面向教育技术学文献数据的主题挖掘

对网络环境下海量的科技文献数据进行文本挖掘可以有效地提高科技文献信息的可用性,发现隐藏的知识。LDA (Latent Dirichlet Allocation)模型是一种能够提取文本隐含主题的非监督学习模型。论文基于LDA模型,以三种国际教育技术期刊在2004-2008年间出版的学术文献为研究对象,进行了主题挖掘和文献分析。     

Investigating students' perceived cognitive needs in university academic reading: a latent variable approach

Although there have been extensive theoretical discussions on the various reading skills needed in academic situations, empirical investigations on this topic are scarce. There is even much less research on the latent structure underlying those skill needs. This study attempts to fill this gap by investigating the perceived factorial structure of academic reading skills and the relative importance of each factor based on questionnaire responses by 221 undergraduates from an English‐medium university in New Zealand. A series of factor analyses and hierarchical model testing revealed that the respondents' perceived needs in academic reading consisted of five distinguishable subdomains, which could be further divided into non‐expeditious reading and expeditious reading. It was also found that the subdomain of textbase comprehension (e.g., understanding explicit details and main ideas) was needed significantly more than other subdomains, although all subdomains were reported to be needed more than half of the time. These findings not only support previous major theoretical discussions about reading types from a novel perspective (i.e., students' perceived needs) but also help specify the skill areas that academic reading curricula and assessments may need to cover and prioritise.     

A Hierarchical Rater Model for Constructed Responses,with a Signal Detection Rater Model

The hierarchical rater model (HRM) re‐cognizes the hierarchical structure of data that arises when raters score constructed response items. In this approach, raters’ scores are not viewed as being direct indicators of examinee proficiency but rather as indicators of essay quality; the (latent categorical) quality of an examinee's essay in turn serves as an indicator of the examinee's proficiency, thus yielding a hierarchical structure. Here it is shown that a latent class model motivated by signal detection theory (SDT) is a natural candidate for the first level of the HRM, the rater model. The latent class SDT model provides measures of rater precision and various rater effects, above and beyond simply severity or leniency. The HRM‐SDT model is applied to data from a large‐scale assessment and is shown to provide a useful summary of various aspects of the raters’ performance.     

基于在线主题模型的新闻热点演化模型分析

为了对新闻媒体平台的重大事件进行话题演化建模分析,基于隐含狄利克雷分布（LDA主题模型算法）对话题动态建模,在变分推断主题模型基础上建立衡量话题内容和热度变化的流行话题模型（TTM-OLDA）。针对用户关注的重大新闻事件发展方向与热度,提出话题内容向量与流行因子,对整个话题生命周期进行量化,从而有效地从大量相关新闻中挖掘出话题演化细节,帮助用户更好地掌握话题发展情况。在特定新闻板块筛选的数据集下,通过设置对比实验和人工评测方式,验证该方法在困惑度上优于在线主题模型算法。     

A Bayesian Approach for Analyzing Hierarchical Data With Missing Outcomes Through Structural Equation Models

Structural equation models are widely appreciated in behavioral, social, and psychological research to model relations between latent constructs and manifest variables, and to control for measurement errors. Most applications of structural equation models are based on fully observed data that are independently distributed. However, hierarchical data with a correlated structure are common in behavioral research, and very often, missing data are encountered. In this article, we propose a 2-level structural equation model for analyzing hierarchical data with missing entries, and describe a Bayesian approach for estimation and model comparison. We show how to use WinBUGS software to get the solution conveniently. The proposed methodologies are illustrated through a simulation study, and a real application in relation to organizational and management research concerning the study of the interrelationships of the latent constructs about job satisfaction, job responsibility, and life satisfaction for citizens in 43 countries.     

A two-process model of metacognitive monitoring: Evidence for general accuracy and error factors

We examined the latent structure of metacognitive monitoring judgments using hierarchical confirmatory factor analysis to compare five competing theoretical models with respect to domain-specific versus domain-general monitoring processes. We expected our results to support a domain-general monitoring model. Of the five models, the domain general monitoring model provided the best fit. In this model, level-1 domain-specific accuracy and error factors for each of the three tests loaded on second-order domain-general accuracy and error factors, which then loaded on a third-order general monitoring factor. This model suggest that metacognitive monitoring consists of two different types of cognitive processes, one that is associated with accurate monitoring judgments, and one that is associated with error in monitoring judgments. We discuss the theoretical and practical instructional implications of our findings.     

