Searching for knowledge in the belly of the beast: diversity, social equity and the African graduate student mother's experience in US higher education

Diversity has become a buzz word in public discourse and in educational circles. Higher education institutions in the US have increasingly used this word as a cornerstone of their mission statements and have made increasing efforts to attract students from different backgrounds. As part of the increase in diversity efforts among US colleges, is a significant rise in the number of international students. Attracting international students has become a priority for U.S. universities regardless of size or location. This study examines the intersection between the structure of American educational environment and the blended identities of African Graduate Student Mothers. Within the context of contemporary diversity efforts in US educational institutions, this study examines both the structural environments and the socio-cultural constructs that affect the experiences of African graduate student mothers. Based on a qualitative research interview design, a total of nineteen African graduate student mothers at a Mid-Western University in the US were interviewed individually and in groups over a six weeks period. Results from this study show that apart from the difficult and often dehumanizing treatment African student mothers endure from immigration and consular officials in their various countries and ports of entry, they often find themselves at the margins of their various programs and departments with very little support if any. This is because most of them enroll into graduate programs after arriving as dependants of their spouses; a process that does not allow them to negotiate for departmental commitments and support prior to their arrival. Not only do these women face racial discrimination from white professors, staff and fellow students, but they also experience discrimination and hostilities from African Americans and other minority groups who see them as threats to the limited resources that are often set aside for minority groups in such institutions.

The influence of past behavior on future behavior: a mind-set perspective

A behavioral mind-set refers to the effect of performing a behavior in one situation (e.g., deciding which animals jump higher, dolphins or sea lions) on the likelihood of performing a conceptually similar behavior in subsequent, unrelated situations (e.g., deciding which of two candies to purchase). It reflects the activation and persistence of procedural knowledge. My dissertation circumscribes the construct of a behavioral mind-set and proposes a theoretical framework describing how mind-sets operate as well as their cognitive and motivational determinants. Three sets of studies investigated the role of mind-sets in different domains. The first set of studies explored the influence of making comparative judgments on subsequent decision making. Specifically, I found that making comparative judgment in one situation activates a which-to-buy mind-set that increases the willingness to decide which of two products to purchase in a later situation without considering the option of not buying anything at all. This mind-set can be activated not only by stating preferences for one of two products but also by comparing the relative attractiveness of wild animals, comparing the animals with respect to physical attributes, and estimating how similar one object is to another. Furthermore, the mind-set, once activated, influences not only purchase intentions in hypothetical situations but the actual decisions to purchase one of different types of products that are on sale after the experiment. The second set of studies investigated whether generating supportive elaborations or counterarguments in one situation will influence people’s tendency to engage in similar behavior in a subsequent, unrelated situation. I found that making supportive elaborations in one situation gives rise to a bolstering mind-set that, once activated, increases participants’ disposition to generate supportive thoughts in response to persuasive communications that they receive later and, therefore, increases the effectiveness of persuasion. Correspondingly, generating opposing arguments in an initial situation activates a counterarguing mind-set that increases the tendency to argue against the persuasive communications and decreases its effectiveness. However, a counterarguing mind-set may increase the effectiveness of persuasion if the messages are difficult to be refuted. The third set of studies distinguished between the influence of motivation on consumer behavior and the influence of a mind-set that is activated by this motivation. Specifically, I found that appetitive motivation, which naturally increases people’s tendency to acquire food products, can give rise to a cognition-based acquisition mind-set that increases people’s disposition to acquire non-food products as well. This acquisition mind-set may persist even when the appetitive motivation that gave rise to it is satiated by eating. Moreover, the disposition to acquire non-food products is not mediated by the products’ attractiveness. The studies suggest that motivation and mind-sets may independently influence consumers’ evaluation of a product and their dispositions to acquire it. Motivation is more likely to influence product evaluations whereas a mind-set is more likely to influence consumers’ acquisition dispositions. In summary, a behavioral mind-set can be activated in the process of performing a behavior. And the mind-set may influence people’s subsequent behaviors in unrelated situations in which the activated procedure is applicable. Moreover, motivation to engage in one behavior could also elicit a cognition-based mind-set, which may change people’s subsequent behaviors.

Scheduling of track inspection and maintenance activities in railroad networks

The U.S. railroad companies spend billions of dollars every year on railroad track maintenance in order to ensure safety and operational efficiency of their railroad networks. Besides maintenance costs, other costs such as train accident costs, train and shipment delay costs and rolling stock maintenance costs are also closely related to track maintenance activities. Optimizing the track maintenance process on the extensive railroad networks is a very complex problem with major cost implications. Currently, the decision making process for track maintenance planning is largely manual and primarily relies on the knowledge and judgment of experts. There is considerable potential to improve the process by using operations research techniques to develop solutions to the optimization problems on track maintenance. In this dissertation study, we propose a range of mathematical models and solution algorithms for three network-level scheduling problems on track maintenance: track inspection scheduling problem (TISP), production team scheduling problem (PTSP) and job-to-project clustering problem (JTPCP). TISP involves a set of inspection teams which travel over the railroad network to identify track defects. It is a large-scale routing and scheduling problem where thousands of tasks are to be scheduled subject to many difficult side constraints such as periodicity constraints and discrete working time constraints. A vehicle routing problem formulation was proposed for TISP, and a customized heuristic algorithm was developed to solve the model. The algorithm iteratively applies a constructive heuristic and a local search algorithm in an incremental scheduling horizon framework. The proposed model and algorithm have been adopted by a Class I railroad in its decision making process. Real-world case studies show the proposed approach outperforms the manual approach in short-term scheduling and can be used to conduct long-term what-if analyses to yield managerial insights. PTSP schedules capital track maintenance projects, which are the largest track maintenance activities and account for the majority of railroad capital spending. A time-space network model was proposed to formulate PTSP. More than ten types of side constraints were considered in the model, including very complex constraints such as mutual exclusion constraints and consecution constraints. A multiple neighborhood search algorithm, including a decomposition and restriction search and a block-interchange search, was developed to solve the model. Various performance enhancement techniques, such as data reduction, augmented cost function and subproblem prioritization, were developed to improve the algorithm. The proposed approach has been adopted by a Class I railroad for two years. Our numerical results show the model solutions are able to satisfy all hard constraints and most soft constraints. Compared with the existing manual procedure, the proposed approach is able to bring significant cost savings and operational efficiency improvement. JTPCP is an intermediate problem between TISP and PTSP. It focuses on clustering thousands of capital track maintenance jobs (based on the defects identified in track inspection) into projects so that the projects can be scheduled in PTSP. A vehicle routing problem based model and a multiple-step heuristic algorithm were developed to solve this problem. Various side constraints such as mutual exclusion constraints and rounding constraints were considered. The proposed approach has been applied in practice and has shown good performance in both solution quality and efficiency.