High School Biology Students' Knowledge and Certainty about Diffusion and Osmosis Concepts

The purpose of this study was to investigate students' understanding about scientifically acceptable content knowledge by exploring the relationship between knowledge of diffusion and osmosis and the student's certainty in their content knowledge. Data was collected from a high school biology class with the Diffusion and Osmosis Diagnostic Test (DODT) and Certainty of Response (CRI) scale. All data was collected after completion of a unit of study on diffusion and osmosis. The results of the DODT were dichotomized into correct and incorrect answers, and CRI values were dichotomized into certain and uncertain. Values were used to construct a series of 2 × 2 contingency tables for each item on the DODT and corresponding CRI. High certainty in incorrect answers on the DODT indicated tenacious misconceptions about diffusion and osmosis concepts. Low certainty in incorrect or correct answers on the DODT indicated possible guessing; and, therefore no understanding, or confusion about their understanding. Chi‐square analyses revealed that significantly more students had misconceptions than desired knowledge on content covering the Influence of Life Forces on Diffusion and Osmosis, Membranes, the Particulate and Random Nature of Matter, and the Processes of Diffusion and Osmosis. Most students were either guessing or had misconceptions about every item related to the concepts osmosis and tonicity. Osmosis and diffusion are important to understanding fundamental biology concepts, but the concept of tonicity not be introduced to high school biology students until effective instructional approaches can be identified by researchers.     

Shift error detection in standardized exams

Hundreds of millions of multiple choice exams are given every year in the United States. These exams permit form-filling shift errors, where an absent-minded mismarking displaces a long run of correct answers. A shift error can substantially alter the exam's score, and thus invalidate it.In this paper, we develop algorithms to accurately detect and correct shift errors, while guaranteeing few false detections. We propose a shift error model, and probabilistic methods to identify shifted exam regions.We describe the results of our search for shift errors in undergraduate Stony Brook exam sets, and in over 100,000 Scholastic Amplitude Tests. These results suggest that approximately 2% of all tests contain shift errors. Extrapolating these results over all multiple choice exams and forms leads us to conclude that exam takers make millions of undetected shift errors each year.Employing probabilistic shift correcting systems is inherently dangerous. Such systems may be taken advantage of by clever examinees, who seek to increase the probability of correct guessing. We conclude our paper with a short study of optimal guessing strategies when faced with a generous shift error correcting system.     

Bayesian knowledge base tuning

For a knowledge-based system that fails to provide the correct answer, it is important to be able to tune the system while minimizing overall change in the knowledge-base. There are a variety of reasons why the answer is incorrect ranging from incorrect knowledge to information vagueness to incompleteness. Still, in all these situations, it is typically the case that most of the knowledge in the system is likely to be correct as specified by the expert (s) and/or knowledge engineer (s). In this paper, we propose a method to identify the possible changes by understanding the contribution of parameters on the outputs of concern. Our approach is based on Bayesian Knowledge Bases for modeling uncertainties. We start with single parameter changes and then extend to multiple parameters. In order to identify the optimal solution that can minimize the change to the model as specified by the domain experts, we define and evaluate the sensitivity values of the results with respect to the parameters. We discuss the computational complexities of determining the solution and show that the problem of multiple parameters changes can be transformed into Linear Programming problems, and thus, efficiently solvable. Our work can also be applied towards validating the knowledge base such that the updated model can satisfy all test-cases collected from the domain experts.     

Correcting grade deflation caused by multiple-choice scoring

In a study involving three sections of 181 pre-calculus students at a four-year college, partial credit scoring on multiple-choice questions was examined over an entire semester. Questions were constructed by experienced teachers to pose carefully designed, incorrect alternatives, many of which implicitly suggested varying degrees of partial knowledge or understanding. Computing students' course grades based solely on percentage correct scoring resulted in a course-wide GPA of 1.38. This seemed inconsistent with the fact that students were screened for entry into the course. To avert apparent grade deflation due to such right/wrong scoring, student scores were recomputed based on supplementing percentage correct scores with partial credit. This resulted in a course-wide increase in GPA to 1.92. Using that same letter grade distribution, and curving percentage correct averages to achieve the 1.92 GPA, it was found that 10% of the students were given different grades from those determined by the partial credit scoring. Regarding the efficacy of awarding partial credit points, it was found that partial credit by itself measured achievement of relevant mathematical skills that were missed by percentage correct scoring. Overall, grades determined by partial credit scoring seemed more reflective of both the quantity and quality of student knowledge than grades determined by either traditional percentage correct scoring or any corrective curving.     

Benford's law and distractors in multiple choice exams

Suppose that in a multiple choice examination the leading significant digit of the correct options follows Benford's Law, while the leading digit of the distractors is uniform. Consider a strategy for guessing at answers that selects the option with the lowest leading digit with ties broken at random. We provide an expression for both the probability that this strategy selects the correct option and also the generalization to the probability of selecting the option with the lowest r significant digit string.     

