Cause and Event: Supporting Causal Claims through Logistic Models

Efforts to identify and support credible causal claims have received intense interest in the research community, particularly over the past few decades. In this paper, we focus on the use of statistical procedures designed to support causal claims for a treatment or intervention when the response variable of interest is dichotomous. We identify seven key features of logistic regression studies that should play a critical role in estimating a causal effect and discuss their implications for causal inference. These include elaboration of research design, clarification of link function, model specification, challenges and limitations of sample size, interpretation of treatment effect through odds ratios, statistical tests and examination of model fit, and the potential for multilevel logistic models in pursuit of causal claims. Our recommendations are intended to guide researchers in the critical evaluation of logistic regression models for analyses culminating in causal claims and to promote stronger design and modeling strategies for reliable causal inference.     

英语教学资源库系统中个性化检索模块的设计

针对目前教学资源服务中普遍存在的海量教学资源与用户个性化需求之间的矛盾,在基于校园网的英语教学资源库系统中建立了一个教学资源个性化检索模型,该模型把基于网络的个性化信息服务引入英语教学资源体系,尊重用户中存在的差异,学习和跟踪用户的个性化兴趣,并根据用户的个性化兴趣特征对英语教学资源进行过滤,实现少而精的教学资源个性化检索。实验表明:该模型能有效地提高英语教学资源服务中个性化检索的查全率和查准率。     

The development of self-regulated learning

The purpose of this study is to examine the tendency for the development of self-regulated learning according to grade level. As a means to this end, competing causal models for each grade were set up, and through validity tests for the causal models the most fitting self-regulated learning model for South Korea students could be arrived at. Based on the conceptual model of self-regulated learning proposed by Pintrich (1989), Pintrich and De Groot (1990), and Zimmerman (1989, 1990), which focus on the relationship between motivation and cognition, three causal models were set up. According to the results of a structural analysis for 1,865 elementary, middle and high school students, model П which structures intrinsic value as an exogenous variable, was determined the best-fitting model. In addition, the 30% variance in the 5th grade elementary school students and 32% and 18% variance in middle school and high school students respectively are explained by the test model.     

设置与调度交巡警服务平台的算法模型

探讨了交巡警服务平台的合理设置与调度问题。建立时间最短模型和最大化均衡模型,得到不同目标下各平台所分配的管辖范围;以最长时间达最小值为指标,建立封锁中心城区的调度模型,进而以总时间最短为目标,建立修正模型;建立双目标规划（MOP）模型,通过设计的改进穷举法和随机搜索算法,给出了增加服务平台的方案;通过对平台资源分配的合理性讨论,利用模拟退火算法（SA）,给出全市平台的重置方案;设计蜘蛛网模型,以网络包围圈的形式,通过外、内两区的有效控制,给出围堵重大刑案嫌犯的方案。     

Models for Adolescent Ministry: Exploring Eight Ecumenical Examples 1

This study addresses eight models for “doing” ecclesial adolescent ministry in the United States from both a Catholic and ecumenical perspective. The article offers youth ministers in the field with creative insights to further enhance their ministry with adolescents. The eight models represent an inclusive theological and pastoral framework on three levels that youth ministers will find helpful. First, the study examines theologically each model under consideration. Second, the article provides pastoral insights for integration into the youth ministry. Third, the essay will address the possible strengths and limitations that a parish youth minister may encounter and find useful. Evangelical, Protestant, and Catholic youth ministers will find each model appropriate and applicable to adolescent needs and church demands as they embark on their valuable and important work.     

A Multi-Objective Genetic Algorithm for Optimal Portfolio Problems

This paper concerns with modeling and design of an algorithm for the portfolio selection problems with fixed transaction costs and minimum transaction lots. A mean-variance model for the portfolio selection problem is proposed, and the model is formulated as a non-smooth and nonlinear integer programming problem with multiple objective functions. As it has been proven that finding a feasible solution to the problem only is already NP-hard, based on NSGA-II and genetic algorithm for numerical optimization of constrained problems (Genocop), a multi-objective genetic algorithm (MOGA) is designed to solve the model. Its features comprise integer encoding and corresponding operators, and special treatment of constraints conditions. It is illustrated via a numerical example that the genetic algorithm can efficiently solve portfolio selection models proposed in this paper.This approach offers promise for the portfolio problems in practice.     

