MétaCan
Menu
Back to cohort
Record W2960813101 · doi:10.5430/rwe.v10n2p96

Comparison Between Conventional and Digital Essay Writing Assessment System: Consumer Concept and User Friendly

2019· article· en· W2960813101 on OpenAlexvenueno aff
Adenan Ayob

Bibliographic record

VenueResearch in World Economy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsMalayMathematics educationChristian ministryDescriptive statisticsUser FriendlyData collectionPerspective (graphical)Computer sciencePsychologyMultimediaStatisticsMedical educationMathematicsArtificial intelligencePolitical scienceLinguisticsMedicine

Abstract

fetched live from OpenAlex

Significant changes occurred in education system; teaching and learning technology in this new era. The changes can be revised through the existence of digital assessment system for essay writing. In utilizing and interpreting these changes, this study was conducted to examine the use of digital and conventional assessment system for Form Three among Malay teachers. The survey method was used in this study. The samples of the study are 60 teachers of the national secondary school which taught Malay Language for form three in Selangor and Federal Territory of Kuala Lumpur. The data are described descriptively and inferentially. Descriptive data are mean and standard deviation. Inferential data was analyzed using ANCOVA statistics. The findings show that there is a significant difference in teachers' opinion on the use of digital assessment system and the use of conventional assessment materials that based on consumer concept and user friendly. From that perspective, digital scoring system make teachers more dynamic in scoring the essay writing for form three. Therefore, it is recommended to the Ministry of Education to implement and revise the use of digital assessment system to improve the process for primary and secondary schools.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.316

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.078
GPT teacher head0.449
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; 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

Citations5
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueResearch in World EconomySame topicEducation and Technology IntegrationFrench-language works237,207