A linguistic approach to assess the dynamics of design team preference in concept selection

This paper addresses the problem of describing the decision-making process of a committee of engineers based upon their verbalized linguistic appraisals of alternatives. First, we show a way to model an individual’s evaluation of an alternative through natural language based on the Systemic-Functional Linguistics system of APPRAISAL. The linguistic model accounts for both the degree of intensity and the uncertainty of expressed evaluations. Second, this multi-dimensional linguistic model is converted into a scalar to represent the degree of intensity and a probability distribution function for the stated evaluation. Finally, we present a Markovian model to calculate the time-varying change in preferential probability, the probability that an alternative is the most preferred alternative. We further demonstrate how preferential probability toward attributes of alternatives correspond to preferential probability toward alternatives. We illustrate the method on two case studies to highlight the time-variant dynamics of preferences toward alternatives and attributes. This research contributes to process tracing in descriptive decision science to understand how engineers actually take decisions.National Science Foundation (U.S.) (Award CMMI-0900255

An Evaluation Framework and Adaptive Architecture for Automated Sentiment Detection

Analysts are often interested in how sentiment towards an organization, a product or a particular technology changes over time. Popular methods that process unstructured textual material to automatically detect sentiment based on tagged dictionaries are not capable of fulfilling this task, even when coupled with part-of-speech tagging, a standard component of most text processing toolkits that distinguishes grammatical categories such as article, noun, verb, and adverb. Small corpus size, ambiguity and subtle incremental change of tonal expressions between different versions of a document complicate sentiment detection. Parsing grammatical structures, by contrast, outperforms dictionary-based approaches in terms of reliability, but usually suffers from poor scalability due to its computational complexity. This work provides an overview of different dictionary- and machine-learning-based sentiment detection methods and evaluates them on several Web corpora. After identifying the shortcomings of these methods, the paper proposes an approach based on automatically building Tagged Linguistic Unit (TLU) databases to overcome the restrictions of dictionaries with a limited set of tagged tokens

Using self-organizing maps in the visualization and analysis of forest inventory

A lot of useful data on forest condition can be gathered from the Forest Inven­tory (FI). Without the help of data analysis tools, human experts cannot ma­nually interpret information in such a large data set. Conventional multivariate statistical analyses provide results that are difficult to interpret and often do not represent the information in a satisfactory way. Our goal is to identify an alternative approach that will enable fast and efficient interpretation and analysis of the FI data. Such interpretation and analysis can be performed automatically with a clustering method, but all clustering methods have some shortcomings. Therefore, our aim was also to provide information in a form suitable for fast and intuitive visualization. Kohonen’s Self Organizing Map (SOM) is an alternative approach to data visualization and analysis of large multidimensional data sets. SOM provides different possibilities and our experiments are presented with component matrices of individual stand parameters and label matrices. In forming data clusters, we experimented with hierarchi­cal and non hierarchical clustering methods. Our experiments showed that SOM provides useful information in a form suitable for data clustering and data vi­sualization. This enables an efficient analysis of large FI data sets at different analysis scales. Clustering results obtained with SOM and two clustering algorithms are in accordance with ground truth. We have also considered the efficiency of SOM component matrices by visual comparison and correlation among structural parameters and by determining contributions of individual stand parameters to clustering input data. SOM application in visualization and analysis of stand structural parameters enables gathering quickly and efficiently holistic information on the current condition of forest stands and forest ecosystem development. Therefore we recommend the application of Kohonen’s SOM for visualization and analysis of FI data

School climate, social identity processes and school outcomes: Making the case for a group-level approach to understanding schools

An import ant area of inquiry within the educa tional domain concerns school climate and related concepts such as school connec ted ness and school belong ing (e.g., Thapa, Cohen, Higgins­D'Alessandro and Guffey, 2013). School climate is defined in differ ent ways, but in essence, it focuses on student percep tions of academic emphasis, the way groups within a school (e.g., teach ers, students, parents) relate to one another, and the higher­ order norms, values, and prac tices (shared mission) that define the school as a whole (Thapa et al., 2013). In this chapter, we argue that incor por at ing a social­ psycho lo gical analysis of the group within the school climate domain can advance under stand ing of school life. To date, most emphasis is placed on the psycho logy of indi vidu als­ as­individuals and inter per sonal rela tion ships. What is missing is an analysis of the group

