中学生物理学科问题分类表征层次的研究

以三套分别含有按照解题方法可以分为二类、三类、四类的物理学科问题分类实验材料,对济南市6所中学3个年级766名中学生进行研究,结果表明:不同年级、不同学业水平学生分类表征层次的差异显著,呈现随年级升高而升高的趋势,学优生的水平明显高于学困生,学优生更多地倾向内在分类表征;随着分类任务的类别数量增多,更多学生倾向外在分类表征;不同性别学生的分类表征层次没有显著差异;不同物理学科问题分类表征层次学生的物理成绩之间存在显著差异。     

Limited‐information goodness‐of‐fit testing of diagnostic classification item response models

Despite the growing popularity of diagnostic classification models (e.g., Rupp et al., 2010, Diagnostic measurement: theory, methods, and applications, Guilford Press, New York, NY) in educational and psychological measurement, methods for testing their absolute goodness of fit to real data remain relatively underdeveloped. For tests of reasonable length and for realistic sample size, full‐information test statistics such as Pearson's X² and the likelihood ratio statistic G² suffer from sparseness in the underlying contingency table from which they are computed. Recently, limited‐information fit statistics such as Maydeu‐Olivares and Joe's (2006, Psychometrika, 71, 713) M₂ have been found to be quite useful in testing the overall goodness of fit of item response theory models. In this study, we applied Maydeu‐Olivares and Joe's (2006, Psychometrika, 71, 713) M₂ statistic to diagnostic classification models. Through a series of simulation studies, we found that M₂ is well calibrated across a wide range of diagnostic model structures and was sensitive to certain misspecifications of the item model (e.g., fitting disjunctive models to data generated according to a conjunctive model), errors in the Q‐matrix (adding or omitting paths, omitting a latent variable), and violations of local item independence due to unmodelled testlet effects. On the other hand, M₂ was largely insensitive to misspecifications in the distribution of higher‐order latent dimensions and to the specification of an extraneous attribute. To complement the analyses of the overall model goodness of fit using M₂, we investigated the utility of the Chen and Thissen (1997, J. Educ. Behav. Stat., 22, 265) local dependence statistic $X_{\mathrm{LD}}^2$ for characterizing sources of misfit, an important aspect of model appraisal often overlooked in favour of overall statements. The $X_{\mathrm{LD}}^2$ statistic was found to be slightly conservative (with Type I error rates consistently below the nominal level) but still useful in pinpointing the sources of misfit. Patterns of local dependence arising due to specific model misspecifications are illustrated. Finally, we used the M₂ and $X_{\mathrm{LD}}^2$ statistics to evaluate a diagnostic model fit to data from the Trends in Mathematics and Science Study, drawing upon analyses previously conducted by Lee et al., (2011, IJT, 11, 144).     

Depicting Caribbean Theological Libraries

Theological libraries in the Caribbean have suffered from invisibility, so this study aims to bring these libraries to the fore. It presents key features of the libraries and seeks to determine if these are influenced by the libraries’ denominational affiliation, geographic location, and size. Geographically speaking, the target area was twenty-nine Caribbean territories. Data was gathered using a questionnaire containing mainly open-ended questions in English and Spanish, which was mostly administered face-to-face. The general results are offered first: the number of libraries found, their geographic location—whether in the English, Spanish and Dutch-speaking Caribbean—their denominations, and their size. These results are followed by a more detailed presentation focusing on classification schemes, subject headings, automation, and staffing at the supervisory level. The findings are then analyzed by denomination, location, and size. The article ends with a discussion of the results, comparing theological libraries in the Caribbean with theological libraries in the United States, Asia, and Europe. The study concludes that denomination and location have not affected the findings; but size has to some extent. Also, in some areas, Caribbean theological libraries resemble those in North America, while in others they resemble those in Asia and Europe.     

Military Enlistment Selection and Classification: Moving Forward

This article places the articles included in this special issue within the larger context of the objectives of a selection and classification system. It examines the full range of individual differences and how, until relatively recently, a focus on training success has led to an emphasis on the cognitive subset of these differences within the military. It describes how, consistent with a greater recognition of the importance of job performance, the research described in this issue has opened the door to expanded coverage of both cognitive and noncognitive attributes. It summarizes the methodological advances that have contributed to the efficacy of new noncognitive measures. It explores how popular approaches to the measurement of classification efficiency have led to unwarranted pessimism regarding the classification potential of multiattribute measures and discusses research indicating how much greater classification efficiency is possible with existing measures. Finally, it examines potentially fruitful areas of future research to better meet military objectives. These include development of an approach to performance measurement and validity transportation that makes future military classification research feasible; exploration of currently untapped individual-difference domains, particularly those that might differentially predict across job groupings; exploration of the interaction between cognitive and noncognitive attributes; and exploration of alternative measurement techniques.     

