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Record W3159012687

Proposed Flipped Classroom Model for High Schools in Developing Countries

2017· article· en· W3159012687 on OpenAlexaboutno aff
Philip Siaw Kissi, Müesser Nat, Adeleye Idowu

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsnot available
Fundersnot available
KeywordsFlipped classroomCurriculumDeveloping countryClass (philosophy)The InternetMathematics educationComputer scienceChinaPedagogyPsychologyPolitical scienceEconomic growthEconomics
DOInot available

Abstract

fetched live from OpenAlex

Flipped classroom is an approach that uses technology-support instruction to deliver content pre-class in order to maximise student-centered learning and problem-solving skills during class time. The concept is emerging as a feasible approach and is having a positive impact on students learning outcomes and improves information retention. Some developed countries such as United States of America, China, Australia and Canada have implemented this instructional approach to reform their educational system. Despite the positive impact of the flipped classroom instruction, the challenge remains for many high school teachers in developing countries to embrace this new paradigm. This situation raises legitimate concerns that need to be addressed. Therefore, this paper examines the existing literature that offer evidence-based of flipped classroom implementation challenges and proposes a practical alternative model for high schools in the developing countries. The proposed model provides teachers and students who face difficulties concerning internet access, video production, and equipment costs with an easy strategy to adopt flipped classroom instructional method. This study contributes to the high school curriculum development in developing countries to integrate flipped classroom approach and enhance students’ learning experiences.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.645
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.049
GPT teacher head0.388
Teacher spread0.339 · 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 designTheoretical or conceptual
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
Published2017
Admission routes1
Has abstractyes

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