How does school climate impact academic achievement? An examination of social identity processes

In explaining academic achievement, school climate and social belonging (connectedness, identification) emerge as important variables. However, both constructs are rarely explored in one model. In the current study, a social psychological framework based on the social identity perspective (Turner, Hogg, Oakes, Reicher, & Wetherell, 1987) is introduced that provides a way to integrate these two areas of enquiry. Using this framework, the current study (N = 340 grade 7 and 9 students) investigates: (a) school climate and social identification as distinct predictors of academic achievement; and (b) social identification as a mediator of the school climate and achievement relationship. Achievement in reading, numeracy and writing was assessed by a national standardized test. The three variables most significantly associated with achievement were parental education, socio-economic status, and school identification. In line with predictions, school identification fully mediated the relationship between school climate and academic achievement in numeracy and writing, but not reading. The research highlights the importance of feeling psychologically connected to the school as a group for academic success

Managament of Immunosuppression in Kidney Post Transplantation

When histocompatibility differences exist between donor and recipient, it is necessary to modify or suppress the immune response in order to enable the recipient to accept a graft. Immunesuppressive therapy, in general, suppresses all immune responses, including those to bacteria, fungi and even malignant tumors. In the 1950s when clinical renal transplantation began, sublethal total body irradiation was employed. Currently immunosuppression is more safely induced pharmacologically. Agents used in humans to suppress the immune response are discussed in our paper

The impact of school climate and school identification on academic achievement: Multilevel modeling with student and teacher data

School climate is a leading factor in explaining student learning and achievement. Less work has explored the impact of both staff and student perceptions of school climate raising interesting questions about whether staff school climate experiences can add "value" to students' achievement. In the current research, multiple sources were integrated into a multilevel model, including staff self-reports, student self-reports, objective school records of academic achievement, and socio-economic demographics. Achievement was assessed using a national literacy and numeracy tests (N = 760 staff and 2,257 students from 17 secondary schools). In addition, guided by the "social identity approach," school identification is investigated as a possible psychological mechanism to explain the relationship between school climate and achievement. In line with predictions, results show that students' perceptions of school climate significantly explain writing and numeracy achievement and this effect is mediated by students' psychological identification with the school. Furthermore, staff perceptions of school climate explain students' achievement on numeracy, writing and reading tests (while accounting for students' responses). However, staff's school identification did not play a significant role. Implications of these findings for organizational, social, and educational research are discussed

Does education really change us? The impact of school-based social processes on the person

The group context and social identity is central for understanding how educational factors can influence behavior. Social identity processes not only help explain student behavior at school but point to pathways that can be used to shape it. It is argued that to change a person's motivations, perceptions, and behavior, it is necessary to transform and change his or her social identity. It is through shifts in defining who "we" are and what "we" do that it is possible to transform who "I" am and what "I" do

Well-being, school climate, and the social identity process: A latent growth model study of bullying perpetration and peer victimization

The present study concerns longitudinal research on bullying perpetration and peer victimization. A focus is on school factors of school climate (academic support, group support) and school identification (connectedness or belonging), which are conceptualized as related but distinct constructs. Analysis of change on these factors as well as individual well-being across time contributes to understanding bullying behavior. Latent growth modeling was employed to examine the predictors of anxiety, depression, 2 school climate factors and school identification in understanding change in physical and verbal bullying behavior. The sample included 492 Australian school students (means age 15 years, 53.5% male) in Grades 7 to 10 who completed measures over 3 years. Academic support and group support were the strongest predictors of change in bullying and victimization. Positive change in school identification also predicted a decrease in bullying behavior over time. An increase in depression or anxiety across time predicted an increase in rates of both bullying and victimization over time. Future research should continue to examine the complex relationship between individual-psychological and social-psychological variables in impacting on incidence of school-based bullying. On a practical note, school-based intervention programs may benefit from an approach that aims to target the school climate, social identity with the school, and promote individual psychological well-being