How can historical understanding best be assessed? Use of prediction tasks to assess how students understand the role of causal factors that produce historical events

The main objective of this research is to study the viability of using hypothetical problems that need the application of causal models for their solution, as a method for assessing understanding in Social Sciences. So, this method was used to describe how seventh grade students understand causal factors affecting the “discovery and colonization of America”. First, the causal model expected to have been understood was made explicit. Then, eight problems were designed to assess whether students were able to deduce the implications of past conditions and to use these implications to anticipate what consequences would most likely follow in each of the hypothetical problems. 51 seventh graders were interviewed with this purpose, and their answers were coded and analysed. The results have shown that 60 to 100% of seventh graders understand correctly the role of most factors that eased the discovery and the colonization. However, students often had previous ideas that caused them to make erroneous predictions The same results were found when examining whether students had a mental model integrating the different causal factors studied. Theoretical and practical implications are discussed.     

When do students ask why? Examining the precursors and outcomes of causal search among first-year college students

It has been theorized that students are most likely to ask why following unexpected, negative, and/or important events (Weiner, 1985); however, the unique contribution of these precursors to causal search and the resultant cognitions, emotions, and behaviors remain largely unclear. In the current study we examined causal search regarding test outcomes among 371 first-year college students. Responses to hypothetical scenarios indicated that unexpected events, and unexpected/negative events in combination, would elicit the most causal search. Based on performance on an actual test, precursors measured prior to the test indicated negative test outcomes elicited the greatest causal search. Alternatively, precursors measured following the test indicated a similar pattern to the scenarios. In each instance, event importance was also found to positively predict casual search. Overall, the results suggest that the exclusion of relevant precursors, self-serving biases, and divergent methodologies may have resulted in the discrepancies of previous research on causal search precursors. Finally, students who engaged in more causal search made more ability, test difficulty, and luck attributions, fewer effort attributions, experienced less pride and more shame, guilt, regret, and anger, and received poorer grades. The internal/uncontrollable attributional pattern suggests that first-year college students who are at-risk of de-motivating cognitions, emotions, and behaviors could be supported with cognitive interventions such as attributional retraining.     

Finite Feedback Cycling in Structural Equation Models

In models containing reciprocal effects, or longer causal loops, the usual effect estimates assume that any effect touching a loop initiates an infinite cycling of effects around that loop. The real world, in contrast, might permit only finite feedback cycles. I use a simple hypothetical model to demonstrate that if the world permits only a few effect cycles, many coefficient estimates are substantially biased. If the world permits additional partial-cycle use in addition to full cyclings around the causal loop, some of the effect estimates are proper, and a full set of proper effect estimates can be recovered by hand calculations involving the model total effects. If the world permits no additional partial-cycle use, it might not be possible to recover proper estimates from the usual output.

It is not the equations representing the causal model, but rather the calculations of the covariance implications of the model, that change with limited cycling possibilities. Unfortunately, the features required to permit direct estimation of limited-cycle effects are not under user control in common structural equation programs, so estimation and detailed investigation of models with finite cycling of effects around feedback loops awaits new programming. To obtain unbiased estimates with limited causal cyclings, the researcher must continue to strive to specify the proper effect locations but must also attend to the number of full and partial causal cyclings permitted by the world. Determining the appropriate number of cycles is not a matter to be delegated to a statistician; it is something the researcher must attend to as a matter of substantive theory, methodology, and model interpretation.     

Measuring teacher perceptions of the “how” and “why” of student motivation

In the field of educational psychology, there is diverse and active research in motivation for learning and achievement. Many instruments exist for assessing students' motivation, primarily as self-report. Fewer instruments are available for assessing teachers' perceptions of their students' motivation, and fewer still for assessing teachers' perceptions of reasons for students' lack of motivation. Teachers' intervention strategies for motivation are linked to their causal perceptions. Therefore, it is important to assess those causal perceptions. In this paper, we offer evidence for the Perceptions of Student Motivation questionnaire, a new measure that offers evidence of validity and reliability for this purpose among high school teachers. It offers potential to increase efficiency and clarity of findings regarding teachers' perceptions of students' motivation.     

