The cognitive factors of spatial ability, human-computer interaction, problem solving ability, and geographic attitude have been recognized as relevant to teaching and learning GIS. The goal of this research was to examine these cognitive abilities in university students taking an introductory course in GIS; examine any changes in these abilities after completing the class; and examine the relationship between those abilities and the students’ grades in the class. It was hypothesized that students with higher cognitive ability scores would have higher grades than students with lower cognitive ability scores. Nine different self-report surveys were used to assess the students’ spatial, computer, problem solving, and geographic cognitive abilities. The surveys were administered at the beginning and end of the two academic semesters. Analysis of the students’ scores revealed a significant improvement on four of the nine cognitive ability surveys; one that measured computer experience and three that measured spatial ability. Bivariate correlations and multiple regression analyses were used to measure the relationship between the students’ scores on the cognitive ability surveys and the students’ grades. Students received grades on lecture exams, lab exercises, individual projects, and an overall grade. Only two of the bivariate correlations were statistically significant: the factors of geography attitude and learning style were significantly correlated with the students’ project grade. Multiple regression analysis also revealed a very weak relationship, explaining less than 20 percent of the variance between the scores on the cognitive ability surveys and the students’ lecture grade, lab grade, and overall grade. However, a much stronger relationship, explaining more than 45% of the variance, existed between the cognitive ability surveys and the students’ project grade. These findings suggest that cognitive processes utilized for traditional classroom learning to pass lecture exams are different than those utilized to learn the software skills necessary to complete a GIS project. Therefore, it was concluded that the cognitive ability scores are poor predictors of grades related to traditional classroom learning such as lecture exams; however, these scores are more useful as predictors of the grades on a GIS project.

摘要 研究動機與目的:在大數據的時代，為NBA籃球運彩迷們提供一些籃球數據的分析與模型，使其在購買台灣運彩時，能多些中獎的機率與樂趣。 研究方法:透過相關係數、迴歸來進行NBA籃球數據分析。 創新與推廣:相關係數與迴歸分析出來後，與NBA運彩應用結合，產生策略。 結論:預測數據部分最為困難，必須找出策略搭配預測數據之方法。 關鍵字: NBA運彩策略、迴歸、預測 / Abstract Motivation and purpose of this study: In the era of big data, provide NBA basketball fans with some analysis and models of basketball data, so that they can win more chances and fun when purchasing Taiwan NBA lottery. Method: NBA basketball data analysis is performed through correlation coefficient and regression. Innovation and promotion: After the correlation coefficient and regression analysis come out, it is combined with NBA lottery application to generate strategies. Conclusion: The prediction data part is the most difficult and it is necessary to find out the method of strategy matching the prediction data. Keywords: NBA lottery strategy, Regression, Predicting

abstract: A myriad of social media services are emerging in recent years that allow people to communicate and express themselves conveniently and easily. The pervasive use of social media generates massive data at an unprecedented rate. It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem. Meanwhile, users in social media can be both passive content consumers and active content producers, causing the quality of user-generated content can vary dramatically from excellence to abuse or spam, which results in a problem of information credibility. Trust, providing evidence about with whom users can trust to share information and from whom users can accept information without additional verification, plays a crucial role in helping online users collect relevant and reliable information. It has been proven to be an effective way to mitigate information overload and credibility problems and has attracted increasing attention. As the conceptual counterpart of trust, distrust could be as important as trust and its value has been widely recognized by social sciences in the physical world. However, little attention is paid on distrust in social media. Social media differs from the physical world - (1) its data is passively observed, large-scale, incomplete, noisy and embedded with rich heterogeneous sources; and (2) distrust is generally unavailable in social media. These unique properties of social media present novel challenges for computing distrust in social media: (1) passively observed social media data does not provide necessary information social scientists use to understand distrust, how can I understand distrust in social media? (2) distrust is usually invisible in social media, how can I make invisible distrust visible by leveraging unique properties of social media data? and (3) little is known about distrust and its role in social media applications, how can distrust help make difference in social media applications? The chief objective of this dissertation is to figure out solutions to these challenges via innovative research and novel methods. In particular, computational tasks are designed to {\it understand distrust}, a innovative task, i.e., {\it predicting distrust} is proposed with novel frameworks to make invisible distrust visible, and principled approaches are develop to {\it apply distrust} in social media applications. Since distrust is a special type of negative links, I demonstrate the generalization of properties and algorithms of distrust to negative links, i.e., {\it generalizing findings of distrust}, which greatly expands the boundaries of research of distrust and largely broadens its applications in social media. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2015

