The Role of Visual Representations in Advanced Mathematical Problem Solving: An Examination of Expert-Novice Similarities and Differences

Expert mathematicians are contrasted with undergraduate students through a two-part analysis of the potential and actual use of visual representations in problem solving. In the first part, a classification task is used to indicate the extent to which visual representations are perceived as having potential utility for advanced mathematical problem solving. The analysis reveals that both experts and novices perceive visual representation use as a viable strategy. However, the two groups judge visual representations likely to be useful with different sets of problems. Novices generally indicate that visual representations would likely be useful mostly for geometry problems, whereas the experts indicate potential application to a wider variety of problems. In the second part, written solutions to problems and verbal protocols of problem-solving episodes are analyzed to determine the frequency, nature, and function of the visual representations actually used during problem solving. Experts construct visual representations more frequently than do novices and use them as dynamic objects to explore the problem space qualitatively, to develop a better understanding of the problem situation, and to guide their solution planning and enactment of problem-solving activity. In contrast, novices typically make little use of visual representations.     

The Role of Visual Representations in Advanced Mathematical Problem Solving: An Examination of Expert-Novice Similarities and Differences

Expert mathematicians are contrasted with undergraduate students through a two-part analysis of the potential and actual use of visual representations in problem solving. In the first part, a classification task is used to indicate the extent to which visual representations are perceived as having potential utility for advanced mathematical problem solving. The analysis reveals that both experts and novices perceive visual representation use as a viable strategy. However, the two groups judge visual representations likely to be useful with different sets of problems. Novices generally indicate that visual representations would likely be useful mostly for geometry problems, whereas the experts indicate potential application to a wider variety of problems. In the second part, written solutions to problems and verbal protocols of problem-solving episodes are analyzed to determine the frequency, nature, and function of the visual representations actually used during problem solving. Experts construct visual representations more frequently than do novices and use them as dynamic objects to explore the problem space qualitatively, to develop a better understanding of the problem situation, and to guide their solution planning and enactment of problem-solving activity. In contrast, novices typically make little use of visual representations.     

Reliable Solution of Parabolic Obstacle Problems with Respect to Uncertain Data

A class of parabolic initial-boundary value problems is considered, where admissible coefficients are given in certain intervals. We are looking for maximal values of the solution with respect to the set of admissible coefficients. We give the abstract general scheme, proposing how to solve such problems with uncertain data. We formulate a general maximization problem and prove its solvability, provided all fundamental assumptions are fulfilled. We apply the theory to certain Fourier obstacle type maximization problem.     

Distinguishing Features of Mechanical and Human Problem-Solving

Over the years, research in mathematical problem-solving has examined expert/novice problem-solving performance on various types of problems and subjects. In particular, DeFranco examined two groups of Ph.D. mathematicians as they solved four mathematics problems and found that although all were content experts, only one group were problem-solving experts. Based on this study, this article posits the notion that one distinguishing feature between experts and novices is that experts tend to look for special features of a problem and use algorithms only as a “fail-safe” system while novices act like “machines” relying on algorithms to solve the problems. Why? The article explores the idea that novice problem solvers learned to solve problems the way they learned proof, that is, in a formal, abstract and mechanizable way. Beliefs about proof and the culture in which it is practiced help frame a mathematician's view of the discipline and ultimately impacts classroom practice. The authors believe that current classroom instruction tends to create a culture that fosters algorithmic proficiency and a “machine-like” approach to the learning of mathematics and problem-solving. Further, they argue that mathematicians need to be aware of the distinction between knowing a proof is true and explaining why it is true. When these distinctions are appreciated and practiced during classroom instruction, then and only then will students begin to acquire the mathematical knowledge to become better problem solvers.     

Optimal television schedules in alternative competitive environments

We formulate network-television-scheduling problems as integer programs for three different competitive environment—smyopic, Nash competitive, and cooperative. To provide input data for the scheduling models, we develop and estimate a regression model in which show-part ratings are regressed on variables that influence television viewership, including day, time slot, show attribute, and competitive effects, as well as lead-in from the previous show part. We apply our models by solving for optimal myopic (noncompetitive) and Nash competitive week-long prime-time schedules for the three major networks for two specific weeks. We find that there are substantial gains to optimization and that those gains are not diminished much by competition. We illustrate the cooperative problem for six time slots and show how the solution differs from the Nash solution. We discuss the use of simple programming heuristics such as counterprogramming.     

