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Record W2937170098 · doi:10.5539/elt.v12n5p73

Problem Based Learning: A Student-Centered Approach

2019· article· en· W2937170098 on OpenAlexvenueno aff
Sheeba Sardar Ali

Bibliographic record

VenueEnglish Language Teaching · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicProblem and Project Based Learning
Canadian institutionsnot available
Fundersnot available
KeywordsProblem-based learningMathematics educationContext (archaeology)PsychologyTeaching methodCritical thinkingLifelong learningCurriculumPedagogyCooperative learningExperiential learningForeign languageActive learning (machine learning)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Problem-based learning is a teaching method in which students’ learn through the complex and open ended problems. These problems are real world problems and are used to encourage students’ learning through principles and concept. PBL is both a teaching method and approach to the curriculum. It can develop critical thinking skill, problem solving abilities, communication skills and lifelong learning. The purpose of this study is to give the general idea of PBL in the context of language learning, as PBL has expanded in the areas of law, education, economics, business, social studies, and engineering. It encourages students to develop skills that can be useful for their future and in practical life within a team environment. For the language classroom in Arab countries, where English is spoken as a foreign language PBL can be very useful. Trained teachers can design problems to meet the needs of the learners, that can be useful to improve English practically in social context.

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)0.006
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0070.003
Open science0.0040.005
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0120.005

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 head0.011
GPT teacher head0.295
Teacher spread0.284 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations225
Published2019
Admission routes1
Has abstractyes

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