Using multidimensional scaling to improve functionality of the Revised Learning Process Questionnaire

The Revised Learning Process Questionnaire has been part of the development of a conceptual understanding of how students learn and what motivates them to engage in particular tasks. We obtained responses from 329 student volunteers at a mid‐sized public university in the southeast USA. We first investigated whether the psychometric properties and latent factor structure of this questionnaire are replicable in a different educational context and with students from a different country than that originally used to create and validate the questionnaire. We found this to be true. Second, we used Profile Analysis via Multidimensional Scaling (PAMS) to improve the diagnostic functionality of the instrument as well as further explore the latent structure of the questionnaire. The factor structure was evident in this solution, but we found that interpreting the latent structure in terms of the dimensions of Strategy and Motive as opposed to the factors of Deep and Surface approaches to be more appropriate for diagnostic use. We also found that PAMS has the inherent ability to assess an individual’s fit within the model, thereby acting as a measure of self‐report credibility. The Strategy dimension was found to have ecological validity through analysing its relationship to academic performance.     

Modeling latent true scores to determine the utility of aggregate student perceptions as classroom indicators in HLM: The case of classroom goal structures

Measures of classroom climate such as classroom goal structures are often assessed through students’ perceptions; the aggregated means within classrooms are then sometimes labeled as “classroom characteristics.” The validity of these constructs is limited by the reliability of the measure at both the student and classroom level; yet, few studies accurately assess reliability when multilevel models are used. We demonstrate the use of a three-level hierarchical linear model to estimate latent true score measures of students’ perceptions of goal structures, appropriately adjusted for their nested structure. To investigate the distinctiveness of goal structures from teacher characteristics, we examined the inter-correlations among the student and classroom level variables, and predictors of each.     

The Impact of Multidimensionality on Extraction of Latent Classes in Mixture Rasch Models

This study investigates the effect of multidimensionality on extraction of latent classes in mixture Rasch models. In this study, two‐dimensional data were generated under varying conditions. The two‐dimensional data sets were analyzed with one‐ to five‐class mixture Rasch models. Results of the simulation study indicate the mixture Rasch model tended to extract more latent classes than the number of dimensions simulated, particularly when the multidimensional structure of the data was more complex. In addition, the number of extracted latent classes decreased as the dimensions were more highly correlated regardless of multidimensional structure. An analysis of the empirical multidimensional data also shows that the number of latent classes extracted by the mixture Rasch model is larger than the number of dimensions measured by the test.     

Accounting for Rater Effects With the Hierarchical Rater Model Framework When Scoring Simple Structured Constructed Response Tests

Many large‐scale assessments are designed to yield two or more scores for an individual by administering multiple sections measuring different but related skills. Multidimensional tests, or more specifically, simple structured tests, such as these rely on multiple multiple‐choice and/or constructed responses sections of items to generate multiple scores. In the current article, we propose an extension of the hierarchical rater model (HRM) to be applied with simple structured tests with constructed response items. In addition to modeling the appropriate trait structure, the multidimensional HRM (M‐HRM) presented here also accounts for rater severity bias and rater variability or inconsistency. We introduce the model formulation, test parameter recovery with a focus on latent traits, and compare the M‐HRM to other scoring approaches (unidimensional HRMs and a traditional multidimensional item response theory model) using simulated and empirical data. Results show more precise scores under the M‐HRM, with a major improvement in scores when incorporating rater effects versus ignoring them in the traditional multidimensional item response theory model.     

油田产量构成二层规划模型及其应用

利用神经网络方法建立了采油厂的各开发指标与其影响因素的关联关系,然后把石油公司看成一个具有递阶性的二层系统,应用二层规划理论建立了油田产量构成二层规划模型。实例表明这种油田产量规划方法更具有合理性、实用性和科学性,可以使油田开发规划更适应开发和经济的发展,使其实现稳产、可持续性发展和更大的经济效益。     

Building Latent Class Growth Trees

Researchers use latent class growth (LCG) analysis to detect meaningful subpopulations that display different growth curves. However, especially when the number of classes required to obtain a good fit is large, interpretation of the encountered class-specific curves might not be straightforward. To overcome this problem, we propose an alternative way of performing LCG analysis, which we call LCG tree (LCGT) modeling. For this purpose, a recursive partitioning procedure similar to divisive hierarchical cluster analysis is used: Classes are split until a certain criterion indicates that the fit does not improve. The advantage of the LCGT approach compared to the standard LCG approach is that it gives a clear insight into how the latent classes are formed and how solutions with different numbers of classes relate. The practical use of the approach is illustrated using applications on drug use during adolescence and mood regulation during the day.     

Item Analysis by the Hierarchical Generalized Linear Model

The hierarchical generalized linear model (HGLM) is presented as an explicit, two-level formulation of a multilevel item response model. In this paper, it is shown that the HGLM is equivalent to the Rasch model and that, characteristic of the HGLM, person ability can be expressed in the form of random effects rather than parameters. The two-level item analysis model is presented as a latent regression model with person-characteristic variables. Furthermore, it is shown that the two-level HGLM model can be extended to a three-level latent regression model that permits investigation of the variation of students' performance across groups, such as is found in classrooms and schools, and of the interactive effect of person-and group-characteristic variables.     

