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Record W2777401606 · doi:10.5430/wje.v7n6p63

Formative Value of an Active Learning Strategy: Technology Based Think-Pair-Share in an EFL Writing Classroom

2017· article· en· W2777401606 on OpenAlexvenueno aff
Cavide Demirci, Halil Duzenli

Bibliographic record

VenueWorld Journal of Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
FundersAnadolu Üniversitesi
KeywordsFormative assessmentRubricPsychologyMathematics educationCoding (social sciences)Grading (engineering)Qualitative researchPedagogyMedical education

Abstract

fetched live from OpenAlex

Think-Pair-Share (TPS) activities in classrooms provide an opportunity for students to revise, practice and reproducepreviously learned knowledge. Teachers also benefit from this active learning strategy by exploiting new learningmaterials, saving time by minimizing presentations and using it as a formative assessment tool. This article exploreshow a teacher can employ the strategy to both promote active learning and conduct formative assessment in atime-efficient way. To do this, a TPS activity was designed on an online platform along with an assessment rubric forstudent products. In 60 minutes, students thought individually on the topic provided, discussed and collaborated ingroups and finally wrote down their paragraphs on the online tool. Each group shared paragraphs simultaneously.The teacher examined the paragraphs in terms of the predefined learning outcomes and determined the points to berevised. The students answered an open-ended online questionnaire a day later and the qualitative data were analyzedthrough a coding system. The assessment results successfully showed the learning points to be revisited and theresults of the questionnaire supported the assessments of the teacher. The majority of the students revealed that theywere satisfied and willing to do the activity again in the future.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.034
GPT teacher head0.402
Teacher spread0.368 · 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 designObservational
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

Citations16
Published2017
Admission routes1
Has abstractyes

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