The prediction of a protein and nucleic acid structure: Problems and prospects

Recent advances in DNA and protein-sequencing technologies have made an increasing number of primary structures available for theoretical investigations. The prediction of a higher-order protein, and nucleic acid structure in particular, is an area where computational approaches will be able to complement the lack of experimental observations. We review some of the problems related to structure predictions: sequence homology searches, secondary structure prediction in RNAs, and regular structure prediction in proteins. The first two are mathematically well-defined problems, for it is not usually necessary to consider long-range interactions. The solution to a smaller segment is a part of the solution to the entire sequence. Thus, the problem can be solved by dynamic programming algorithms. The prediction of protein structures poses a more complex combinatorial problem, as illustrated in our statistical mechanical treatment. A promising approximation is to calculate locally optimal structures stabilized by relatively short-range interactions, and then to include longer-range effects as interactions between the locally optimal structures.     

Use of invented algorithms by second graders in a reform mathematics curriculum

Second-grade students in three schools were individually tested on multidigit addition and subtraction problems and solution procedures observed. The schools were all using a reform mathematics curriculum (UCSMP) with an emphasis on problem solving in broader mathematical contexts. Both contextualized and bare computation problems were included in these interviews. On all but one problem, more students used a mental procedure than used the standard written algorithms, and both methods were used with about the same degree of accuracy. Although the standard school algorithm was the only written algorithm used, a number of different mental procedures were employed by students, and choice appeared to be influenced by characteristics of the problems (magnitude of the numbers or the need for regrouping). Major differences between the three schools were found, which are linked to instruction.     

High‐School Students' Approaches to Solving Algebra Problems that are Posed Symbolically: Results from an Interview Study

A cross‐curricular structured‐probe task‐based clinical interview study with 44 pairs of third year high‐school mathematics students, most of whom were high achieving, was conducted to investigate their approaches to a variety of algebra problems. This paper presents results from three problems that were posed in symbolic form. Two problems are TIMSS items (a linear inequality and an equation involving square roots). The other problem involves square roots. We found that the majority of student pairs used symbol manipulation when solving the problems, and while many students seemed to prefer symbolic over graphical and tabular representations in their first attempt at solving the problems, we found that it was common for student pairs to use more than one strategy throughout the course of their solving. Students' use of graphing calculators to solve the problems is discussed.     

A Lagrangian relaxation approach to solve the second phase of the exam scheduling problem

The problem of scheduling final exams at a large university can be viewed as a three phase process. The first phase consists of grouping the exams into sets called exam blocks. The second phase deals with the assignment of exam blocks to exam days and the third phase consists of arranging the exam days and also arranging the blocks within days.In this paper, we present new integer programming formulations for the second phase of the scheduling problem. We present an integer program with a single objective of minimizing the number of students with two or more exams per day. We then present a Lagrangian relaxation based solution procedure to solve this problem. Further, we present a bicriterion integer programming formulation to minimize the number of students with two exams per day and the number of students with three exams per day. Finally, we present some computational experience using randomly generated problems as well as real world data obtained from the State University of New York at Buffalo.     

Fifth-grade students�� approaches to and beliefs of mathematics word problem solving: a large sample Hungarian study

Mathematical word problems used in Verschaffel et al.??s (Learning and Instruction 7:339?C359, 1994) study were applied in several follow-up studies. The goal of the present study was to replicate and extend the results of this line of research in a large sample of Hungarian students using an alternative set of data-gathering and data-analysis techniques. 4,037 students forming a nationwide representative sample of the Hungarian fifth-grade student population (aged 10?C11) completed the test. The test contained five word problems from the list of 10 P(??problematic)-items from Verschaffel et al.??s test. In contrast to all previous research in this domain, we used a multiple-choice format, where three options were given for each task: (a) routine-based, non-realistic answer, (b) numerical response that does take into account realistic considerations, (c) a realistic solution stating that the task cannot be solved. The hypotheses of this study were: (1) Students?? responses will confirm previous results, i.e. upper elementary school students prefer to respond to P-items by means of the routine-based answer; (2) Most students will demonstrate a more or less consistent preference for a given answer type (a, b or c) over problems; (3) Students?? school math marks will have low correlation indices with students?? achievement on these word problems. Our results confirm student??s overall tendency to follow non-realistic approaches when doing school word problem solving. The tendency even holds when confronting students with various kinds of realistic answers. Our results show that students demonstrate response patterns over problems, and that the correlation with math school performance is significant but small.     

