The Validation and Development of Electronic Language Test
Bibliographic record
Abstract
The enormous velocity of development and advancement in Information Communication Technology (ICT) to date has remarkably transformed the educational system which incorporates aspects of teaching, knowledge delivery and training. Language testing or measurement at the present time is employing conventional method to measure the academic performance of the students. With the advent of ICT, it has initiated the new innovative approach of assessing student’s performance. The objective of this study is to investigate the effectiveness of online and conventional mode of Malaysian English Competency Test (MECT) among undergraduates of Universiti Kebangsaan Malaysia (UKM), focusing on reading test. The test is intended to test four components of English language; listening, reading, writing and speaking skills. This quantitative research involved a total of 43 students and was randomly divided into two groups. These groups were tested on paper test of Malaysian English Competency Test (MECT) and Electronic Malaysian English Competency Test (E-MECT). Apart from the tests, the respondents were also given a set of questionnaire to obtain the data on their computer literacy level, online reading test, and also knowledge and experience of using E-MECT online test. The results showed that there were no marked differences in terms of student’s performance even though their marks were higher in the online version. Other features such as user friendliness, interface design, interactivity and time management play important role in the success of online test. Recommendations to administer and adapt the online test are put forth at the end of the report. It could be concluded that the study has significant implications for not only the students, but also the teachers and programmer as well. Additionally, this study helps both the students and teachers in taking online language testing in a conducive and convenient way. In fact, this could also sharpen the students’ computer literacy in using the computer. Key words: Language; Information Communication Technology; Online test, Conventional test; Reading skill
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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 | 0.031 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".