An historical example of mathematical modelling: the trajectory of a cannonball

Classroom considerations of the concept and processes of mathematical modelling can do much to strengthen students’ problem solving skills. A systematic exposure to the techniques of mathematical modelling helps students formulate problems, re‐think those problems in mathematical terms, appreciate possible solution constraints and seek solutions that are realistic within the scope and conditions of the problem. While many mathematical modelling situations can be found in today's world, there are special pedagogical values in examining existing mathematical models that have an historical basis. Such an examination should reveal the mechanics of a modelling situation and how a model evolves or is refined to meet ever increasing human demands for accuracy or practicality. The trajectory of a cannonball provides such a modelling example. This topic captures the imagination of students and supplies the basis for a variety of classroom discussions and problem solving encounters.     

Psychological aspects of equation-based modelling

Psychological aspects of equation-based modelling languages like Modelica are under-represented in literature. This does not reflect the growth of the corresponding userbase. In this paper we try to close this gap by tackling the problem from three sides: we conduct expert interviews, we conduct an experiment based on self-reported timings to analyse the effects of inheritance on understandability, and we conduct an online experiment to analyse the effects of model representations on the performance at modelling tasks. The expert interviews suggest that experienced modelling experts tend to develop their models from the top-down, while novices do the opposite. Results from the second experiment indicate that the effect of inheritance on the time to understand a model is both significant and large. The results of the last experiment imply that graphical representations outperform block-diagrams for several metrics. These results open a broad research field on the theory of good modelling practice.     

Multi-period portfolio selection problem under uncertain environment with bankruptcy constraint

The complexity of financial markets leads to different types of indeterminate asset returns. For example, asset returns are considered as random variables, when the available data is enough. When the available data is too small or even no available data to estimate a probability distribution, we have to invite some domain experts to evaluate the belief degrees of asset returns. Then, asset returns can be described as uncertain variables. In this paper, we discuss a multi-period portfolio selection problem under uncertain environment, which maximizes the final wealth and minimizes the risk of investment. Unlike the common method to describe the multi-period portfolio selection problem as a bi-objective optimization model, we formulate this uncertain multi-period portfolio selection problem by a new method in three steps with two single objective optimization models. And, we consider the influence of transaction cost and bankruptcy of investor. Then, the proposed uncertain optimization models are transformed into the corresponding crisp optimization models and we use the genetic algorithm combined with penalty function method to solve them. Finally, a numerical example is given to show the effectiveness and practicability of proposed models and method.     

Categorical approach to modelling and to coupling of models

Solution of any engineering problem starts with a modelling process, which typically involves a choice among different kinds of models. To create a realistic model, one has to think carefully about the modelling process. Particularly in the case of coupled problems when several models are coupled together to represent a given physical phenomenon. This paper presents an approach based on the category theory that allows to describe this modelling process on a more abstract level. Using the advantages of abstract level, one can describe the coupling process in a concise way and introduce certain criteria to check consistency of a coupled model. The main idea of the proposed approach is to introduce a structure in the modelling process, which allows to see how different models interact without a precise look into them. Copyright © 2016 John Wiley & Sons, Ltd.     

Pauli problem and related mathematical problems

We formulate several mathematical modifications of the Pauli problem of recovering the particle state from the results of measuring its coordinates and momenta, including the most abstract statement of the problem for two projection-valued measures. We describe how the Pauli problem reduces to the problem of finding the eigenvectors of certain operators. The performed analysis introduces the problems of modifications of Pauli pairs, and the considered topics and formulated problems require further study. __________ Translated from Teoreticheskaya i Matematicheskaya Fizika, Vol. 157, No. 1, pp. 3–7, October, 2008.     

Modelling to optimise consumptive use of game

There has been a rapid increase in the number of private game ranches established in South Africa in recent years. These ranches are good for conservation but many are driven by the profit motive. A number of models have been used to help managers formulate strategies for achieving their economic objectives. These models are discussed and their use illustrated.A detailed sex and age structured model is presented first with an illustration of its use in attaining two different management objectives. For a given management objective this model generates the returns per unit of food consumed for each species. These returns are then fed into a model to determine the relative abundance of each species in a multispecies herbivory that is required to maximise income.Finally, some problems with the use of these models are discussed. Suggestions and current modelling activities towards improving the tools available to African game ranch managers are presented.     

