775 · doi:10.5539/jel.v9n1p119

Using Nearpod as a Tool to Promote Active Learning in Higher Education in a BYOD Learning Environment

2020· article· en· W2999927775 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Education and Learning · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsAffordancePsychologyActive learning (machine learning)Mathematics educationEducational technologyHigher educationTeaching methodLearning environmentThe artsBlended learningPedagogyComputer science

Abstract

fetched live from OpenAlex

The present study aims to explore Nearpod as a tool to promote active learning in higher education. In this study, Nearpod is regarded as a tool that can be used to enhance teaching and learning for those lectures provided by male instructors to female students at Sharoura College of Science and Arts, Najran University. Hence, the Nearpod is integrated with video-conference learning system which is used as distance learning system to provide lectures by male lecturers to female students who study at a separate campus. Consequently, students’ own devices have been used to fulfil learning activities during classes. The author utilized the mainly quantitative research method and designed an electronic learning questionnaire applied to (74) female students. The findings of the study showed that the affordances of Nearpod and the BYOD model have promoted active learning in the classroom. Students were very satisfied with integrated learning environment, and they commended Nearpod in all courses specially those ones taught by video-conference learning system.

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)0.002
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.597
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.360
Teacher spread0.317 · 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