基于B／S模式的学生信息管理系统

文章应用网络技术,采用B／S模式,构建基于WEB(环境PHP MySQL Apache)的学生管理信息系统,不仅可以使信息通道更为通畅,而且可以资源共享．既为全面了解学生综合信息提供了方便,又可以用电子档案逐步代替或辅佐纸质的档案,实现高校学生管理的信息化、科学化, 辅助学校领导决策,方便学校对学生的管理,并向师生员工提供更好的服务．     

A causal framework for analysing alternative conceptions

A model for the analysis of subjects’ causal reasoning is proposed, drawing on de Kleer and Brown's ‘mechanistic mental models’. The purpose of the model is to provide a framework for understanding the possible causal structure of children's and adults’ spontaneous reasoning about the physical world. The model is used to analyse data on students’ understanding of aspects of forces and motion; to reanalyse a protocol of Driver's; and to compare and criticize the models of Andersson and of Rozier. We show how the model can provide a language for describing common‐sense causal thinking.     

Using causal models to bridge the divide between big data and educational theory

An extraordinary amount of data is becoming available in educational settings, collected from a wide range of Educational Technology tools and services. This creates opportunities for using methods from Artificial Intelligence and Learning Analytics (LA) to improve learning and the environments in which it occurs. And yet, analytics results produced using these methods often fail to link to theoretical concepts from the learning sciences, making them difficult for educators to trust, interpret and act upon. At the same time, many of our educational theories are difficult to formalise into testable models that link to educational data. New methodologies are required to formalise the bridge between big data and educational theory. This paper demonstrates how causal modelling can help to close this gap. It introduces the apparatus of causal modelling, and shows how it can be applied to well-known problems in LA to yield new insights. We conclude with a consideration of what causal modelling adds to the theory-versus-data debate in education, and extend an invitation to other investigators to join this exciting programme of research.

Practitioner notes

What is already known about this topic

• ‘Correlation does not equal causation’ is a familiar claim in many fields of research but increasingly we see the need for a causal understanding of our educational systems.
• Big data bring many opportunities for analysis in education, but also a risk that results will fail to replicate in new contexts.
• Causal inference is a well-developed approach for extracting causal relationships from data, but is yet to become widely used in the learning sciences.

What this paper adds

• An overview of causal modelling to support educational data scientists interested in adopting this promising approach.
• A demonstration of how constructing causal models forces us to more explicitly specify the claims of educational theories.
• An understanding of how we can link educational datasets to theoretical constructs represented as causal models so formulating empirical tests of the educational theories that they represent.

Implications for practice and/or policy

• Causal models can help us to explicitly specify educational theories in a testable format.
• It is sometimes possible to make causal inferences from educational data if we understand our system well enough to construct a sufficiently explicit theoretical model.
• Learning Analysts should work to specify more causal models and test their predictions, as this would advance our theoretical understanding of many educational systems.

     

Towards a Curricular Model of the Nature of Science

The nature of science is a complex theme, and continues to be the subject of advanced and ongoing scholarship, drawing upon a range of disciplines. Therefore, whatever is presented in school science as being ‘the’ nature of science must at best be a simplification, and so there is a need to form judgements about which simplifications are most appropriate. Effective ‘curricular models’ of science concepts are designed simplifications of scientific models that guide teachers by indicating target knowledge that is deemed appropriate in terms of the prior learning and conceptual development of a group of learners, and which is both ‘intellectually honest’ and a suitable basis for further learning. A similar approach can guide teaching about the nature of science. A consideration of the English National Curriculum offers an example of how aims relating to the teaching of the nature of science may not be realised in the absence of a suitable explicit curricular model to guide teaching.     

