基于Gini指数的认知诊断计算机化自适应选题策略

Gini指数可用来描述分布的不均匀性,已广泛应用于决策树算法,本文开发了基于Gini指数的认知诊断计算机化自适应选题策略,并在饱和模型和缩减模型下与SHE, MPWKL,GDI,PWKL选题策略进行比较。模拟研究表明,基于Gini指数的选题策略与SHE,MPWKL,GDI相比,分类精度相近并提高了题库的利用率;与PWKL相比,提高了分类的精度和选题速度,综合来看,基于Gini指数的选题策略能够兼顾分类精度和题库使用均匀性。     

Flexible Computerized Adaptive Tests to Detect Misconceptions and Estimate Ability Simultaneously

The Scaling Individuals and Classifying Misconceptions (SICM) model is an advanced psychometric model that can provide feedback to examinees’ misconceptions and a general ability simultaneously. These two types of feedback are represented by a discrete and a continuous latent variable, respectively, in the SICM model. The complex structure of the SICM model brings difficulties in estimating both misconception profile and ability efficiently in a linear test. To overcome this challenge, this study proposes a flexible computerized adaptive test (FCAT) design as a new test delivery method to increase test efficiency by administering an individualized test to examinees. We propose three item selection methods and two transition criteria to determine adaptive steps based on the needs of estimating one or two latent variables. Through two simulation studies, we demonstrate how to select an appropriate item selection method for an adaptive step and what transition criterion should be used between two adaptive steps. Results reveal the combination of the item selection method and the transition criterion could improve the estimation accuracy of a specific latent variable to a different extent and thus provide further guidance in designing an FCAT.     

A VIROCENTRIC PERSPECTIVE ON EVIL

Abstract. The coronavirus pandemic has stirred interest in viruses. This has been accompanied by a proliferation of popular works trying to explain how viruses fit into the Christian worldview. In an anthropocentric perspective, viruses are easily regarded as malicious entities. This article, however, shows that a proper understanding of the biology of viruses actually adds another level of complexity to our perception of good and evil. Interestingly, this additional layer of complexity might help us solve some of the most urgent difficulties in the discussion about good and evil, if we recognize the subjective nature of what we call natural evil. We need to be more nuanced not merely in our theological discussion about good and evil, but also in the way we talk about viruses.     

Human age classification using facial skin aging features and artificial neural network

In this paper a novel method based on facial skin aging features and Artificial Neural Network (ANN) is proposed to classify the human face images into four age groups. The facial skin aging features are extracted by using Local Gabor Binary Pattern Histogram (LGBPH) and wrinkle analysis. The ANN classifier is designed by using two layer feedforward backpropagation neural networks. The proposed age classification framework is trained and tested with face images from PAL face database and shown considerable improvement in the age classification accuracy up to 94.17% and 93.75% for male and female respectively.     

Explaining and Explaining Away in Cosmology and Theology

Modern cosmology raises two significant questions that potentially relate to theology: does the universe have a beginning, and why is the universe so apparently fine tuned for life? In a significant paper, Mark McCartney and David Glass ask whether science can explain away, or at least explain away in part, such features of the universe in cosmology and other sciences that may alternatively invite a theological explanation. In this paper I argue that two proposals made by cosmologists fail to explain away the universe's beginning, and that science is powerless to explain away the more fundamental question as to why there is a universe at all. I argue similarly that scientific, or quasi-scientific, proposals such as the multiverse fail to explain away the fine tuning.     

Invariance Properties for General Diagnostic Classification Models

In item response theory (IRT), the invariance property states that item parameter estimates are independent of the examinee sample, and examinee ability estimates are independent of the test items. While this property has long been established and understood by the measurement community for IRT models, the same cannot be said for diagnostic classification models (DCMs). DCMs are a newer class of psychometric models that are designed to classify examinees according to levels of categorical latent traits. We examined the invariance property for general DCMs using the log-linear cognitive diagnosis model (LCDM) framework. We conducted a simulation study to examine the degree to which theoretical invariance of LCDM classifications and item parameter estimates can be observed under various sample and test characteristics. Results illustrated that LCDM classifications and item parameter estimates show clear invariance when adequate model data fit is present. To demonstrate the implications of this important property, we conducted additional analyses to show that using pre-calibrated tests to classify examinees provided consistent classifications across calibration samples with varying mastery profile distributions and across tests with varying difficulties.     

Slippery Slopes and Collapsing Taboos

A slippery slope argument is an argument to this twofold effect. First, that if a policy or practice P is permitted, then we lack the dialectical resources to demonstrate that a similar policy or practice P* is not permissible. Since P* is indeed not permissible, we should not endorse policy or practice P. At the heart of such arguments is the idea of dialectical impotence, the inability to stop the acceptance of apparently small deviations from a heretofore secure policy or practice from leading to apparently large and unacceptable deviations. Using examples of analogical arguments and sorites arguments I examine this phenomenon in the context of collapsing taboos.     

State-of-the-Art: The Structure of Argumentation

In this paper, a survey is presented of the main approaches to the structure of argumentation. The paper starts with a historical overview of the distinctions between various types of argument structure. Next, the main definitions given in the various approaches are discussed as well as the methods that are proposed to deal with doubtful cases.     

Resisting Explanation

Although explanation is widely regarded as an important concept in the study of rational inquiry, it remains largely unexplored outside the philosophy of science. This, I believe, is not due to oversight as much as to institutional resistance. In analytic philosophy it is basic that epistemic rationality is a function of justification and that justification is a function of argument. Explanation, however, is not argument nor is belief justification its function. I argue here that the task of incorporating explanation into the theory of rational inquiry poses a serious challenge to our basic concept of epistemic rationality as well as the a priori method of inquiry that still lies at the heart of analytic philosophy. Specifically, it pushes us toward a much stronger form of naturalism than is generally thought necessary, one in which argument and explanation are recognized as distinct and equally fundamental cognitive processes whose dynamic relationship is one of the central issues in the theory of rationality.