This research is designed to gain an understanding of how accounting students respond to realistic, business ethical dilemmas. Prior research suggests that accounting students exhibit lower levels of ethical reasoning compared to other business and non-business majors. This study uses the Defining Issues Test, Version 2 (Rest, et al., 1999) to measure accounting students' ethical reasoning processes. The Mach IV scale (Christie and Geis, 1970) is used to measure moral behavior. Eight ethical vignettes adapted from prior ethics studies represent realistic, business ethical scenarios. A total of sixty-eight undergraduate accounting students are used to examine three hypotheses. Literature suggests that individuals with lower ethical reasoning levels are more likely to agree with unethical behavior. Therefore, hypothesis one investigates the relationship between ethical reasoning and ethical decision making. Literature also suggests that individuals agreeing with Machiavellian statements are more likely to agree with questionable activities. Hypothesis two investigates the relationship between Machiavellian behavior and ethical decision making. Prior gender literature suggests that gender influences ethical decision making, with females being more ethical than males. Therefore, hypothesis three examines whether female accounting students agree less with questionable activities compared to males. Results indicate that ethical reasoning is significantly correlated with students' ethical ratings on the business vignettes. Similarly, Machiavellian behavior is significantly correlated with students' ethical ratings. Consistent with prior gender literature, females agree less with questionable activities compared to male accounting students. / Ph. D.

Early algebraic reasoning can be viewed as developing a bridge between arithmetic and algebra. Accordingly, this research examines how middle-grades students' arithmetic reasoning, classified by their number sequences, can be used to model their algebraic reasoning as it pertains to generalizing, writing, and solving linear equations and systems of equations. In the quantitative phase of research, 326 students in grades six through nine completed a survey to assess their number sequence construction. In the qualitative phase, 18 students participated in clinical interviews, the purpose of which was to elicit their algebraic reasoning. Results show that the numbers of students who had constructed the two least sophisticated number sequences did not change significantly across grades six through nine. In contrast, the numbers of students who had constructed the three most sophisticated number sequences did change significantly from grades six and seven to grades eight and nine. Furthermore, students did not consistently reason algebraically unless they had constructed at least the fourth number sequence. Thus, it is concluded that students with the two least sophisticated number sequences are no more prepared to reason algebraically in ninth grade than they were in sixth. / Doctor of Philosophy / Early algebraic reasoning can be viewed as developing a bridge between arithmetic and algebra. This study examines how students in grades six through nine reason about numbers, and whether their reasoning about numbers can be used to explain how they reason on algebra tasks. Particularly, the students were asked to extend numerical patterns by writing algebraic expressions, and were asked to read contextualized word problems and write algebraic equations and systems of equations to represent the problems. In the first phase of research, 326 students completed a survey to assess their understanding of numbers and their ability to reason about numbers. In the second phase, 18 students participated in interviews, the purpose of which was to elicit their algebraic reasoning. Results show that the numbers of students who had constructed a more sophisticated understanding of number did not change significantly across grades six through nine. In contrast, the numbers of students who had constructed a less sophisticated understanding of number did change significantly from grades six and seven to grades eight and nine. Furthermore, students were not consistently successful on algebraic tasks unless they had constructed a more sophisticated understanding of number. Thus, it is concluded that students with an unsophisticated understanding of number are no more prepared to reason algebraically in ninth grade than they were in sixth.

