Computerized Adaptive Test (CAT) Applications and Item Response Theory Models for Polytomous Items

2017 ◽  
pp. 475-475 ◽  
Author(s):  
Eren Can Aybek ◽  
R. Nukhet Demirtasli
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Response Theory ◽  
Polytomous Items ◽  
Computerized Adaptive Test ◽  
Adaptive Test ◽  
Item Response Theory Models

scholarly journals An Extension of Testlet-Based Equating to the Polytomous Testlet Response Theory Model

2022 ◽  
Vol 12 ◽  
Author(s):  
Feifei Huang ◽  
Zhe Li ◽  
Ying Liu ◽  
Jingan Su ◽  
Li Yin ◽  
...  
Keyword(s):  
Item Response Theory ◽  
Sample Size ◽  
Item Response ◽  
Theory Model ◽  
Response Theory ◽  
Polytomous Items ◽  
Testlet Response Theory ◽  
Accurate Performance ◽  
The Impact ◽  
Item Response Theory Models

Educational assessments tests are often constructed using testlets because of the flexibility to test various aspects of the cognitive activities and broad content sampling. However, the violation of the local item independence assumption is inevitable when tests are built using testlet items. In this study, simulations are conducted to evaluate the performance of item response theory models and testlet response theory models for both the dichotomous and polytomous items in the context of equating tests composed of testlets. We also examine the impact of testlet effect, length of testlet items, and sample size on estimating item and person parameters. The results show that more accurate performance of testlet response theory models over item response theory models was consistently observed across the studies, which supports the benefits of using the testlet response theory models in equating for tests composed of testlets. Further, results of the study indicate that when sample size is large, item response theory models performed similarly to testlet response theory models across all studies.

scholarly journals PERBANDINGAN PENERAPAN ALGORITMA TEORI RESPON BUTIR DAN FUZZY TSUKAMOTO PADA COMPUTERIZED ADAPTIVE TEST

2020 ◽  
Vol 12 (2) ◽  
pp. 38
Author(s):  
Wahyuni - Wahyuni ◽  
Muhammad Fahmi
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Response Theory ◽  
Computerized Adaptive Test ◽  
Adaptive Test

Pada dasarnya sistem CAT (Computerized Adaptive Test)  hampir sama dengan sistem CBT (Computerized Based Testing) dimana test dilakukan melalui komputer, namun sistem CAT dapat merandom dan memberikan soal sesuai dengan kemampuan pesertanya. Jika peserta tes tersebut memiliki kemampuan rendah, maka sistem akan memberikan soal yang cenderung mudah. Sebaliknya, jika peserta tes memiliki kemampuan tinggi, maka sistem akan memberikan soal yang cenderung sulit.Penelitian ini bertujuan untuk mengetahui metode manakah yang paling tepat untuk diterapkan pada sistem Computerized Adaptive Test dalam pemberian butir soal kepada peserta tes sehingga menghasilkan hasil yang maksimal. Pemilihan butir soal dimulai dari  butir soal kedua sampai soal yang terakhir. Metode Teori Respon Butir atau Item Response Theory (IRT)pada penelitian ini menggunakan tiga parameter logistik (3PL). Metode analisis yang dilakukan berupa pengujian kepada responden secara manual, menggunakan sistem CAT dengan metode IRT dan menggunakan sistem CAT dengan metode Fuzzy Tsukamoto. Setelah dilakukan pengujian, maka dibandingkan hasil yang telah didapat dari masing-masing pengujian tersebut untuk mengetahui metode manakah yang paling baik untuk diterapkan pada sistem CAT ini. Hasil yang di dapat adalah metode IRT dan Fuzzy  sama-sama dapat diterapkan dalam sistem CAT, namun metode IRT lah yang lebih baik untuk diterapkan dibandingkan dengan metode Fuzzy Tsukamoto.

scholarly journals DESIGNING OF COMPUTERIZED ADAPTIVE TESTS IN THE ABSENCE OF TESTING STATISTICS

2019 ◽  
Vol 73 (5) ◽  
pp. 101-115
Author(s):  
Viktor E. Bondarenko
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Testing Time ◽  
Knowledge Level ◽  
Response Theory ◽  
Computerized Adaptive Test ◽  
Adaptive Test ◽  
Record Statistics ◽  
Test Constructor ◽  
Decision Tables

A Computerized Adaptive Test proposes items according to the student's knowledge level. Therefore, the number of items, which are given to students, is reduced. Besides, the ending of such test is determined by the student's knowledge level, which allows an instructor to reduce testing time. As usual, construction of such tests is based on the Item Response Theory (IRT). This theory gives models which use statistical data about the student's knowledge level and difficulty of items. We do not have such statistics for new tests. In such cases, this paper proposes to estimate the complexity of items on the basis of the experts' conclusions. These conclusions are based on the analytic hierarchy process (AHP) which was modified. The modification allows experts to estimate the complexity of items with the help of the collection of the items characteristics. This modification can remove the expert's inadequate estimates of items or their characteristics. This method allows experts to classify all items in clusters according to their complexity in the first stage of the testing when statistics of items use is absent. A test constructor, on the basis of a decision tables network, realizes the algorithm of the items' selection from different clusters. In the future, tutors will have tested a sufficient number of students' groups. They record statistics of the test using. A test constructor receives such statistics, which will allow them to use the models of the Item Response Theory for estimation of the test items' complexity. The assessment of the knowledge level of students is made with the help of an adaptive test, which is based on a network of decision tables. This network determines the algorithm of using items from different clusters for the testing. The adaptive test is built on the basis of the network of decision tables as a computer system. This system is constructed on the Java platform with the help of the programming environment Android Studio. It has the interface suitable for students as well as for a constructor, which allows the constructor to change the algorithm of using items if received statistics of items use shows such necessity.

Using Item Response Theory Models to Evaluate the Practice Environment Scale

2014 ◽  
Vol 22 (2) ◽  
pp. 323-341 ◽  
Author(s):  
Dheeraj Raju ◽  
Xiaogang Su ◽  
Patricia A. Patrician
Keyword(s):  
Item Response Theory ◽  
Item Response ◽  
Information Criterion ◽  
Partial Credit Model ◽  
Practice Environment ◽  
Partial Credit ◽  
Response Theory ◽  
Environment Scale ◽  
Graded Response ◽  
Item Response Theory Models

Background and Purpose: The purpose of this article is to introduce different types of item response theory models and to demonstrate their usefulness by evaluating the Practice Environment Scale. Methods: Item response theory models such as constrained and unconstrained graded response model, partial credit model, Rasch model, and one-parameter logistic model are demonstrated. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) indices are used as model selection criterion. Results: The unconstrained graded response and partial credit models indicated the best fit for the data. Almost all items in the instrument performed well. Conclusions: Although most of the items strongly measure the construct, there are a few items that could be eliminated without substantially altering the instrument. The analysis revealed that the instrument may function differently when administered to different unit types.

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