The impact of a conceptual model-based mathematics computer tutor on multiplicative reasoning and problem-solving of students with learning disabilities

The study explored the impact of Please Go Bring Me-COnceptual Model-based Problem Solving (PGBM-COMPS) computer tutoring system on multiplicative reasoning and problem solving of students with learning disabilities. The PGBM-COMPS program focused on enhancing the multiplicative reasoning and problem solving through nurturing fundamental mathematical ideas and moving students above and beyond the concrete level of operation. This is achieved by taking advantages of the constructivist approach from mathematics education and explicit conceptual model-based problem solving approach from special education. Participants were three elementary students with learning disabilities (LD). A mixed method design was employed to investigate the effect of the PGBM-COMPS program on enhancing students’ multiplicative reasoning and problem solving. It was found that the PGBM-COMPS program significantly improved participating students’ problem solving performance not only on researcher developed criterion tests but also on a norm-referenced standardized test. Qualitative and quantities data from this study indicate that, in addition to nurturing fundamental concept of composite units, it is necessary to help students to understand underlying problem structures and move toward mathematical model-based problem representation and solving for generalized problem solving skills.     

Notions of equivalence through ratios: Students with and without learning disabilities

Students with learning disabilities (LD) specific to mathematics historically underperform in foundational content such as rational number equivalence. This study examined the strategy usage and multiplicative thinking of three third grade children (i.e., Bill, a child identified as having a learning disability specific to mathematics, Carl, a child labeled as low achieving in mathematics, and Albert, a child labeled as typically achieving) before, during, and after participating in tutoring sessions consisting of student-centered pedagogy and equivalence tasks presented through an underutilized interpretation of rational number: namely, the ratio interpretation. Constant comparison analysis of the children's work during the tutoring sessions as well as responses to tasks during two clinical interviews seemed to indicate that all three children increased their use of viable strategies, with notable differences in the sophistication of the strategies as well as the level of multiplicative thinking utilized before and after the ratio-based tutoring sessions. Yet, Bill's continued use of rudimentary strategies reflects a need for continued research to investigate why the use of such strategies persists and how supporting the development of more sophisticated strategies (especially among children with LD) can be achieved.     

Non-robustness with Respect to Intervention Costs in Optimal Control

Abstract

It is well known that a wide range of impulse control problems are continuous, but not differentiable, with respect to intervention cost as it approaches zero. We show a similar non-C¹ result, assuming that the problem and the value function are sufficiently regular. The result is that if the value function is locally C¹ in the state variable, then it does not admit first order approximation wrt. the intervention cost. Our analysis also covers problems with reflection (i.e., vanishing impulse).     

Where is Difference? Processes of Mathematical Remediation through a Constructivist Lens

In this study, we challenge the deficit perspective on mathematical knowing and learning for children labeled as LD, focusing on their struggles not as a within student attribute, but rather as within teacher-learner interactions. We present two cases of fifth-grade students labeled LD as they interacted with a researcher-teacher during two constructivist-oriented teaching experiments designed to foster a concept of unit fraction. Data analysis revealed three main types of interactions, and how they changed over time, which seemed to support the students’ learning: Assess, Cause and Effect Reflection, and Comparison/Prediction Reflection. We thus argue for an intervention in interaction that occurs in the instructional process for students with LD, which should replace attempts to “fix” ‘deficiencies’ that we claim to contribute to disabling such students.     

Intervention analysis to identify significant exposures in pulsing advertising campaigns: an operative procedure

The purpose of this paper is to develop an operational method to detect the most effective exposures in the context of a given pulsing advertising campaign. For most effective, are intended those exposures that produce a statistically significant increase in the level of a response variable, either temporarily or permanently. The method consists in specifying an intervention model for the response variable, where the significant exposures are selected on the basis of a probabilistic criterion, and is empirically evaluated by using brandlevel data from five advertising tracking studies that also include the actual spending schedules. Given a pulsing advertising campaign, the proposed method serves both as an a-posteriori improvement of the campaign itself and as an a-priori additional information for programming future scheduling. Mathematics Subject Classification: 62M10.     

