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Ethical leadership and ethical reasoning in higher education have been the focus of many concerns as universities and colleges attempt to prepare and train educational leaders, particularly in light of high-profile scandals involving educational leaders. Scholars are increasingly interested in why unethical behavior continues to be problematic among leaders. Unethical behavior continues to exist, even though diverse strategies have been incorporated in programs that prepare prospective leaders for leadership roles (i.e., leadership programs and graduate programs). This study addressed the perceptions among community college leaders regarding ethical leadership and ethical reasoning and what guides ethical decision-making among community college leaders. A qualitative study was conducted using a questionnaire designed specifically for this study. The researcher collected data by conducting face-toace interviews with 15 community college leaders in Alabama. The findings of this research demonstrated that community college leaders believe that ethical leadership is more about who the leader is and what a leader does. Additionally, ethical leadership incorporates fairness, integrity, and concern for others into the leadership style. Ethical reasoning among community college leaders tends to be three dimensional; it is about the situation, institutional obligations, and other institutional endeavors. Participants believe that community college leaders in Alabama relate ethics and decision-making to duty and institutional obligations. Research results indicated that the answer to leaders behaving more ethically lies in the quality of programs that train leaders (i.e., graduate programs and leadership training programs). Participants suggested that programs have improved. However, a more comprehensive and intensive concentration on ethics and ethical behavior should be incorporated into graduate and leadership training programs. Specifically, there should be more opportunities to learn from real life ethical case studies and more role playing scenarios.

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Thesis advisor: Liane Young / Effective social interaction requires reasoning about people as generative models. In our day-to-day experience, we come across a remarkable amount of social information, often in the form of other people’s behaviors. Observed behaviors are used to infer agents’ unobservable mental states and traits – the latent causes that drive their behavior. These inferences are stored in person models, which allow us to interpret patterns of observed behaviors across multiple instances and contexts by attributing a common cause to those behaviors, and also allow us to predict people’s future actions, so that we may navigate interactions smoothly and choose our social partners wisely. This dissertation pursued several open questions on flexible trait reasoning. In Paper 1, we found that the relative contributions of different traits to overall impressions may vary depending on what we know about a person. In Paper 2, we found increased neural activity in Theory of Mind regions following the violation of strong and positive prior impressions. In Paper 3, we found that 6-9-year-olds exhibit a negativity bias in impression updating, and older children are sensitive to the strength of behavioral evidence. Overall, we found evidence for flexible trait reasoning – both children and adults were sensitive to the strength and valence of available behavioral evidence, and to the overall inference context. These studies help shed light on how children and adults reason about person models and respond to new social information, and we suggest multiple avenues for further research in this arena. / Thesis (PhD) — Boston College, 2023. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Psychology.

Dual Processing theory proposes that the ability to over ride associative (System 1) in favour of analytical (System 2) processed in deductive reasoning may depend on inhibitory control. The present study applies this association to a spatial reasoning task by adapting a mental rotation task to a multichoice format including System 1 (mirror) and System 2 (rotated image) responses. Fifty undergraduate volunteers from the University of Canterbury responded to a Stroop task as a measure of inhibitory control that was compared with System 1 and System 2 responding from a spatial and a deductive reasoning task. It was expected that people with weaker inhibitory potential would make more System 1 and fewer System 2 responses in both deductive and visual-spatial reasoning tasks. Contrary to expectation System 2 responding dominated for both tasks and correlations between both reasoning tasks and measures of inhibitory control were non-significant. The differing idiosyncratic demands of each task may have obscured any common variables associated with inhibitory control. This research initiated a test for the presence of System 1 and System 2 in spatial reasoning.

The semantic model developed in this research was in response to the difficulty a group of mathematics learners had with conventional mathematical language and their interpretation of mathematical constructs. In order to develop the model ideas from linguistics, psycholinguistics, cognitive psychology, formal languages and natural language processing were investigated. This investigation led to the identification of four main processes: the parsing process, syntactic processing, semantic processing and conceptual processing. The model showed the complex interdependency between these four processes and provided a theoretical framework in which the behaviour of the mathematics learner could be analysed. The model was then extended to include the use of technological artefacts into the learning process. To facilitate this aspect of the research, the theory of instrumentation was incorporated into the semantic model. The conclusion of this research was that although the cognitive processes were interdependent, they could develop at different rates until mastery of a topic was achieved. It also found that the introduction of a technological artefact into the learning environment introduced another layer of complexity, both in terms of the learning process and the underlying relationship between the four cognitive processes.

One reason Qualitative Spatial Reasoning (QSR) is becoming increasingly important to Artificial Intelligence (AI) is the need for a smooth ‘human-like’ communication between autonomous agents and people. The selected, yet general, task motivating the work presented here is the scenario of an object configuration that has to be described by an observer on the ground using only relational object positions. The description provided should enable a second agent to create a map-like picture of the described configuration in order to recognize the configuration on a representation from the survey perspective, for instance on a geographic map or in the landscape itself while observing it from an aerial vehicle. Either agent might be an autonomous system or a person. Therefore, the particular focus of this work lies on the necessity to develop description and reconstruction methods that are cognitively easy to apply for a person. This thesis presents the representation scheme QuaDRO (Qualitative Description and Reconstruction of Object configurations). Its main contributions are a specification and qualitative classification of information available from different local viewpoints into nine qualitative equivalence classes. This classification allows the preservation of information needed for reconstruction nto a global frame of reference. The reconstruction takes place in an underlying qualitative grid with adjustable granularity. A novel approach for representing objects of eight different orientations by two different frames of reference is used. A substantial contribution to alleviate the reconstruction process is that new objects can be inserted anywhere within the reconstruction without the need for backtracking or rereconstructing. In addition, an approach to reconstruct configurations from underspecified descriptions using conceptual neighbourhood-based reasoning and coarse object relations is presented.