There is a need of storing and keeping track of things. As the amount of information increases, so does the demand for suitable applications that can manage the data. This thesis has had its focus on developing a web administration interface for relational databases, where the focus has been on managing and visualizing the data, where relationships between data within the database could be predicted through an algorithm. During the thesis, it was revealed that administrators can utilize naming conventions for databases, a property which can be used to predict its relationships. Furthermore, existing applications for managing databases has been compared with the thesis' implementation. Notable differences are that existing solutions are focused towards the structure of the data, rather than the data itself. To accomplish all this, an agile method was chosen for fast results within the deadline, together with standardized web development tools and JavaScript frameworks. The resulting implementation consists of a front- and backend. The frontend was developed using the Ember.JS framework for making web applications and the backend was implemented using Node.JS, together with a component for handling different database dialects called Sequelize. It has been concluded that the prototype this thesis has resulted in works as a proof of concept, complete with a prediction algorithm that can suggest relationships within databases that utilizes convenient and consistent naming conventions. In the future, further research and tests could be conducted to evaluate the security, reliability and usability of the application, to ensure its production quality.

Thesis advisor: Babak Momeni / Microbial communities are complex networks comprised of multiple species that are facilitating and inhibiting one another (as well as themselves). Currently, we lack an understanding of what mechanisms drive coexistence within these communities. We aimed to remedy this by studying the dynamics of coexisting communities, focusing on the complexity of their interaction networks, the impact of spatial dynamics, and the interplay of facilitating and inhibiting interactions. These limitations in our understanding prevent the furtherment of designing intentional communities for bioremediation, maintenance of healthy microbiota, and other functional communities. To better understand these microbial dynamics, we chose to address the problem from two fronts: computational modeling and exploring dynamics of cocultures. Through our 1-D model, spatial structure fostering more coexistence – especially when facilitation is present. For the coexistence assays, we determined that contact-dependent growth inhibition is a density dependent mechanism, and the use of a Tn-Seq mutant library to predict species interactions is possible, but needs further optimization to reconcile density dependent effects of interactions. / Thesis (MS) — Boston College, 2021. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

Magister Curationis - MCur / Academic performance of students is influenced by a combination of several psychosocial factors which include seeking academic help, use of various sources for academic learning, extent of the student seeking academic help, seeing academic help-seeking as a threat to self-esteem, interest in a subject, self-motivation and stress related to academic workload. Therefore, this study aimed to investigate psychosocial factors predicting the academic performance of first-year college nursing students, using a quantitative research method with a descriptive survey design. The population for this study was all first-year nursing students registered at a college of nursing in 2019. An inclusive sampling technique was used to include all 171 members of the student population in the study.

On-time delivery of supplies to industry is essential because delays can disrupt production schedules. The aim of the proposed application is to predict transit times for outbound logistics thereby allowing suppliers to plan for timely mitigation of risks during shipment planning. The predictive model consists of a classifier that is trained for each specific source-destination pair using historical shipment, weather, and social media data. The model estimates the transit times for future shipments using Support Vector Machine (SVM). These estimates were validated using four case study routes of varying distances in the United States. A predictive model is trained for each route. The results show that the contribution of each input feature to the predictive ability of the model varies for each route. The mean average error (MAE) values of the model vary for each route due to the availability of testing and training historical shipment data as well as the availability of weather and social media data. In addition, it was found that the inclusion of the historical traffic data provided by INRIX™ improves the accuracy of the model. Sample INRIX™ data was available for one of the routes. One of the main limitations of the proposed approach is the availability of historical shipment data and the quality of social media data. However, if the data is available, the proposed methodology can be applied to any supplier with high volume shipments in order to develop a predictive model for outbound transit time delays over any land route.

