MétaCan
Menu
Back to cohort
Record W4317788187 · doi:10.5539/elt.v16n2p35

The Effectiveness of Reciprocal Peer Teaching in an EAP Class

2023· article· en· W4317788187 on OpenAlexvenueno aff
Jie Zhang

Bibliographic record

VenueEnglish Language Teaching · 2023
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTeaching methodClass (philosophy)PerceptionMathematics educationReciprocal teachingPeer learningClass sizeQuality (philosophy)PedagogyComputer science

Abstract

fetched live from OpenAlex

Peer teaching refers to the process of students teaching other students and has been applied in various contexts because of its positive impact on student academic performance as well as its social and cognitive values. Yet, most research on peer teaching tends to be long-term programmes with fixed student tutors and is mostly conducted in the field of science. This research, therefore, aims to examine the effectiveness of reciprocal peer teaching in a 50-minute EAP lesson to undergraduate students in a transnational university in China. Students alter their roles to teach and be taught by their peers on provided materials. Retention of knowledge was tested after 2 weeks to investigate the effectiveness of peer teaching, and a questionnaire-based survey was conducted to gather students’ perceptions of this learning approach. Results show that peer teaching has a positive impact on student retention rate, especially for student tutors. Students’ perception of reciprocal peer teaching is mainly positive but mixed with concerns, particularly regarding unguaranteed teaching quality. Further considerations have been discussed in this regard. Future research is also recommended to expand peer teaching in other teaching contexts and combine peer teaching with other instructional methods to better facilitate learning.

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)0.059
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0590.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
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 head0.027
GPT teacher head0.398
Teacher spread0.371 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueEnglish Language TeachingSame topicInnovative Teaching and Learning MethodsFrench-language works237,207