The effects of meta-cognitive instruction on third and sixth graders solving word problems

This study examined the differential effects of a meta-cognitive instruction, called IMPROVE, on third and sixth graders’ solution of word problems. In particular, the study focused on the solution of two kinds of word problems: with consistent and with inconsistent language. Participants were 194 Israeli students who studied in third (N = 110) and sixth (N = 84) grades. All students were administered pre- and post-tests constructed of 16 word problems with consistent and inconsistent language. About half of the students within each grade level were exposed to IMPROVE and the others studied under a ‘traditional’ teaching method. The findings indicate that at both grade levels the IMPROVE students significantly outperformed their counterparts in the control group, but third graders benefited from IMPROVE more than sixth graders. In addition, the study indicates that the gap in achievement between IMPROVE and control groups was larger on word problems with inconsistent language compared to word problems with consistent language. The theoretical and practical implications of the study are discussed.     

Mathematical Problem-Solving Processes of Elementary Male and Female Students

This study examined the problem solving behavior of young elementary school students. Ninety second-and third-grade students were observed and interviewed as they solved three problems. Conclusions included that the children readily attempted unfamiliar problems and that they used systematic solution processes. No significant gender differences were observed.     

新中考中的排课问题

为提高初中学业水平和综合素质教育,近几年有些地方中考进行改革,要求学生进行学科选择。新中考课程分为选修和必修两大类。必修课程为语文、数学、英语,选修课程为其他六门课程,从中选出三项。把最后总成绩作为中考录取的标准。跟传统排课不同,这里排课要求每人一张课表,问题变得复杂困难。本文以北京某初三课程为例,研究了新中考的排课问题。一般情况下约束和变量是上百万级的,无法求解。本文利用整数规划建模,然后把该问题转化成多阶段问题,每个阶段给出小问题的解,这样使得问题的求解变得可行。最终的排课结果,仅比预期增加三位老师就可以实现新中考的排课问题。本文的求解过程给新中考排课带来了新的启发。     

Evolutionary,constructive and hybrid procedures for the bi-objective set packing problem

The bi-objective set packing problem is a multi-objective combinatorial optimization problem similar to the well-known set covering/partitioning problems. To our knowledge and surprise, this problem has not yet been studied whereas several applications have been reported. Unfortunately, solving the problem exactly in a reasonable time using a generic solver is only possible for small instances. We designed three alternative procedures for approximating solutions to this problem. The first is derived from the original ‘Strength Pareto Evolutionary Algorithm’, which is a population-based metaheuristic. The second is an adaptation of the ‘Greedy Randomized Adaptative Search Procedure’, which is a constructive metaheuristic. As underlined in the overview of the literature summarized here, almost all the recent, effective procedures designed for approximating optimal solutions to multi-objective combinatorial optimization problems are based on a blend of techniques, called hybrid metaheuristics. Thus, the third alternative, which is the primary subject of this paper, is an original hybridization of the previous two metaheuristics. The algorithmic aspects, which differ from the original definition of these metaheuristics, are described, so that our results can be reproduced. The performance of our procedures is reported and the computational results for 120 numerical instances are discussed.     

A stochastic local search algorithm with adaptive acceptance for high-school timetabling

Automating high school timetabling is a challenging task. This problem is a well known hard computational problem which has been of interest to practitioners as well as researchers. High schools need to timetable their regular activities once per year, or even more frequently. The exact solvers might fail to find a solution for a given instance of the problem. A selection hyper-heuristic can be defined as an easy-to-implement, easy-to-maintain and effective ‘heuristic to choose heuristics’ to solve such computationally hard problems. This paper describes the approach of the team hyper-heuristic search strategies and timetabling (HySST) to high school timetabling which competed in all three rounds of the third international timetabling competition. HySST generated the best new solutions for three given instances in Round 1 and gained the second place in Rounds 2 and 3. It achieved this by using a fairly standard stochastic search method but significantly enhanced by a selection hyper-heuristic with an adaptive acceptance mechanism.     

Two-Way Tables: Issues at the Heart of Statistics and Probability for Students and Teachers

Some problems exist at the intersection of statistics and probability, creating a dilemma in relation to the best approach to assist student understanding. Such is the case with problems presented in two-way tables representing conditional information. The difficulty can be confounded if the context within which the problem is set is one where students have preconceived opinions on the direction of the potential association present. This article considers school students’ responses to two problems of association, with data presented in 2 × 2 tables. A hierarchical rubric is presented to document students’ understandings. Teachers’ pedagogical content knowledge is also considered in relation to the same two problems. Findings include a surprising relationship of outcomes for students across the problem contexts and some concern about teachers’ pedagogical content knowledge in this area of the curriculum.     