A Dynamic Programming Solution to a Problem of Loom Box Sequence Planning

A drop box loom has a vertical rack of four boxes on each side of the loom to hold the shuttles. When a weft pick is inserted there is a choice of empty boxes to receive the shuttle. Choosing the empty box to be used at each pick of the pattern is termed "the loom box sequence problem". For reasons of safety and wear and tear, rack movements are minimized; in particular, movements to the opposite end of the rack are to be avoided. A further constraint is that, to repeat, the shuttles must begin and end the sequence in the same boxes.This problem may be formulated simply in dynamic programming terms, but this leads to a computer storage requirement of 10¹² variables. A practical program using only 16 K storage has been written and the results for a typical problem are presented.     

Pruning boxes in a box-based classification method

In this work we address an extension of box clustering in supervised classification problems that makes use of optimization problems to refine the results obtained by agglomerative techniques. The central concept of box clustering is that of homogeneous boxes that give rise to overtrained classifiers under some conditions. Thus, we focus our attentions on the issue of pruning out redundant boxes, using the information gleaned from the other boxes generated under the hypothesis that such a choice would identify simpler models with good predictive power. We propose a pruning method based on an integer optimization problem and a family of sub problems derived from the main one. The overall performances are then compared to the accuracy levels of competing methods on a wide range of real data sets. The method has proven to be robust, making it possible to derive a more compact system of boxes in the instance space with good performance on training and test data.     

Certificates of infeasibility via nonsmooth optimization

An important aspect in the solution process of constraint satisfaction problems is to identify exclusion boxes which are boxes that do not contain feasible points. This paper presents a certificate of infeasibility for finding such boxes by solving a linearly constrained nonsmooth optimization problem. Furthermore, the constructed certificate can be used to enlarge an exclusion box by solving a nonlinearly constrained nonsmooth optimization problem.     

A space-indexed formulation of packing boxes into a larger box

Current integer programming solvers fail to decide whether 12 unit cubes can be packed into a 1×1×11 box within an hour using the natural relaxation of Chen/Padberg. We present an alternative relaxation of the problem of packing boxes into a larger box, which makes it possible to solve much larger instances.     

On the information about individual utilities used in social choice

This paper investigates the consequences of the requirement tha a social choice method should convey as much information as possible about the individuals' values. It is first shown that if interpersonal comparisons of utilities are excluded, then the only information about individual values that can be used in the context of social choice is ordinal information. It is then argued that Arrow's independence condition demands that only a part of the information about the individual preferences be used in the social choice. Finally, it is shown that the requirement of maximal information gives strong support to Smith's (1973) ‘separability’ condition. In combination with Smith's result this shows that the choice methods that convey the most information about individual preferences are the ‘generalized point systems’ (also called ‘scoring functions’ or ‘ranking systems’).     

学英语（英文）

<正>There are 7questions in total,presenting various different question types.While you attempt to resolve the problems,remember to be creative.During accomplishing these flexible mathematical exercises,you can inspire your mathematical thinking.1.If box A can fit inside box B,box C can fit box D,and boxes B and the same size,which of     

Reply to the note on article “The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees”

In a very recent note by Gao and Ni [B. Gao, M.F. Ni, A note on article “The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees”, European Journal of Operational Research, in press, doi:10.1016/j.ejor.2007.10.0381], they argued that Yen’s combination rule [J. Yen, Generalizing the Dempster–Shafer theory to fuzzy sets, IEEE Transactions on Systems, Man and Cybernetics 20 (1990) 559–570], which normalizes the combination of multiple pieces of evidence at the end of the combination process, was incorrect. If this were the case, the nonlinear programming models we proposed in [Y.M. Wang, J.B. Yang, D.L. Xu, K.S. Chin, The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees, European Journal of Operational Research 175 (2006) 35–66] would also be incorrect. In this reply to Gao and Ni, we re-examine their numerical illustrations and reconsider their analysis of Yen’s combination rule. We conclude that Yen’s combination rule is correct and our nonlinear programming models are valid.     

Analysis of a multi-category classifier

The use of boxes for pattern classification has been widespread and is a fairly natural way in which to partition data into different classes or categories. In this paper we consider multi-category classifiers which are based on unions of boxes. The classification method studied may be described as follows: find boxes such that all points in the region enclosed by each box are assumed to belong to the same category, and then classify remaining points by considering their distances to these boxes, assigning to a point the category of the nearest box. This extends the simple method of classifying by unions of boxes by incorporating a natural way (based on proximity) of classifying points outside the boxes. We analyze the generalization accuracy of such classifiers and we obtain generalization error bounds that depend on a measure of how definitive is the classification of training points.     

Accurate and Inaccurate Conceptions About Osmosis That Accompanied Meaningful Problem Solving

This study focused on the knowledge of six outstanding science students who solved an osmosis problem meaningfully. That is, they used appropriate and substantially accurate conceptual knowledge to generate an answer. Three generated a correct answer; three, an incorrect answer. This paper identifies both the accurate and inaccurate conceptions about osmosis of each correct and incorrect solver. The investigation consisted of a presolving clinical interview, think-aloud solving of the problem, and retrospective report of the solving. Of the 12 accurate conceptions identified here, two were especially important in enabling these solvers to generate a correct answer. Of the 8 inaccurate conceptions, either of 2 blocked a correct answer. Four, however, accompanied (and could therefore be concealed by) a correct answer. Teachers could use this information to make a meaningful solving of this problem accessible to more students and to identify more effectively students' inaccurate conceptions about osmosis.     