The Weird World,and Equally Weird Measurement Models: Reactive Indicators and the Validity Revolution

Researchers using structural equation modeling (SEM) aspire to learn about the world by seeking models with causal specifications that match the causal forces extant in the world. This quest for a model matching existing worldly causal forces constitutes an ontology that orients, or perhaps reorients, thinking about measurement validity. This article illustrates several ways the seemingly innocuous quest for structural equation models that mirror “the world beyond” confronts entrenched notions of measurement validity. The article begins by considering simple measurement models and ends by “discovering” a new class of indicators called reactive indicators. Reactive indicators act as both the cause and effect of an underlying latent variable. The identifiability of a simple model containing a reactive indicator is proven and a research example illustrating the use of a reactive indicator is provided. However, the real challenge is to understand how an indicator can be both a cause and effect of the latent it measures. The understanding does not come from complying with the traditional rules for reliability and validity, but from focusing on the quest to make the structural equation model match the structuring of the worldly forces we seek to understand. Valid measurement in the context of a weirdly structured world requires an equally weird structural equation model.     

Applying Case‐based Reasoning in Knowledge Management to Support Organizational Performance

Research and practice in human performance technology (HPT) has recently accelerated the search for innovative approaches to supplement or replace traditional training interventions for improving organizational performance. This article examines a knowledge management framework built upon the theories and techniques of case‐based reasoning (CBR) and Nonaka's (1991, 1994) knowledge conversion model to shed light on how organizational performance can be enhanced by leveraging organizational knowledge represented as cases to support learning, working, and innovation of knowledge workers. This framework offers HPT practitioners new ways of thinking and methods for the design of performance support interventions by which organizational knowledge is stored, codified, delivered, and acted upon in context, on demand, and at the point of need. This paper describes a project, Knowledge Innovation for Technology in Education (KITE), which was designed to support professional development of teachers using CBR and knowledge conversion theories.     

Spark作业性能建模及参数优化

Apache Spark分布式大数据计算框架应用广泛,但是其配置参数繁多导致使用难度较大,且不合理的配置将严重影响作业执行性能,研究Spark参数对性能的影响并进一步对参数进行自动优化具有重要意义.该文分析了 Spark作业中影响系统行为的关键参数,建立了性能模型,并进一步探索了 Spark参数自动优化的方法和策略.通...     

基于用户兴趣的个性化搜索系统研究

针对目前常用搜索引擎在查询时返回结果数量巨大且杂乱无章的现象,在Web客户端为实现对用户的个性化信息服务设计了一种基于用户兴趣的搜索系统。利用用户的兴趣对于用户提出的搜索条件进行处理,再通过常用的搜索引擎进行查询,并将得到的结果进行二次排序,同时通过反馈信息不断更新用户的兴趣,以满足用户不断变化的需求。实验证明这样在保证了查全率的基础上,提高了查准率,从而提高了搜索效率。     

基于粒子群算法的可靠性优化

系统可靠性优化已被证明是一个NP完全问题,不存在精确的求解方法。人们构造了大量的启发式算法,如遗传算法(GA),蚁群算法(ACO),模拟退火算法(SA)等。针对各种算法所存在的早熟收敛,易陷入局部极值点的缺点,提出了将粒子群算法(particle swarm optimization,PSO)用于求解可靠性问题。给出了基于粒子群算法的可靠性优化求解策略,根据数学模型,详细讨论了求解步骤,最后给出了实验仿真结果。结果表明该算法具有较强的局部搜索能力,同时也有更高的搜索效率,与其它方法相比,该算法能够找到更优解,验证了该算法的可行性和有效性。     

Assessing power PC

In a recent theoretical paper, Cheng (1997) presented a new causal model, power PC. She argued that power PC was able to account for data in the literature that raised problems for associative models—notably, the Rescorla-Wagner model (Rescorla & Wagner, 1972). The purpose of the present paper is threefold: (1) to show that, overall, the data in the literature, which Cheng relied on to make her case, do not in fact provide support for power PC, (2) to show that, overall, the experiments reported in the literature since the publication of Cheng, designed specifically to evaluate the predictions of power PC, also do not provide support for power PC, and (3) to suggest that Cheng’s assessment of associative models was too narrowly defined.