Bireyselleştirilmiş Bilgisayarlı Sınıflama Testi Kriterlerinin Test Etkililiği ve Ölçme Kesinliği Açısından Karşılaştırılması

Year 2018, Volume: 9 Issue: 2, 161 - 177, 30.06.2018

Ceylan Gündeğer , Nuri Doğan

https://doi.org/10.21031/epod.401077

Cited By: 3

Abstract

Bu çalışmada Bireyselleştirilmiş Bilgisayarlı
Sınıflama Testleri’nin (BBST) etkililiğinin sınıflama kriterlerine, madde seçme
ve yetenek kestirim yöntemlerine göre nasıl değiştiğinin belirlenmesi
amaçlanmıştır. Bu amaçla 3 Parametreli Lojistik Model temel alınmış; belirlenen
kesme noktası ve etrafında yüksek bilgi verecek şekilde 500 maddelik bir havuz
oluşturulmuş; birey yetenekleri (N(0,1)) 3000 kişi üzerinden türetilmiş ve
bireylerin madde cevap örüntüleri R yazılımda rasgele türetilmiştir. Sınıflama
kriterlerinden Ardışık Olasılık Oran Testi (AOOT), Genelleştirilmiş Olabilirlik
Oranı (GOO) ve Güven Aralığı (GA) yöntemleri; yetenek kestirim yöntemlerinden
Beklenen Sonsal Dağılım (BSD) ve Ağırlıklandırılmış Olabilirlik Kestirimi (AOK)
yöntemleri; madde seçme yöntemlerinden ise kesme noktasında (KN) ve kestirilen
yetenek (KY) temelinde Maksimum Fisher Bilgisi (MFB) ve Kullback-Leibler
Bilgisi (KLB) yöntemleri çaprazlanarak 48 koşul oluşturulmuştur. R yazılımında
yürütülen BBST simülasyonu sonunda, ortalama test uzunluğu (OTU), ortalama
sınıflama doğruluğu (OSD), bireylerin gerçek yetenek düzeyleri ile kestirilen
yetenek düzeyleri arasındaki korelasyon (r), yanlılık, RMSE ve ortalama mutlak
hata (OMH) değerlerinin 25 tekrara ait ortalamaları hesaplanmıştır. Araştırma
sonuçlarına göre test etkililiği bakımından GOO ve GA yöntemlerinin; ölçme
kesinliği bakımından ise AOOT’nin daha iyi performans gösterdiği; sınıflama
kriterlerinin farksızlık bölgesi genişledikçe veya hata düzeyi değeri
küçüldükçe test etkililiğinin arttığı; sınıflama kriterlerinin tümünün her
koşulda oldukça yüksek düzeyde sınıflama doğruluğuna sahip olduğu
belirlenmiştir. Bireylerin gerçek yetenek düzeyleri ile kestirilen yetenek
düzeyleri arasındaki korelasyon bakımından BSD ve AOK yetenek kestirim
yöntemlerinin her ikisinin de başarılı kestirimlerde bulundukları ancak ölçme
kesinliği bakımından BSD’nin daha iyi performans sergilediği; madde seçme
yöntemlerinin ise tümünün birbirine benzer çalıştığı ancak MFB-KY’nin tüm
bağımlı değişkenler açısından tüm koşullarda daha iyi performans gösterdiği
görülmüştür.

Keywords

bireyselleştirilmiş bilgisayarlı sınıflama testi, sınıflama kriteri, yetenek kestirimi, madde seçme yöntemi, ölçme kesinliği

A Comparison of Computerized Adaptive Classification Test Criteria in Terms of Test Efficiency and Measurement Precision

Abstract

In this study, it was aimed to determine how the efficiency of the Computerized Adaptive Classification Testing (CACT) changes according to classification criteria, item selection and ability estimation methods. For this purpose, a pool of 500 items, which is based on 3 PLM and informs at the arbitrary cut-point and around, has been generated; individual abilities have been generated using normal distribution (N(0,1)) for 3000 individuals and the item response patterns have been generated randomly in R software with the Monte Carlo simulation. As classification criteria, Sequential Probability Ratio Test (SPRT), Generalized Likelihood Ratio (GLR) and Confidence Interval (CI) methods; as ability estimation methods, Expected a Posteriori (EAP) and Weighted Likelihood Estimation (WLE) methods; and as item selection methods, Maximum Fisher Information (MFI) and Kullback-Leibler Information (KLI) methods on the basis of cut-point (CP) and estimated ability (EA) have been crossed and 48 conditions have been investigated. At the end of the CACT simulations in R, the mean values of Average Test Length (ATL), Average Classification Accuracy (ACA), correlation between the true thetas and estimated thetas (r), bias, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) for 25 replications have been calculated. According to the results of the study, it has been observed that the GLR and the CI classification criteria perform better in terms of test efficiency, however the SPRT works better in terms of the measurement precision; test efficiency increases as the indifference region of classification criteria expands or the error value decreases; all classification criteria have considerably high level of the classification accuracy in all conditions. It has been concluded that both ability estimation methods have successful estimation results in terms of the correlation between true and estimated thetas (r); whereas the EAP relatively performs better in terms of the measurement precision; and all of the item selection methods work similarly to each other however the MFI-EA performs better for all conditions in terms of all dependent variables.

Keywords

computerized adaptive classification testing, classification criteria, ability estimation, item selection method, measurement precision

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