Wild facial expression recognition based on incremental active learning

Facial expression recognition in a wild situation is a challenging problem in computer vision research due to different circumstances, such as pose dissimilarity, age, lighting conditions, occlusions, etc. Numerous methods, such as point tracking, piecewise affine transformation, compact Euclidean space, modified local directional pattern, and dictionary-based component separation have been applied to solve this problem. In this paper, we have proposed a deep learning–based automatic wild facial expression recognition system where we have implemented an incremental active learning framework using the VGG16 model developed by the Visual Geometry Group. We have gathered a large amount of unlabeled facial expression data from Intelligent Technology Lab (ITLab) members at Inha University, Republic of Korea, to train our incremental active learning framework. We have collected these data under five different lighting conditions: good lighting, average lighting, close to the camera, far from the camera, and natural lighting and with seven facial expressions: happy, disgusted, sad, angry, surprised, fear, and neutral. Our facial recognition framework has been adapted from a multi-task cascaded convolutional network detector. Repeating the entire process helps obtain better performance. Our experimental results have demonstrated that incremental active learning improves the starting baseline accuracy from 63% to average 88% on ITLab dataset on wild environment. We also present extensive results on face expression benchmark such as Extended Cohn-Kanade Dataset, as well as ITLab face dataset captured in wild environment and obtained better performance than state-of-the-art approaches.     

The contextually controlled, feature-mediated classification of symbols

The classification of names of people or objects based on the features acquired by the names and the sorting instructions provided is a commonplace occurrence. For example, given the names Renoir, Pollock, James and Voltaire the average adult would be able to classify them differentially based on the instruction to classify them based on vocation or nationality. In general, such a repertoire reflects the reclassification of symbols (i.e, the names of individuals) in terms of contextual cues (instructions to sort by vocation or nationality) and the features acquired by the symbols (the specific nationalities and vocations). The present experiment studied this phenomenon with the use of arbitrary stimuli that did not have clear preexperimental associations. Two of 4 participants classified the symbols into different equivalence classes based on the prevailing contextual cues and the features that had been acquired by the symbols. Using an ABA reversal design we then demonstrated that 1 participant classified the symbols in accordance with the contextual cues and acquired features when present, but not in the absence of the contextual cues. A 3rd participant showed symbol classification that differed from that predicted by the procedures, and the 4th classified the symbols based on one set of features but not on context. These data describe one set of conditions that could account for the establishment of complex classification repertoires that occur in natural settings.     

Cognitive diagnosis models for multiple strategies

Cognitive diagnosis models (CDMs) have been used as psychometric tools in educational assessments to estimate students’ proficiency profiles. However, most CDMs assume that all students adopt the same strategy when approaching problems in an assessment, which may not be the case in practice. This study develops a generalized multiple-strategy CDM for dichotomous response data. The proposed model provides a unified framework to accommodate various condensation rules (e.g., conjunctive, disjunctive, and additive) and different strategy selection approaches (i.e., probability-matching, over-matching, and maximizing). Model parameters are estimated using the marginal maximum likelihood estimation via expectation-maximization algorithm. Simulation studies showed that the parameters of the proposed model can be adequately recovered and that the proposed model was relatively robust to some types of model misspecifications. A set of real data was analysed as well to illustrate the use of the proposed model in practice.     

An efficient cervical disease diagnosis approach using segmented images and cytology reporting

Cervical cancer is the second most common cancer in women globally. A computer aided cervical disease diagnosis system that can relieve pressure on medical experts and save the cost is proposed. To implement our approach in the reality of cervical diseases diagnosis, a multi-modal framework is designed for three kinds of cervical diseases diagnosis that integrates uterine cervix images, Thinprep Cytology Test, human papillomavirus test, and patients’ age. However, too many features increase memory storage costs and computational costs, and it affects the spread of this system in poor areas. Feature selection not only eliminates redundant or irrelevant features but also finds the factors that influence the disease most first is performed in multi-modal frameworks for cervical diseases diagnosis. The detailed process of the method is as follows: first, according the representative color, an efficient image segmentation algorithm is developed; then from three different types of segmented images, we extract color features and texture features for interpreting uterine cervix images; next, Boruta algorithm is applied to feature selection; finally, the performance of Random Forests that utilizes selected features for cervical disease diagnosis is investigated. In the experiment, the proposed multi-modal diagnostic approach gives the final diagnosis for three different kinds of cervical diseases with 83.1% accuracy, which significantly outperforms methods using any single source of information alone. The validation cohort is applied to validate the efficiency of our method, and the performance of random forest obtained by using only 1.2% of features is like or even better than using 100% of features.     

Geometric representation of association between categories

Categories can be counted, rated, or ranked, but they cannot be measured. Likewise, persons or individuals can be counted, rated, or ranked, but they cannot be measured either. Nevertheless, psychology has realized early on that it can take an indirect road to measurement: What can be measured is the strength of association between categories in samples or populations, and what can be quantitatively compared are counts, ratings, or rankings made under different circumstances, or originating from different persons. The strong demand for quantitative analysis of categorical data has thus created a variety of statistical methods, with substantial contributions from psychometrics and sociometrics. What is the common basis of these methods dealing with categories? The basic element they share is that the sample space has a special geometry, in which categories (or persons) are point masses forming a simplex, while distributions of counts or profiles of ratings are centers of gravity, which are also point masses. Rankings form a discrete subset in the interior of the simplex, known as the permutation polytope, and paired comparisons form another subset on the edges of the simplex. Distances between point masses form the basic tool of analysis. The paper gives some history of major concepts, which naturally leads to a new concept: the shadow point. It is then shown how loglinear models, Luce and Rasch models, unfolding models, correspondence analysis and homogeneity analysis, forced classification and classification trees, as well as other models and methods, fit into this particular geometrical framework.