Sloane's box stacking problem

Recently, Sloane suggested the following problem: We are given n boxes, labeled 1,2,…,n. For i=1,…,n, box i weighs (m-1)i grams (where m?2 is a fixed integer) and box i can support a total weight of i grams. What is the number of different ways to build a single stack of boxes in which no box will be squashed by the weight of the boxes above it? Prior to this generalized problem, Sloane and Sellers solved the case m=2. More recently, Andrews and Sellers solved the case m?3. In this note we give new and simple proofs of the results of Sloane and Sellers and of Andrews and Sellers, using a known connection with m-ary partitions.     

First order rejection tests for multiple-objective optimization

Three rejection tests for multi-objective optimization problems based on first order optimality conditions are proposed. These tests can certify that a box does not contain any local minimizer, and thus it can be excluded from the search process. They generalize previously proposed rejection tests in several regards: Their scope include inequality and equality constrained smooth or nonsmooth multiple objective problems. Reported experiments show that they allow quite efficiently removing the cluster effect in mono-objective and multi-objective problems, which is one of the key issues in continuous global deterministic optimization.     

Local search for a multi-drop multi-container loading problem

We consider a complex variant of the Container Loading Problem arising from a real-world industrial application. It includes several features such as multiple containers, box rotation, and bearable weight, which are of importance in many practical situations. In addition, it also considers the situation in which boxes have to be delivered to different destinations (multi-drop). Our solution technique is based on local search metaheuristics. Local search works on the space of sequences of boxes to be loaded, while the actual load is obtained by invoking, at each iteration, a specialized procedure called loader. The loader inserts the boxes in the container using a deterministic heuristic which produces a load that is feasible according to the constraints. We test our solver on real-world instances provided by our industrial partner, showing a clear improvement on the previous heuristic solution. In addition, we compare our solver on benchmarks from the literature on the basic container loading problems. The outcome is that the results are in some cases in-line with the best ones in the literature and for other cases they also improve upon the best known ones. All instances and solutions are made available on the web for future comparisons.     

Solving the pallet loading problem

This paper presents new bounds, heuristics, and an exact algorithm for the Pallet Loading Problem (PLP). PLP maximizes the number of boxes placed on a rectangular pallet. All boxes have identical rectangular dimensions and, when placed, must be located completely within the pallet. Boxes may be rotated 90° so long as they are placed with edges parallel to the pallet’s edges. The set of all PLP instances with an area ratio (pallet area divided by box area) less than 101 boxes can be represented by 3,080,730 equivalent classes. Our G5-heuristic finds optimal solutions to 3,073,724 of these 3,080,730 classes and in the remaining 7006 classes only differs from the best known bound by one box. We develop three other heuristics that solve another 54 instances. Finally, we solve the 6952 remaining classes with our exact HVZ algorithm. Only a subset of these classes has been solved previously.     

The minimum size instance of a Pallet Loading Problem equivalence class

The Pallet Loading Problem (PLP) maximizes the number of identical rectangular boxes placed within a rectangular pallet. Boxes may be rotated 90° so long as they are packed with edges parallel to the pallet’s edges, i.e., in an orthogonal packing. This paper defines the Minimum Size Instance (MSI) of an equivalence class of PLP, and shows that every class has one and only one MSI. We develop bounds on the dimensions of box and pallet for the MSI of any class. Applying our new bounds on MSI dimensions, we present an algorithm for MSI generation and use it to enumerate all 3,080,730 equivalence classes with an area ratio (pallet area divided by box area) smaller than 101 boxes. Previous work only provides bounds on the ratio of box dimensions and only considers a subset of all classes presented here.     

Drawing inferences from logit models for panel data

Logit models have been widely used in marketing to predict brand choice and to make inference about the impact of marketing mix variables on these choices. Most researchers have followed the pioneering example of Guadagni and Little, building choice models and drawing inference conditional on the assumption that the logit model is the correct specification for household purchase behaviour. To the extent that logit models fail to adequately describe household purchase behaviour, statistical inferences from them may be flawed. More importantly, marketing decisions based on these models may be incorrect. This research applies White's robust inference method to logit brand choice models. The method does not impose the restrictive assumption that the assumed logit model specification be true. A sandwich estimator of the covariance ‘corrected’ for possible mis‐specification is the basis for inference about logit model parameters. An important feature of this method is that it yields correct standard errors for the marketing mix parameter estimates even if the assumed logit model specification is not correct. Empirical examples include using household panel data sets from three different product categories to estimate logit models of brand choice. The standard errors obtained using traditional methods are compared with those obtained by White's robust method. The findings illustrate that incorrectly assuming the logit model to be true typically yields standard errors which are biased downward by 10–40 per cent. Conditions under which the bias is particularly severe are explored. Under these conditions, the robust approach is recommended. Copyright © 2000 John Wiley & Sons, Ltd.