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Record W2151527905 · doi:10.5539/ass.v8n16p134

Evaluation of Pre-assessment Method on Improving Students Performance in Complex Analysis Course

2012· article· en· W2151527905 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsRasch modelMathematics educationGRASPTest (biology)Course (navigation)CurriculumEngineering mathematicsComputer scienceMathematicsEngineeringPsychologyPedagogySoftware engineeringStatistics

Abstract

fetched live from OpenAlex

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.

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.

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)0.003
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 score0.525
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.402
Teacher spread0.374 · 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