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Record W3011600926 · doi:10.5430/ijhe.v9n3p129

Virtual Classroom: To Create A Digital Education System in Bangladesh

2020· article· en· W3011600926 on OpenAlexvenueno aff
Faieza Chowdhury

Bibliographic record

VenueInternational Journal of Higher Education · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsClass (philosophy)Construct (python library)Virtual learning environmentVirtual classroomGovernment (linguistics)Order (exchange)Higher educationComputer scienceLearning ManagementMathematics educationMultimediaKnowledge managementPsychologyBusinessPolitical science

Abstract

fetched live from OpenAlex

The use of web-based tools for educational purposes is a rapidly growing trend in Bangladesh. Most of the academic institutions in Bangladesh have decided to develop academic portals where teachers can create online versions of their courses. This paper addresses students’ opinions on the use of virtual classroom from their own personal experiences and identifies features of virtual classroom that are vital to create an interactive student-centered learning environment. We try to understand whether the use of virtual classroom can bring improvement in students’ learning and performance in the class. The results from binary logistic regression indicate that most of the participants have positive opinions regarding the usage of virtual classroom for learning purposes. As the present Government of Bangladesh (GOB) has urged all higher education institutions (HEIs) to take effective measures in order to implement ‘Integrated University Information Management Platform’, the findings from this study will help educators and administrators to understand how to construct an interactive student-friendly academic portal that will fulfill all the needs of the customers and to assess whether the features of the existing portals that they are currently using need any further improvements.

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)0.000
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.384

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 communication0.0000.001
Open science0.0000.000
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 head0.017
GPT teacher head0.342
Teacher spread0.325 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations28
Published2020
Admission routes1
Has abstractyes

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