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Full-Text Articles in Education
Adaptive Testing For Psychological Assessment: How Many Items Are Enough To Run An Adaptive Testing Algorithm?, Michaela Wagner-Menghin, Geoff Masters
Adaptive Testing For Psychological Assessment: How Many Items Are Enough To Run An Adaptive Testing Algorithm?, Michaela Wagner-Menghin, Geoff Masters
Prof Geoff Masters AO
Although the principles of adaptive testing were established in the psychometric literature many years ago (e.g., Weiss, 1977), and practice of adaptive testing is established in educational assessment, it is not yet widespread in psychological assessment. One obstacle to adaptive psychological testing is a lack of clarity about the necessary number of items to run an adaptive algorithm. The study explores the relationship between item bank size, test length and measurement precision. Simulated adaptive test runs (allowing a maximum of 30 items per person) out of an item bank with 10 items per ability level (covering .5 logits, 150 items …
Adaptive Estimated Maximum-Entropy Distribution Model, L Tan, D Taniar
Adaptive Estimated Maximum-Entropy Distribution Model, L Tan, D Taniar
Dr Ling Tan
The Estimation of Distribution Algorithm (EDA) model is an optimization procedure through learning and sampling a conditional probabilistic function. The use of conditional density function permits multivariate dependency modelling, which is not captured in a population-based representation, like the classical Genetic Algorithms. The Gaussian model is a simple and widely used model for density estimation. However, an assumption of normality is not realistic for many real-life problems. Alternatively, the maximum-entropy model can be used, which makes no assumption of a normal distribution. One disadvantage of the maximum-entropy model is the learning cost of its parameters. This paper proposes an Adaptive …
Parametric Optimization In Data Mining Incorporated With Ga-Based Search, L Tan, D Taniar, K Smith
Parametric Optimization In Data Mining Incorporated With Ga-Based Search, L Tan, D Taniar, K Smith
Dr Ling Tan
A number of parameters must be specified for a data-mining algorithm. Default values of these parameters are given and generally accepted as ‘good’ estimates for any data set. However, data mining models are known to be data dependent, and so are for their parameters. Default values may be good estimates, but they are often not the best parameter values for a particular data set. A tuned set of parameter values is able to produce a data-mining model of better classification and higher prediction accuracy. However parameter search is known to be expensive. This paper investigates GA-based heuristic techniques in a …
Dynamic Task Assignment In Server Farms: Better Performance By Task Grouping, Ling Tan, Z Tari
Dynamic Task Assignment In Server Farms: Better Performance By Task Grouping, Ling Tan, Z Tari
Dr Ling Tan
This paper describes a dynamic load balancing approach to distributed server farm systems. This approach overcomes the interference caused by non-negligible very-large tasks in the heavy-tailed distribution. First, a subset of tasks is allocated proportionally to the processing capability of participating servers by taking into account their remaining processing time. Later, tasks in the servers are processed in order of priority to optimise the system response time. The proposed load balancing algorithm also takes into account the information on server loads to avoid load imbalance caused by very large tasks. The experiments show that the mean waiting time and the …
Multilevel Item Response Models: An Approach To Errors In Variables Regression, Ray Adams, M Wilson, Margaret Wu
Multilevel Item Response Models: An Approach To Errors In Variables Regression, Ray Adams, M Wilson, Margaret Wu
Prof Ray Adams
In this article the authors show how certain analytic problems that arise when one attempts to use latent variables as outcomes in regression analyses can be addressed by taking a multilevel perspective on item response modelling.