Bayesian Copulae Distributions, with Application to Operational Risk Management

The aim of this paper is to introduce a new methodology for operational risk management, based on Bayesian copulae. One of the main problems related to operational risk management is understanding the complex dependence structure of the associated variables. In order to model this structure in a flexible way, we construct a method based on copulae. This allows us to split the joint multivariate probability distribution of a random vector of losses into individual components characterized by univariate marginals. Thus, copula functions embody all the information about the correlation between variables and provide a useful technique for modelling the dependency of a high number of marginals. Another important problem in operational risk modelling is the lack of loss data. This suggests the use of Bayesian models, computed via simulation methods and, in particular, Markov chain Monte Carlo. We propose a new methodology for modelling operational risk and for estimating the required capital. This methodology combines the use of copulae and Bayesian models.      

A process for generating quantitative belief functions

We present a structured methodology for transforming qualitative preference relationships among propositions into appropriate numeric representations. This approach will be useful in the difficult process of knowledge acquisition from experts on the degree of belief in various propositions or the probability of the truthfulness of those propositions. The approach implicitly (through the qualitative assignments) and explicitly (through the vague interval pairwise comparisons) provides for different levels of preference relationships. Among its advantages, it permits the expert to: explore the given problem situation, using linguistic quantifiers; avoid the premature use of numeric measures; and identify input data that are inconsistent with the theory of belief functions.     

The role of problem representation and feature knowledge in algebraic equation-solving

Domain experts have two major advantages over novices with regard to problem solving: experts more accurately encode deep problem features (feature encoding) and demonstrate better conceptual understanding of critical problem features (feature knowledge). In the current study, we explore the relative contributions of encoding and knowledge of problem features (e.g., negative signs, the equals sign, variables) when beginning algebra students solve simple algebraic equations. Thirty-two students completed problems designed to measure feature encoding, feature knowledge and equation solving. Results indicate that though both feature encoding and feature knowledge were correlated with equation-solving success, only feature knowledge independently predicted success. These results have implications for the design of instruction in algebra, and suggest that helping students to develop feature knowledge within a meaningful conceptual context may improve both encoding and problem-solving performance.     

Modelling in the absence of data: a case study of fleet maintenance in a developing country

Adequate and relevant objective data for modelling maintenance decision problems are often incomplete or not readily accessible. This is particularly true in developing countries. In this paper the experience gained between 1991–95 in conducting a maintenance study of an inter-city express bus fleet in a developing country is presented. The lack of available maintenance records and operating data rendered the study the most data-starved maintenance modelling exercise the authors have met before or since. The study required the use of subjective methods to both define the problem and to estimate parameters, and the application of recently developed concepts in maintenance modelling along with snapshot analysis and delay time modelling. This imposed a structured approach to problem recognition and problem solution. The study contributed both directly and indirectly to a change in work culture and to a reduction in bus breakdown rate. The company was re-visited 5?years later specifically to seek evidence of lasting impact. Some evidence existed and is reported in the paper.     

Regional development assessment: A structural equation approach

We propose a multivariate statistical framework for regional development assessment based on structural equation modelling with latent variables and show how such methods can be combined with non-parametric classification methods such as cluster analysis to obtain development grouping of territorial units. This approach is advantageous over the current approaches in the literature in that it takes account of distributional issues such as departures from normality in turn enabling application of more powerful inferential techniques; it enables modelling of structural relationships among latent development dimensions and subsequently formal statistical testing of model specification and testing of various hypothesis on the estimated parameters; it allows for complex structure of the factor loadings in the measurement models for the latent variables which can also be formally tested in the confirmatory framework; and enables computation of latent variable scores that take into account structural or causal relationships among latent variables and complex structure of the factor loadings in the measurement models. We apply these methods to regional development classification of Slovenia and Croatia.     

Pre-service teachers' free and structured mathematical problem posing

This exploratory study examined how pre-service teachers (PSTs) pose mathematical problems for free and structured mathematical problem-posing conditions. It was hypothesized that PSTs would pose more complex mathematical problems under structured posing conditions, with increasing levels of complexity, than PSTs would pose under free posing conditions, because the structured posing condition would guide PSTs to more closely consider the mathematical relationships in a posing situation. Sixty-five PSTs – 61 participating in a written assessment and 4 participating in task-based interviews – responded to problem-posing tasks under free or structured posing conditions. Two-way independent samples t-tests and chi-square tests were used to test the hypothesis, along with a qualitative analysis of the task-based interviews. We found that while the task format had limited impact on the complexity of problems posed, PSTs in the structured-posing condition may have more closely attended to the mathematical concepts in each task, and may have also impacted their process of posing problems than those in the free posing condition.