Dynamic and Comprehensive Item Selection Strategies for Computerized Adaptive Testing Based on Graded Response Model

2013 ◽  
Vol 44 (3) ◽  
pp. 400-412 ◽  
Author(s):  
Fen LUO ◽  
Shu-Liang DING ◽  
Xiao-Qing WANG
Keyword(s):  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Item Selection ◽  
Response Model ◽  
Graded Response Model ◽  
Selection Strategies ◽  
Graded Response

scholarly journals Efficient and precise Ultra-QuickDASH scale measuring lymphedema impact developed using computerized adaptive testing

2021 ◽  
Author(s):  
Cai Xu ◽  
Mark V. Schaverien ◽  
Joani M. Christensen ◽  
Chris J. Sidey-Gibbons
Keyword(s):  
Factor Analysis ◽  
Computerized Adaptive Testing ◽  
Daily Activities ◽  
Response Model ◽  
Factor Scores ◽  
Graded Response Model ◽  
Local Independence ◽  
Irt Model ◽  
Graded Response ◽  
Item Responses

Abstract Purpose This study aimed to evaluate and improve the accuracy and efficiency of the QuickDASH for use in assessment of limb function in patients with upper extremity lymphedema using modern psychometric techniques. Method We conducted confirmative factor analysis (CFA) and Mokken analysis to examine the assumption of unidimensionality for IRT model on data from 285 patients who completed the QuickDASH, and then fit the data to Samejima’s graded response model (GRM) and assessed the assumption of local independence of items and calibrated the item responses for CAT simulation. Results Initial CFA and Mokken analyses demonstrated good scalability of items and unidimensionality. However, the local independence of items assumption was violated between items 9 (severity of pain) and 11 (sleeping difficulty due to pain) (Yen’s Q3 = 0.46) and disordered thresholds were evident for item 5 (cutting food). After addressing these breaches of assumptions, the re-analyzed GRM with the remaining 10 items achieved an improved fit. Simulation of CAT administration demonstrated a high correlation between scores on the CAT and the QuickDash (r = 0.98). Items 2 (doing heavy chores) and 8 (limiting work or daily activities) were the most frequently used. The correlation among factor scores derived from the QuickDASH version with 11 items and the Ultra-QuickDASH version with items 2 and 8 was as high as 0.91. Conclusion By administering just these two best performing QuickDash items we can obtain estimates that are very similar to those obtained from the full-length QuickDash without the need for CAT technology.

scholarly journals Item Selection Methods in Multidimensional Computerized Adaptive Testing With Polytomously Scored Items

2018 ◽  
Vol 42 (8) ◽  
pp. 677-694 ◽  
Author(s):  
Dongbo Tu ◽  
Yuting Han ◽  
Yan Cai ◽  
Xuliang Gao
Keyword(s):  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Entropy Method ◽  
Two Dimensions ◽  
Item Selection ◽  
Estimation Accuracy ◽  
Selection Methods ◽  
Item Exposure ◽  
Selection Strategies ◽  
Selection Algorithms

Multidimensional computerized adaptive testing (MCAT) has been developed over the past decades, and most of them can only deal with dichotomously scored items. However, polytomously scored items have been broadly used in a variety of tests for their advantages of providing more information and testing complicated abilities and skills. The purpose of this study is to discuss the item selection algorithms used in MCAT with polytomously scored items (PMCAT). Several promising item selection algorithms used in MCAT are extended to PMCAT, and two new item selection methods are proposed to improve the existing selection strategies. Two simulation studies are conducted to demonstrate the feasibility of the extended and proposed methods. The simulation results show that most of the extended item selection methods for PMCAT are feasible and the new proposed item selection methods perform well. Combined with the security of the pool, when two dimensions are considered (Study 1), the proposed modified continuous entropy method (MCEM) is the ideal of all in that it gains the lowest item exposure rate and has a relatively high accuracy. As for high dimensions (Study 2), results show that mutual information (MUI) and MCEM keep relatively high estimation accuracy, and the item exposure rates decrease as the correlation increases.

scholarly journals Alternative item selection strategies for improving test security in computerized adaptive testing of the algorithm

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Iwan Suhardi
Keyword(s):  
Computerized Adaptive Testing ◽  
Adaptive Testing ◽  
Item Selection ◽  
Information Function ◽  
Step Size ◽  
Maximum Likeli ◽  
Test Security ◽  
Item Exposure ◽  
Selection Strategies ◽  
Size Model

Salah satu metode estimasi kemampuan  yang banyak diaplikasikan pada algoritma Computerized Adaptive Testing (CAT) adalah Maximum Likeli-hood Estimation (MLE).  Metode MLE mempunyai kekurangan yaitu ketidakmampuan menemukan solusi estimasi kemampuan peserta tes ketika skor peserta masih belum berpola. Bila ada peserta tes yang memperoleh skor 0 atau skor sempurna, maka untuk menentukan estimasi kemampuan peserta tes umumnya menggunakan model step size. Namun, model step-size tersebut mengakibatkan item exposure. Item exposure merupakan fenomena dimana butir-butir soal tertentu akan lebih sering muncul dibandingkan dengan butir-butir soal yang lain. Hal tersebut membuat tes menjadi tidak aman karena butir-butir soal yang sering muncul akan lebih mudah pula untuk dikenali. Kajian ini mencoba memberikan alternatif strategi dengan cara memodifikasi model step-size dan dilanjutkan dengan merandom hasil perhitungan fungsi informasi yang diperoleh. Berdasarkan hasil kajian didapatkan bahwa alternatif strategi pemilihan butir soal ini mampu menghasilkan kemunculan butir soal yang lebih bervariasi sehingga dapat meningkatkan keamanan tes pada CAT.Kata kunci: item exposure, step-size, adaptive testing AbstractOne method of capability estimation that is widely applied to the Computerized Adaptive Testing (CAT) algorithm is Maximum Likeli-hood Estimation (MLE). The MLE method has the disadvantage of being unable to find a solution to the test taker's ability when the participant's score is not patterned. If there are test takers who get a score of 0 or perfect score, then to determine the ability of the test takers to generally use the step size model. However, the step-size model results in exposure items. The exposure item is a phenomenon where certain items will appear more often than other items. This makes the test insecure because items that often appear will be easier to recognize. This study tries to provide an alternative strategy by modifying the step-size model and proceed by randomizing the results of the calculation of the information function obtained. Based on the results of the study, it was found that alternative item selection strategies were able to produce the appearance of more varied items so as to improve the safety of tests on the CAT.

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