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Record W3044639365 · doi:10.5539/ies.v13n8p16

The Outcomes of Constructivist Learnıng Envıroments from the Perspectives of Secondary School Students

2020· article· en· W3044639365 on OpenAlexvenueno aff
Salih Uslu, Melek Körükçü

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsConstructivist teaching methodsPsychologyPerceptionMathematics educationChristian ministryAcademic achievementEducational attainmentAcademic yearPedagogyLearning environmentTeaching method

Abstract

fetched live from OpenAlex

Constructivist learning environments are those in which individuals absorb knowledge by conducting in-depth research and analysis. In these environments, the individuals are aware of why and how to learn the information, realize their mistakes by testing the knowledge they have learned before and reach new information by correcting these mistakes. The purpose of this research is to determine the secondary school students’ levels of perception about constructivist learning environments in terms of different variables (gender, access to a suitable place to study, grade level, and mother and father educational attainment). The research was held in the central district of a province in the Central Anatolia Region in the spring semester of the 2018-2019 academic year. The study group of the research, selected on voluntary basis with simple random method, consists of 205 students; 100 male and 105 female, who continue their education in the 6th, 7th and 8th grades of a secondary school affiliated to the Ministry of National Education. The results of the research revealed that students have a moderate constructivist learning environment perception. It was found that there was no statistically significant difference in their perceptions in terms of gender and grade level.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.096
GPT teacher head0.396
Teacher spread0.300 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes1
Has abstractyes

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