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Record W2559480382 · doi:10.5539/ijel.v6n7p99

The Impact of Student-Based Instruction on Improving IBT TOEFL Scores of Iranian Students

2016· article· en· W2559480382 on OpenAlexvenueno aff
Seyed Ehsan Afsahi, Reza Biria

Bibliographic record

VenueInternational Journal of English Linguistics · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsTest of English as a Foreign LanguageClass (philosophy)PsychologyMathematics educationTest (biology)Significant differenceQualitative researchControl (management)Qualitative propertyMedical educationEnglish languageComputer scienceMedicineSociology

Abstract

fetched live from OpenAlex

<p>Student-based instruction is a form of collaborative education that students play an active role in learning the process. Student’s activity is in group forms. The teacher is presenting in class just to answer students’ questions. The paradigm and pattern shifting away from teaching to an emphasis on learning have persuaded power to be changed and moved from the teacher to the student. Being a qualitative research paper, it is an attempt to investigate the impact of student-based instruction on improving IBT scores of Iranian students. The participants of this study will include two groups of male and female students each consisting of 15, all of whom are enrolling in TOEFL classes in Kish English Institute in Tehran. The participants will be assigned to control (N=15) and experimental (N=15) groups. The participants in the experimental and control groups exposed to the same content, but a different instructional method. The participants are 30 students in Advance level in one class, male and female, aged 25-35 years old. SPSS and Independent T-test are used to measure hypothesis of research and to analyze data, respectively. According to the results of this investigation, student-based instruction significantly affects IBT TOEFL scores of students.</p>

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)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.024
GPT teacher head0.390
Teacher spread0.366 · 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 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

Citations2
Published2016
Admission routes1
Has abstractyes

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