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Record W2908132256 · doi:10.5539/ass.v15n1p42

Designing English Material for Second Semester Students of Peer-Assisted Learning Program (PALP)

2018· article· en· W2908132256 on OpenAlexvenueno aff
Ratri Nur Hidayati

Bibliographic record

VenueAsian Social Science · 2018
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsnot available
Fundersnot available
KeywordsRubricLikert scaleComputer scienceMathematics educationSubject (documents)Qualitative propertyData collectionMedical educationPsychologyMathematicsMedicineLibrary science

Abstract

fetched live from OpenAlex

This research aims at developing Peer-Asissted Learning Program (PALP) Modul for PBI UAD Students at Second Semester. Since the purpose of the research is developing an instructional module, this research belongs to research and development. There were some steps conducted in this research which were conducting needs analysis, formulating the instructional design, arranging the modul, validating the modul, revising the modul, applying the modul, and evaluating the use of the modul. Then, the instruments used in this research were interview guideline and questionnaires. The interview guidelines were used in conducting needs analysis while the questionnaires were used in validating and evaluating the modul. Next, there were two types of data gained from this research, qualitative and quantitative data. The qualitative data were analyzed descriptively while quantitative data were analyzed through Likert scale and the use criteria percentage. The subject of the study were 12 of board PALP, 30 PALP mentors, and 50 mentees. The module entitled PALP Module for Semester 2 students which focuses on speaking skill. It contains 7 chapters, each chapters consists of the teaching order and scoring rubric. Moreover, the module was developed and revised based on the data gained during the research. The result from the expert judgment related to the face aspect is 84% while the result related to the development aspect is 80%. Then, the result of the evaluation from the board is 84%, from the mentors is 86%, and from the mentee is 88%. So, it can be concluded that the modul is feasible to apply.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.383
Teacher spread0.346 · 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 designOther design
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

Citations0
Published2018
Admission routes1
Has abstractyes

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