Open AccessApplication of partial credit models in testing performance assessments for programming course
Author(s) -
Ni Made Sri Mertasari,
I. M. Candiasa
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1810/1/012055
Subject(s) - rasch model , measure (data warehouse) , polytomous rasch model , computer science , rubric , test (biology) , item response theory , process (computing) , machine learning , artificial intelligence , statistics , data mining , mathematics , programming language , arithmetic , psychometrics , paleontology , biology
The assessment of programming skills must be able to measure holistically the theoretical abilities and programming skills. Performance assessment is seen as an appropriate assessment to measure programming ability because it is able to evaluate authentic ability through the process carried out or the product produced. The problem that often arises is performance assessment testing to measure programming capabilities. On this occasion, the Partial Credit Model (PCM) was tried to test performance assessments to measure programming capabilities. PCM is a development of the Rasch model, which applies one parameter, namely the item difficulty index. If the Rasch model is applied to dichotomous items, then PCM is applied to politomic items. PCM assumes that all items have the same difference power. The category score on the PCM shows the per-step score for correctly completing the item according to the scoring rubric developed. The probability of each test taker is estimated by calculating the probability of answering each step in completing an item. There were five items that were tested in two parallel classes in relatively different times. The test results showed a difference in probability in the two classes, but the difference was not too far away. So, the performance assessment was precise enough to measure programming capability, and the test results were quite precise when tested with PCM.