Adaptive testing: Conquering process variations

2012 ◽  
Author(s):  
Ender Yilmaz ◽  
Sule Ozev ◽  
Ozgur Sinanoglu ◽  
Peter Maxwell
Keyword(s):  
Process Variations ◽  
Adaptive Testing

Benefits from Computerized Adaptive Testing as Seen in Simulation Studies

1999 ◽  
Vol 15 (2) ◽  
pp. 91-98 ◽  
Author(s):  
Lutz F. Hornke
Keyword(s):  
Measurement Error ◽  
Computerized Adaptive Testing ◽  
Test Procedure ◽  
Adaptive Testing ◽  
Parameter Estimates ◽  
Simulation Studies ◽  
Computerized Adaptive Test ◽  
Item Banks ◽  
Item Parameters ◽  
General Reliability

Summary: Item parameters for several hundreds of items were estimated based on empirical data from several thousands of subjects. The logistic one-parameter (1PL) and two-parameter (2PL) model estimates were evaluated. However, model fit showed that only a subset of items complied sufficiently, so that the remaining ones were assembled in well-fitting item banks. In several simulation studies 5000 simulated responses were generated in accordance with a computerized adaptive test procedure along with person parameters. A general reliability of .80 or a standard error of measurement of .44 was used as a stopping rule to end CAT testing. We also recorded how often each item was used by all simulees. Person-parameter estimates based on CAT correlated higher than .90 with true values simulated. For all 1PL fitting item banks most simulees used more than 20 items but less than 30 items to reach the pre-set level of measurement error. However, testing based on item banks that complied to the 2PL revealed that, on average, only 10 items were sufficient to end testing at the same measurement error level. Both clearly demonstrate the precision and economy of computerized adaptive testing. Empirical evaluations from everyday uses will show whether these trends will hold up in practice. If so, CAT will become possible and reasonable with some 150 well-calibrated 2PL items.

Methods for Restricting Maximum Exposure Rate in Computerized Adaptative Testing

Methodology ◽  
2007 ◽  
Vol 3 (1) ◽  
pp. 14-23 ◽  
Author(s):  
Juan Ramon Barrada ◽  
Julio Olea ◽  
Vicente Ponsoda
Keyword(s):  
Measurement Accuracy ◽  
Computerized Adaptive Testing ◽  
Computation Time ◽  
Adaptive Testing ◽  
Exposure Rate ◽  
Control Parameters ◽  
The Impact ◽  
Two Alternatives ◽  
Selection Of ◽  
Maximum Exposure

Abstract. The Sympson-Hetter (1985) method provides a means of controlling maximum exposure rate of items in Computerized Adaptive Testing. Through a series of simulations, control parameters are set that mark the probability of administration of an item on being selected. This method presents two main problems: it requires a long computation time for calculating the parameters and the maximum exposure rate is slightly above the fixed limit. Van der Linden (2003) presented two alternatives which appear to solve both of the problems. The impact of these methods in the measurement accuracy has not been tested yet. We show how these methods over-restrict the exposure of some highly discriminating items and, thus, the accuracy is decreased. It also shown that, when the desired maximum exposure rate is near the minimum possible value, these methods offer an empirical maximum exposure rate clearly above the goal. A new method, based on the initial estimation of the probability of administration and the probability of selection of the items with the restricted method ( Revuelta & Ponsoda, 1998 ), is presented in this paper. It can be used with the Sympson-Hetter method and with the two van der Linden's methods. This option, when used with Sympson-Hetter, speeds the convergence of the control parameters without decreasing the accuracy.