Addressing the needs of developmental math students has been one of the most challenging problems in higher education. Administrators at a private university were concerned about poor academic performance of math-deficient students and sought to identify factors that influenced students' successful progression from developmental to college-level coursework. The purpose of this retrospective prediction study was to determine which of 7 variables (enrollment in a college success course, math placement results, frequency of use of the developmental resource center, source of tuition payment, student's age, gender, and race/ethnicity) would be predictive of success in developmental math as defined by a final course grade of C or higher. Astin's theory of student involvement and Tinto's theory of student retention formed the theoretical framework for this investigation of 557 first-year students who entered the university during Fall 2013 and Fall 2014. Binary logistic regression analysis was performed. Successful completion of the university's college success course as well as enrollment in introductory/intermediate algebra or intermediate algebra were significant predictors of success in remedial math courses. In addition, the lower the level of developmental math a student was placed in and engaged with, the higher the probability of success in the course. These findings were used to create a policy recommendation for a prescriptive means of ensuring students' early enrollment in developmental math courses and engagement with university resources, which may help students overcome barriers to success in developmental math and lead to positive social change for both the students and university through higher retention and graduation rates.

prediction of future percormance is attempted in almost every field of endeavor. The accuracy varies in different lines of study, and perhaps none is as subject to variability as those attempted with human beings as subjects. When an attempt is made to ascertain in advance the performance of college students in their schoolwork, a multiplicity of complicating problems are introduced. Whereas intelligence can be fairly well isolated, it is difficult to control or even enumerated all the other factors that come into the problem of predicting grades from scores received on an intelligence examination. Among the factors that are difficult to objectively measure or control are the transference of past learning, levels of aspiration, efficiency of study habits and time spent in studyingy, attentiveness in class, as well as specific aptitudes of disabilities, varying difficulty of different academic courses, and susceptibility to or freedom fromphysiological or paychological disorders. Evn though correlations between scholastic grades and intelligence test scores will be, due to various factors of limitation, only moderately high at best, their values cannot be doubted. With high correlations, a definite relationship can be established. With lower correlations, trends can be noted. A segment analysis also may prove to be of value in establishing areas of relative stringth and weaknesses in the predictive structure. The thesis problem is one of determining certain predictive values of the American Council on Education Psychological Examination. Inasmuch as specificity is a virtue in educational measurement, the American Council test is a definite step in this direction. The aim of the test is to measure what the authors of hte test consider to be schoolastic aptitude. The purpose of this study is to determine the accuracy of this scholastic aptitude test in predicting grade-point averages of Utah State Agricultural College freshmen students in their first quarter in college.

The objectives of this study were (a) to apply sediment and associated plot data from various infiltrometer studies to the parameters in the Universal Soil Loss Equation, a modified version of the original Musgrave Equation, and a modified version of the original Universal Soil Loss Equation, and compare the computed results with the measured soil loss, (b) to suggest reasons for any differences between computed and measured soil loss, and (c) to suggest improvements for each equation so that it will give results near the measured soil loss. The data used consisted of 2805 infiltrometer plots collected by previous researchers in a variety of rangeland conditions, both in the western United States and Australia, and included the necessary information needed to compute the factors in each of the above equations. Simple and multiple linear regression techniques were used to make the evaluations by computing the coefficient of determination (R2), correlation coefficients (r), and to optimize each factor in the equations by placing an exponent on it. The results showed that the three soil loss prediction equations are not universal, but, for the most part, explain sediment yield with varying degrees of accuracy in different situations with no apparent trends or patterns. However, most individual mine sites and other sites with loosely consolidated soil resembling fallow conditions showed high R2 values when the computed sediment yield was regressed against measured sediment yield. Little improvement was made in reducing the variability of the equations by placing exponents on each factor indicating that the factors, as determined in each equation, do not explain sediment yield under western rangeland conditions. In summary, the prediction equations are not recommended as "universal" predictors of sheet erosion in western rangelands, but, may be applied in specific situations.