An application of the neural network energy function to machine sequencing

We apply a neural network approach for solving a one-machine sequencing problem to minimize either single- or multi-objectives, namely the total tardiness, total flowtime, maximimum tardiness, maximum flowtime, and number of tardy jobs. We formulate correspondingly nonlinear integer models, for each of which we derive a quadratic energy function, a neural network, and a system of differential equations. Simulation results based on solving the nonlinear differential equations demonstrate that our approach can effectively solve the sequencing problems to optimality in most cases and near optimality in a few cases. The neural network approach can also be implemented on a parallel computing network, resulting in significant runtime savings over the optimization approach. This paper should not be disseminated without written permission of the authors.     

一类因果模型的可识别性条件

因果问题在近代医学,生物学,社会科学的研究中占有非常重要的地位。通过因果关系预见某些行为或策略对研究对象的影响已经成为一些实际研究的最终目的。Rubin(1978)提出了解决因果问题的虚拟事实模型,建立了因果推断统计分析的基本框架。虚拟事实模型的因果效应是以实际观测数据为研究对象的,但又不完全由数据之间的相关性决定,因此在讨论因果效应时存在可识别性问题。如果因果效应可识别,则有可能利用观测数据直接计算因果效应。但是,众 所周知:在不加任何假设或限制的条件下,虚拟事实模型的因果效应是不可识别的。若要研究变量间的因果效应就必须对虚拟事实模型加入某些必要的限制,使因果效应在这些限制下可识别。郑忠国,张艳艳,童行伟在“因果模型因果效应的可识别性研究”中针对控制变量与协变量相互独立的一类模型的可识别性进行了研究,指出在某些特定的可替换性假设之下,模型的因果效应具有可识别性。该文将针对控制变量作用于协变量的虚拟事实模型进行可识别性研究。作者将指出:控制变量是否作用于协变量并不影响因果效应的可识别性和可替换性假设。并给出:此类模型因果效应可唯一确定的充要条件 。      

Stability and optimality of a multi-product production and storage system under demand uncertainty

This work develops a discrete event model for a multi-product multi-stage production and storage (P&S) problem subject to random demand. The intervention problem consists of three types of possible decisions made at the end of one stage, which depend on the observed demand (or lack of) for each item: (i) to proceed further with the production of the same product, (ii) to proceed with the production of another product or (iii) to halt the production. The intervention problem is formulated in terms of dynamic programming (DP) operators and each possible solution induces an homogeneous Markov chain that characterizes the dynamics. However, solving directly the DP problem is not a viable task in situations involving a moderately large number of products with many production stages, and the idea of the paper is to detach from strict optimality with monitored precision, and rely on stability. The notion of stochastic stability brought to bear requires a finite set of positive recurrent states and the paper derives necessary and sufficient conditions for a policy to induce such a set in the studied P&S problem. An approximate value iteration algorithm is proposed, which applies to the broader class of control problems described by homogeneous Markov chains that satisfy a structural condition pointed out in the paper. This procedure iterates in a finite subset of the state space, circumventing the computational burden of standard dynamic programming. To benchmark the approach, the proposed algorithm is applied to a simple two-product P&S system.     

Claustrophobia in MRI: the role of cognitions

Purpose

This study aimed to investigate the role of cognitive and behavioural factors in the experience of claustrophobia in the context of magnetic resonance imaging (MRI) scanners.

Materials and Methods

One hundred and thirty outpatients attending an MRI unit completed questionnaires before and after their scans. Specific measures of experience in the scanner included subjective anxiety, panic symptoms, strategies used to stay calm and negative cognitions (such as ‘I will suffocate’ and ‘I am going to faint in here’). Other general measures used included anxiety, depression, health anxiety and fears of restriction and suffocation.

Results

The amount of anxiety experienced during the scan was related to the perceived amount of time spent having physical symptoms of panic. Cognitions reported concerned the following: suffocation, harm caused by the machine and lack of perceived control. The number of strategies patients used to cope in the machine was also a related factor. Neither position in the scanner, nor head coil use nor previous experience of being in the scanner was related to levels of anxiety.

Conclusion

The cognitions identified here may be used to construct a measure to identify those unable to enter the scanner or those most likely to become claustrophobic whilst undergoing the procedure and to further inform future brief, effective interventions.