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School-mathematics has been shown to mainly comprise rote-learning of procedures where the considerations of intrinsic mathematical properties are scarce. At the same time theories and syllabi emphasize competencies like problem solving and reasoning. This thesis will therefore concern how task design can influence the reasoning that students apply when solving tasks, and how the reasoning during practice is associated to students’ results, cognitive capacity, and brain activity. In studies 1-3, we examine the efficiency of different types of reasoning (i.e., algorithmic reasoning (AR) or creative mathematically founded reasoning (CMR)) in between-groups designs. We use mathematics grade, gender, and cognitive capacity as matching variables to get similar groups. We let the groups practice 14 different solution methods with tasks designed to promote either AR or CMR, and after one week the students are tested on the practiced solution methods. In study 3 the students did the test in and fMRI-scanner to study if the differing practice would yield any lasting differences in brain activation. Study 4 had a different approach and focused details in students’ reasoning when working on teacher constructed tasks in an ordinary classroom environment. Here we utilized audio-recordings of students’ solving tasks, together with interviews with teachers and students to unravel the reasoning sequences that students embark on. The turning points where the students switch subtask and the reasoning between these points were characterized and visualized. The behavioral results suggest that CMR is more efficient than AR, and also less dependent on cognitive capacity during the test. The latter is confirmed by fMRI, which showed that AR had higher activation than CMR in areas connected to memory retrieval and working memory. The behavioral result also suggested that CMR is more beneficial for cognitively less proficient students than for the high achievers. Also, task design is essential for both students’ choice of reasoning and task progression. The findings suggest that: 1) since CMR is more efficient than AR, students need to encounter more CMR, both during task solving and in teacher presentation, 2) cognitive capacity is important but depending on task design, cognitive strain will be more or less high during test situations, 3) although AR-tasks does not prohibit the use of CMR they make it less likely to occur. Since CMR-tasks can emphasize important mathematical properties, are more efficient than AR- tasks, and more beneficial for less cognitively proficient students, promoting CMR can be essential if we want students to become mathematically literate.

In this dissertation, we investigate how commonsense reasoning can be formalized by using extended logic programs. In this investigation, we first use extended logic programs to formalize inheritance hierarchies with exceptions by adopting McCarthy's simple abnormality formalism to express uncertain knowledge. In our representation, not only credulous reasoning can be performed but also the ambiguity-blocking inheritance and the ambiguity-propagating inheritance in skeptical reasoning are simulated. In response to the anomalous extension problem, we explore and discover that the intuition underlying commonsense reasoning is a kind of forward reasoning. The unidirectional nature of this reasoning is applied by many reformulations of the Yale shooting problem to exclude the undesired conclusion. We then identify defeasible conclusions in our representation based on the syntax of extended logic programs. A similar idea is also applied to other formalizations of commonsense reasoning to achieve such a purpose.

Thesis advisor: Elida V. Laski / This research explored the roles of symbolic mapping and relational thinking in early reading and mathematics learning. It examined whether symbolic mapping and relational thinking were predictive of children’s reading and mathematics knowledge; the extent to which these domain-general cognitive scores explained correlations between the two domains; and whether these cognitive scores mediated relations between verbal intelligence and reading and mathematics. Furthermore, the present research explored whether home learning experiences were predictive of children’s symbolic, relational, reading, and mathematics scores. Participants in Study 1 were 86 preschool children from the Boston area. Children completed an assessment of verbal intelligence and a range of symbolic, relational, reading, and mathematics measures. Results showed that reading and mathematics scores were highly correlated; symbolic and relational scores were predictive of domain-specific performance; and symbolic and relational thinking mediated relations between verbal intelligence and reading and mathematics knowledge. These findings suggest that symbolic mapping and relational thinking may provide foundational cognitive skills that support early learning. Study 2 investigated whether home learning experiences were related to children’s symbolic, relational, reading, and mathematics scores. Participants were the 86 parents of children from Study 1. Parents reported the frequency with which they and their child engaged in various activities. Findings showed a significant relation between symbolic learning experiences and children’s reading and mathematics scores, but no relations between learning experiences and children’s symbolic or relational scores. There was a strong association between parents’ beliefs about the importance of mathematics for kindergarten readiness and children’s reading and mathematics scores. The results suggest that homes rich in symbolic learning experiences may best support children’s early learning, but parental beliefs about mathematics may differentiate highly effective and less effective learning environments. Taken together, these two studies contribute to our understanding of the constructs of symbolic and relational thinking as foundations for early learning in reading and mathematics. Findings are discussed in terms of their implications for improving school readiness via increased intentionality in early educational activities. / Thesis (PhD) — Boston College, 2016. / Submitted to: Boston College. Lynch School of Education. / Discipline: Counseling, Developmental and Educational Psychology.

