Comparison Between Conventional and Digital Essay Writing Assessment System: Consumer Concept and User Friendly
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".