MARKOV MATHEMATICAL MODEL OF DYNAMIC ADAPTIVE TESTING OF AN ACTIVE AGENT

2018 ◽  
pp. 29-35
Author(s):  
N. V. Brovka ◽  
P. P. Dyachuk ◽  
M. V. Noskov ◽  
I. P. Peregudova
Keyword(s):  
Mathematical Model ◽  
Finite State Machine ◽  
Active Agent ◽  
Adaptive Testing ◽  
State Machine ◽  
Learning Activities ◽  
Independent Learning ◽  
Complex Object ◽  
Total Reward ◽  
Finite State

The problem and the goal.The urgency of the problem of mathematical description of dynamic adaptive testing is due to the need to diagnose the cognitive abilities of students for independent learning activities. The goal of the article is to develop a Markov mathematical model of the interaction of an active agent (AA) with the Liquidator state machine, canceling incorrect actions, which will allow mathematically describe dynamic adaptive testing with an estimated feedback.The research methodologyconsists of an analysis of the results of research by domestic and foreign scientists on dynamic adaptive testing in education, namely: an activity approach that implements AA developmental problem-solving training; organizational and technological approach to managing the actions of AA in terms of evaluative feedback; Markow’s theory of cement and reinforcement learning.Results.On the basis of the theory of Markov processes, a Markov mathematical model of the interaction of an active agent with a finite state machine, canceling incorrect actions, was developed. This allows you to develop a model for diagnosing the procedural characteristics of students ‘learning activities, including: building axiograms of total reward for students’ actions; probability distribution of states of the solution of the problem of identifying elements of the structure of a complex object calculate the number of AA actions required to achieve the target state depending on the number of elements that need to be identified; construct a scatter plot of active agents by target states in space (R, k), where R is the total reward AA, k is the number of actions performed.Conclusion.Markov’s mathematical model of the interaction of an active agent with a finite state machine, canceling wrong actions allows you to design dynamic adaptive tests and diagnostics of changes in the procedural characteristics of educational activities. The results and conclusions allow to formulate the principles of dynamic adaptive testing based on the estimated feedback.

Stochastic Non-homogeneous Arnoldi Method for Analysis of On-Chip Power Grid Networks under Process Variations

2011 ◽  
Vol E94-C (4) ◽  
pp. 504-510
Author(s):  
Zhihua GUI ◽  
Fan YANG ◽  
Xuan ZENG
Keyword(s):  
Power Grid ◽  
Process Variations ◽  
Arnoldi Method ◽  
Grid Networks ◽  
On Chip ◽  
Power Grid Networks

Pseudo-Dynamic Fault Isolation Technique Using Backside Emission Microscopy – Case Study, Where All Other Methods Fail

2002 ◽  
Author(s):  
Yoav Weizman ◽  
Ezra Baruch ◽  
Michael Zimin
Keyword(s):  
Failure Analysis ◽  
Process Variations ◽  
Fault Isolation ◽  
Operating Conditions ◽  
Detection Sensitivity ◽  
Isolation Technique ◽  
Root Cause ◽  
Emission Microscopy ◽  
Failure Location ◽  
Noisy Background

Abstract Emission microscopy is usually implemented for static operating conditions of the DUT. Under dynamic operation it is nearly impossible to identify a failure out of the noisy background. In this paper we describe a simple technique that could be used in cases where the temporal location of the failure was identified however the physical location is not known or partially known. The technique was originally introduced to investigate IDDq failures (1) in order to investigate timing related issues with automated tester equipment. Ishii et al (2) improved the technique and coupled an emission microscope to the tester for functional failure analysis of DRAMs and logic LSIs. Using consecutive step-by-step tester halting coupled to a sensitive emission microscope, one is able detect the failure while it occurs. We will describe a failure analysis case in which marginal design and process variations combined to create contention at certain logic states. Since the failure occurred arbitrarily, the use of the traditional LVP, that requires a stable failure, misled the analysts. Furthermore, even if we used advanced tools as PICA, which was actually designed to locate such failures, we believe that there would have been little chance of observing the failure since the failure appeared only below 1.3V where the PICA tool has diminished photon detection sensitivity. For this case the step-by-step halting technique helped to isolate the failure location after a short round of measurements. With the use of logic simulations, the root cause of the failure was clear once the failing gate was known.

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