Evaluation of Pre-assessment Method on Improving Students Performance in Complex Analysis Course
Bibliographic record
Abstract
Engineering mathematics has always been the fundamental and important courses in engineering curriculum. Engineering students are required to understand the fundamental of mathematics and apply this knowledge to solve real world problem. The requirement for engineering mathematics for the different branches of engineering is more or less the same at the first and second year level but tend to be more specific and complicated at the later years. Problem started to occur when students lack the fundamental knowledge of mathematics and unable to grasp the higher level of mathematics. One such course is Complex Analysis, which is one of the compulsory course for the third year electrical engineering students at the University Kebangsaan Malaysia, UKM. The course requires the students to be able to understand, analyse and apply the complex concepts and techniques in solving practical electrical engineering problem. However, previous results for different batch of students revealed that most students taking the course had fundamental problem in understanding the new concept although such concepts were built upon on the fundamental mathematics learned in the first year. Thus the objective of this study is to improve student’s performance by assessing systematically student’s level of understanding on a particular topic in the complex analysis course using the Rasch measurement technique. Students were given pre-midsem test with the combination of different question related to the learning outcomes of the course. The result of the pre-midsem test were then analysed using the Rasch measurement and the correlation level between the performance of each student and question was identified..Rasch measurement was able evaluate the validity of the intended question given and classifying the students according to their level of understanding on the course.. Preliminary findings indicated that majority of the students have problems with contour integral especially the use of the Cauchy-Goursat theorem in solving application problem. With this early identification, the existing method of teaching on the particular topics needs to be adjusted and re-evaluated. From this study, some suggestions were put toward for future improvement in the teaching and learning of the Complex Analysis course.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".