基于层次分析法的柔性基板材料评价

柔性基板材料种类多样并且存在各自的优缺点，筛选最优材料存在一定困难。为此，提出了层次分析法，对柔性基板材料进行评价。通过了解柔性基板材料的属性与性能，提取重要因素作为决策的准则，构建层次结构模型与判断矩阵，进行层次单排序与一致性检验及层次总排序。利用柔性基板材料的具体案例详细介绍了层次分析的运算过程，用MATLAB 软件编写程序，减少了层次分析法的计算量。评价结果，层次分析法只是筛选出备选方案中的最优者并不能为决策者提供新的思路，获得更优的方案得综合考量各种因素。     

基于 MOOC 课程评论的话题挖掘与情感分析研究

为了深入挖掘与分析在线课程评论文本,探索学习者参与在线课程学习时关注的话题及其情感态度,为提高在线课程质量提供帮助。首先采用词频分析方法,实现对学习者在线课程评论内容的整体认识|然后利用非监督学习方法潜在狄利克雷分布主题模型对评论文本信息的特征结构、语义内容进行自动挖掘和分析,得到学习者的关注话题|最后对每个话题的课程评论文本进行情感倾向分析,得到学习者的情感倾向分布。实验结果表明,在参与课程学习的过程中,学习者主要关注教师授课、课程内容和学习资源 3 个话题。情感分析结果显示,学习者对于该课程普遍表示满意和赞赏,但是对于该课程学习资源表达了较多负面情感。     

A Multidimensional Finite Mixture Structural Equation Model for Nonignorable Missing Responses to Test Items

We propose a structural equation model, which reduces to a multidimensional latent class item response theory model, for the analysis of binary item responses with nonignorable missingness. The missingness mechanism is driven by 2 sets of latent variables: one describing the propensity to respond and the other referred to the abilities measured by the test items. These latent variables are assumed to have a discrete distribution, so as to reduce the number of parametric assumptions regarding the latent structure of the model. Individual covariates can also be included through a multinomial logistic parameterization for the distribution of the latent variables. Given the discrete nature of this distribution, the proposed model is efficiently estimated by the expectation–maximization algorithm. A simulation study is performed to evaluate the finite-sample properties of the parameter estimates. Moreover, an application is illustrated with data coming from a student entry test for the admission to some university courses.     

国外自我概念结构与测量研究综述

自我概念一直是国外心理学研究的重要内容。对自我概念结构的探讨,经历了从单维模型向阶层模型的转变,与此对应,自我概念的测量也经历了从测量单维结构的第一代量表向测量多维阶层结构的第二代量表的转变。国外通过理论模型建构与实证测量验证互动来研究自我概念对于探究中国人自我概念具有方法论的启示。     

Hierarchical Regression Without Phantom Factors

In a recent note in the Teacher's Corner of this journal, de Jong (1999) proposed a method for computing hierarchical or fixed-order regressions in the context of latent variables. The essence of this approach is to decompose the predictor variables in the regression into orthogonal components based on a Cholesky decomposition and to regress the dependent variable on these orthogonal components. The components may be conceived of as phantom factors that do not have their own indicators. Because the idea of sequential entry of predictors in a latent variable regression framework seems generally to be unknown, the approach was developed by de Jong for latent variable regressions. However, it equally can be used for observed variable regression or path models. In this article we show that the phantom factors are unnecessary to achieve the objectives of a hierarchical regression. We give a direct approach that is equivalent to de Jong's approach.     

近十年来我国信息技术与研究性学习整合的演进与思考

我国中小学实施研究性学习已经十年了,为全面了解近十年来信息技术与研究性学习整合的研究现状与发展趋势,采用内容分析法,对发表在核心期刊上的有关文章,从时间分布模型、期刊分布模型、研究者群体分布模型和主题分布模型等四个维度进行了统计分析;并对网络环境下"研究性学习课程实施的理论基础和模式"、"学科教学中研究性学习实施的模式"、"研究性学习中信息资源与技术工具的作用模式"和"研究性学习支持平台设计与开发模式"四个典型研究主题的特征与变化趋势进行了定性分析,找出了我国信息技术与研究性学习整合研究中存在的问题,给出了建议与思考。     

基于层次分析法的潜在市场影响因素研究

突破性技术创新的市场是一种潜在市场,研究潜在市场的影响因素,有助于对潜在市场进行预测和培育,从而引导突破性技术创新的发展和壮大。在研究潜在市场内在规律的基础上,建立了其影响因素的层次模型结构,并采用层次分析法对各因素的重要性进行排序,以便为市场政策的决定提供依据。