Children's construction of mathematical knowledge in solving novel isomorphic problems in concrete and written form

The focus of this article is children's construction and analogical transfer of mathematical knowledge during novel problem solving, as reflected in their strategies for dealing with isomorphic combinatorial problems presented in “hands-on” and written form. Case studies of low- and high-achieving 9-year-olds in school mathematics serve to illustrate a general progression through three identified stages of strategy construction. The important role of domain-general strategies in this development is highlighted. Included in the study's findings is the fact that achievement level in school mathematics does not predict children's attainment of the third stage, as is evidenced by the low-achieving student's construction of sophisticated combinatorial knowledge and the high-achieving student's failure to do so. Children's ability to recognize structural correspondence between the two isomorphic problem sets and the extent to which this facilitates problem solution are also reported.     

Surjections‐‐a classroom discussion

Learning pure mathematics through problem solving, group work and classroom discussion can be very motivating for students provided that they are given suitable problems, and appropriate guidance and instruction. Problems should be simple to state and they should yield some results easily but have very much more challenging components so that, with some early success, students will have the confidence and determination to learn new mathematics if necessary in order to reach a final solution. Students can be encouraged to work in groups and then to compare the advantages and disadvantages of different approaches. The problem outlined here is one suitable for undergraduates, and for very able school students, involving the definition of a function and simple combinatorics. It can be solved by a variety of methods involving algebraic expansions using the multinomial theorem, or solution of sets of linear equations using matrices and inverse matrices, or the inclusion exclusion formula as applied to the number of elements in the union of sets.     

A Fast,Network-based,Hybrid Heuristic for the Assignment of Students to Schools

In an era of declining and fluctuating enrolments, the determination of appropriate school sizes and student assignments poses difficult problems for administrators. For two decades, researchers have worked with variants of a linear programming districting model which minimizes total weighted distance as all students are assigned to schools constrained by both capacity and racial balance limitations. This model could aid in the generation of districting alternatives, but is difficult to solve in realistic applications with generalized solution procedures because of overall problem size. This has prompted the development of fast, network-based models, solvable with special algorithms and codes. We show, however, that these approaches all contain major shortcomings. We present a fast, hybrid heuristic based upon two network representations. Computational results demonstrate this to be a very fast solution approach for the general case of this districting problem.     

A Case Study of the Effectiveness of Teacher Experience in the Use of Explanation‐Based Assessment in High School Physics

This article reports on the results of a study involving an innovative assessment program initiated to investigate student predictions and revised explanations regarding a variety of optical phenomena. The assessments were administered via videotape to two classes of high school physics students from different high schools. Two high school teachers with similar educational and teaching backgrounds administered the tapes to the two groups of students. The school environments and the ability levels of the two student groups were similar. The students of the teacher with greater experience with this new form of assessment provided considerably more substantive explanations to the phenomena presented on the videos. The results of the case study suggest that the introduction of new forms of assessment in science education, although desirable, will require significant reordering of the goals and strategies of science teaching.     

Review and comparison of three methods for the solution of the car sequencing problem

The car sequencing problem is the ordering of the production of a list of vehicles which are of the same type, but which may have options or variations that require higher work content and longer operation times for at least one assembly workstation. A feasible production sequence is one that does not schedule vehicles with options in such a way that one or more workstations are overloaded. In variations of the problem, other constraints may apply. We describe and compare three approaches to the modeling and solution of this problem. The first uses integer programming to model and solve the problem. The second approaches the question as a constraint satisfaction problem (CSP). The third method proposes an adaptation of the Ant Colony Optimization for the car sequencing problem. Test-problems are drawn from CSPLib, a publicly available set of problems available through the Internet. We quote results drawn both from our own work and from other research. The literature review is not intended to be exhaustive but we have sought to include representative examples and the more recent work. Our conclusions bear on likely research avenues for the solution of problems of practical size and complexity. A new set of larger benchmark problems was generated and solved. These problems are available to other researchers who may wish to solve them using their own methods.     

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.     

Understanding and representing the arithmetic averaging algorithm: an analysis and comparison of US and Chinese students' responses

Analysing the responses of 311 sixth-grade Chinese students and 232 sixth-grade US students to two problems involving arithmetic average, this study explored students' understanding and representation of the averaging algorithm from a cross-national perspective. Results of the study show that Chinese students were more successful than US students in obtaining correct numerical answers to each of the problems, but US and Chinese students had similar cognitive difficulties in solving the second task. The difficulties were not due to their lack of procedural knowledge of the averaging algorithm, rather due to their lack of conceptual understanding of the algorithm. There were significant differences between the US and Chinese students in their solution representations of the two average problems. Chinese students were more likely to use algebraic representations than US students; while US students were more likely to use pictorial or verbal representations. US and Chinese students' use of representations are related to their mathematical problem-solving performance. Students who used more advanced representations were better problem solvers. The findings of the study suggest that Chinese students' superior performance on the averaging problems is partly due to their use of advanced representations (e.g. algebraic).