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Record W2808735880 · doi:10.5539/elt.v11n7p146

Relooking at the ESL Reading Comprehension Assessment for Malaysian Primary Schools

2018· article· en· W2808735880 on OpenAlexvenueno aff
Chang Kuan Lim, Lin Siew Eng, Abdul Rashid Mohamed, Shaik Abdul Malik Mohamed Ismail

Bibliographic record

VenueEnglish Language Teaching · 2018
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsReading comprehensionMathematics educationPsychologyReading (process)Test (biology)ComprehensionRealmPedagogyComputer scienceLinguistics

Abstract

fetched live from OpenAlex

The purpose of the study is to have a relook at the ESL reading comprehension assessment system for Malaysian Year Five students. Traditionally, the ESL teachers have been assessing and reporting on their primary year’s students by merely giving a composite grade with some vague remarks. This process has been used and is still being employed in spite of the numerous advances and progress that have been made in the realm of education. To gauge the students’ reading ability there is a need to take a serious look into the way teachers assess the students. In this ESL reading comprehension assessment system, a set of standardised generic reading comprehension test, a reading matrix and reading performance descriptors were developed. The findings revealed that Year Five respondents at reading performance Band 1, Band 2, Band 3, Band 4 and Band 5 have acquired the literal, reorganization and inferential reading sub-skills to a certain extent. The results obtained were found to be consistent indicating that the ESL reading assessment is reliable and valid to a large extent as revealed by a second administration of the test conducted in a few other selected primary schools. This ESL reading comprehension system can provide information on students’ reading ability at both the micro and macro levels. At the micro level, ESL teachers can plan their teaching instructions that tailor to the needs of the students. At the macro level, it can assist the district as well as the state education departments in Malaysia to plan reading programmes for primary school students.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.326
Teacher spread0.313 · 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 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

Citations21
Published2018
Admission routes1
Has abstractyes

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