Design and Evaluation of an XML-Based Platform-Independent Computerized Adaptive Testing System

Ho, Rong-Guey; Yen, Yung-Chin

doi:10.1109/te.2004.837035

Cited by 18 publications

(6 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The item bank used in this experiment consisted of 84 items, each with four response options. This item bank was suggested in the study of Ho and Yen (2005), with each item drawn from the English Ability Test for college entrance in Taiwan, administered by the College Entrance Examinations Center (CEEC). The properties of the three parameters in the item bank are shown in Table 2.…”

Section: Methodsmentioning

confidence: 99%

An Empirical Evaluation of the Slip Correction in the Four Parameter Logistic Models With Computerized Adaptive Testing

Yen

Laio

et al. 2012

Applied Psychological Measurement

View full text Add to dashboard Cite

In a selected response test, aberrant responses such as careless errors and lucky guesses might cause error in ability estimation because these responses do not actually reflect the knowledge that examinees possess. In a computerized adaptive test (CAT), these aberrant responses could further cause serious estimation error due to dynamic item administration. To enhance the robust performance of CAT against aberrant responses, Barton and Lord proposed the four-parameter logistic (4PL) item response theory (IRT) model. However, most studies relevant to the 4PL IRT model were conducted based on simulation experiments. This study attempts to investigate the performance of the 4PL IRT model as a slip-correction mechanism with an empirical experiment. The results showed that the 4PL IRT model could not only reduce the problematic underestimation of the examinees’ ability introduced by careless mistakes in practical situations but also improve measurement efficiency.

show abstract

Section: Methodsmentioning

confidence: 99%

An Empirical Evaluation of the Slip Correction in the Four Parameter Logistic Models With Computerized Adaptive Testing

Yen

Laio

et al. 2012

Applied Psychological Measurement

View full text Add to dashboard Cite

show abstract

“…On the other hand, IRT has been mainly applied in the Computerized Adaptive Test (CAT) domain for personalized test construction based on individual ability (Chen et al, 2004;Ho and Yen, 2005;Yen and Fitzpatrick, 2006;Meyer and Zhu, 2013). Despite its high degree of support among theoreticians and some practitioners, IRT's complexity and dependence on unidimensional test data and large samples often relegate its application to experimental purposes only.…”

Section: Related Workmentioning

confidence: 99%

LMS assessment: using IRT analysis to detect defective multiple-choice test items

Fotaris

Mastoras

2014

IJTEL

View full text Add to dashboard Cite

Due to the computerization of assessment tests, the use of Item Response Theory (IRT) has become commonplace for educational assessment development, evaluation, and refinement. When used appropriately by a Learning Management System (LMS), IRT can improve the assessment quality, increase the efficiency of the testing process, and provide in-depth descriptions of item properties. This paper introduces a methodological and architectural framework which embeds an IRT analysis tool in an LMS so as to extend its functionality with assessment optimisation support. By applying a set of validity rules to the statistical indices produced by the IRT analysis, the enhanced LMS is able to detect several defective items from an item pool which are then reported for reviewing of their content. Assessment refinement is achieved by repeatedly employing this process until all flawed items are eliminated.

show abstract

“…IRT always comes with Computerized Adaptive Testing (CAT). Adaptive testing is used in computer administrated tests to dynamically estimate the examinee level, such as Graduate Record Examination (GRE) and the Test of English as a Foreign Language (TOEFL) [7]. However, this kind of technique is appropriate for testing, not for detecting problem's quality.…”

Section: Related Workmentioning

confidence: 99%

Applying IRT to Estimate Learning Ability and K-means Clustering in Web based Learning

Chang¹,

Yang²

2009

JSW

View full text Add to dashboard Cite

E-learning provides a convenient and efficient way for learning and enriching people’s lives. But there is no appropriate way to estimate and diagnose people when they learning with e-learning environment/system. For learning ability estimation issue, Item Response Theory which plays an important role in modern mental test theory is applied. Besides, K-means clustering method is also applied to cluster learner’s ability for remedying courses or enhancing courses. We integrate these two theories and propose a combination methodology to solve the estimation and diagnostic issues in e-learning environment. A web-based assist system is provided as well. Experimental data is collected with forty sophomore students studying “Business Data Communication” class at Dept. of Information Management in Chung Hua University in Taiwan. We illustrated the method to observe and estimate the variation of learner’s ability. This methodology and system could make some valuable contribution in e-learning related study and society.

show abstract

Design and Evaluation of an XML-Based Platform-Independent Computerized Adaptive Testing System

Cited by 18 publications

References 3 publications

An Empirical Evaluation of the Slip Correction in the Four Parameter Logistic Models With Computerized Adaptive Testing

An Empirical Evaluation of the Slip Correction in the Four Parameter Logistic Models With Computerized Adaptive Testing

LMS assessment: using IRT analysis to detect defective multiple-choice test items

Applying IRT to Estimate Learning Ability and K-means Clustering in Web based Learning

Contact Info

Product

Resources

About