Relationship between Students’ Diagnostic Assessment and Achievement in a Pre-University Mathematics Course
Bibliographic record
Abstract
Educators have always highlighted the importance of mathematics mastery in education for many years. With the current emphasis of Science, Technology, Engineering and Mathematics (STEMs) education, mathematics mastery is even more vital because it supports the learning and mastery of science fields such as engineering and science. Furthermore, in higher education, mathematics is essential because majority of the courses require the use of mathematical concepts in their learning. In recent years, many countries including Malaysia have seen an increase in the enrolment number of tertiary students. The increase in student enrolment has resulted in a population of students with diverse abilities, and this creates challenges for educators in providing instruction to the students. Educators need to detect students’ mathematical ability at an early stage before teaching them new content. One way to gather information about students’ basic mathematics skill is through the use of diagnostic test. Diagnostic test in education is a preliminary assessment mainly used to detect students’ strengths and weaknesses in learning. It allows educators to cater their teaching style and content to suit to the students’ basic knowledge. With researches indicating the importance and benefits of using diagnostic test in various subjects, it is important to further examine the use of diagnostic test in the local context of pre-university studies. This study investigated the relationship between students’ mathematics diagnostic test results and final mathematics examination performances at a public pre-university programme. The samples of the study consisted of 250 pre-university students and the data of the study were collected through a mathematics diagnostic test paper, a questionnaire and a final mathematics examination. The outcomes of the study show that there was a strong positive correlation between mathematics diagnostic test results and students’ mathematics achievement in pre-university.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".