One of the most fundamental functions of human cognition is to parse an otherwise chaotic world into different kinds of things. The ability to learn what objects are and how to respond to them appropriately is essential for daily living. The literature has presented contrasting evidence about the role of perpetual features such as artifact appearance versus causal or inductive reasoning in chldren's category distinctions (e.g., function). The present project used a child-initiated inquiry paradigm to investigate how children conceptualize artifacts, specifically how they prioritize different types of information that typify not only novel but also familiar objects. Results underscore a hybrid model in which perceptual features and deeper properties act synergistically to inform children's artifact conceptualization. Function, however, appears to be the driving force of this relationship. / by Patricia P. Schultz. / Thesis (M.A.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

This report describes a paradigm for combining associational and causal reasoning to achieve efficient and robust problem-solving behavior. The Generate, Test and Debug (GTD) paradigm generates initial hypotheses using associational (heuristic) rules. The tester verifies hypotheses, supplying the debugger with causal explanations for bugs found if the test fails. The debugger uses domain-independent causal reasoning techniques to repair hypotheses, analyzing domain models and the causal explanations produced by the tester to determine how to replace faulty assumptions made by the generator. We analyze the strengths and weaknesses of associational and causal reasoning techniques, and present a theory of debugging plans and interpretations. The GTD paradigm has been implemented and tested in the domains of geologic interpretation, the blocks world, and Tower of Hanoi problems.

The law too often avoids or misuses statistical evidence. This problem is partially explained by the absence of a shared normative framework for working with such evidence. There is considerable disagreement within the legal community about how statistical evidence relates to legal inquiry. It is proposed that the first step to addressing the problem is to accept Bayesianism as a normative framework that leads to outcomes that largely align with legal intuitions. It is only once this has been accepted that we can proceed to encourage education about common conceptual errors involving statistical evidence as well as techniques to limit their occurrence. Objections to using Bayesianism in the legal context are addressed. It is argued that the objection based on the irrelevance of statistical evidence is fundamentally incoherent in its failure to identify most evidence as statistical. Second, objections to the incompleteness of a Bayesian approach in accounting for non-truth-related values do place legitimate limits on the use of Bayesianism in the law but in no way undermine its normative usefulness. Lastly, many criticisms of the role of Bayesianism in the law rest on misunderstandings of the meaning and manipulation of statistical evidence and are best addressed by presenting statistical evidence in ways that encourage correct understanding. Once it is accepted that, put in its proper place, a Bayesian approach to understanding statistical evidence can align with most fundamental legal intuitions, a less fearful approach to the use of statistical evidence in the law can emerge.

The law too often avoids or misuses statistical evidence. This problem is partially explained by the absence of a shared normative framework for working with such evidence. There is considerable disagreement within the legal community about how statistical evidence relates to legal inquiry. It is proposed that the first step to addressing the problem is to accept Bayesianism as a normative framework that leads to outcomes that largely align with legal intuitions. It is only once this has been accepted that we can proceed to encourage education about common conceptual errors involving statistical evidence as well as techniques to limit their occurrence. Objections to using Bayesianism in the legal context are addressed. It is argued that the objection based on the irrelevance of statistical evidence is fundamentally incoherent in its failure to identify most evidence as statistical. Second, objections to the incompleteness of a Bayesian approach in accounting for non-truth-related values do place legitimate limits on the use of Bayesianism in the law but in no way undermine its normative usefulness. Lastly, many criticisms of the role of Bayesianism in the law rest on misunderstandings of the meaning and manipulation of statistical evidence and are best addressed by presenting statistical evidence in ways that encourage correct understanding. Once it is accepted that, put in its proper place, a Bayesian approach to understanding statistical evidence can align with most fundamental legal intuitions, a less fearful approach to the use of statistical evidence in the law can emerge.