DePaul University

About: DePaul University is a education organization based out in Chicago, Illinois, United States. It is known for research contribution in the topics: Population & Context (language use). The organization has 5658 authors who have published 11562 publications receiving 295257 citations.

Outcomes of self-labeling sexual harassment.

Abstract: Research has consistently documented a discrepancy between experiencing offensive sex-related behaviors and labeling these situations as sexual harassment, leading to several attempts to understand this phenomenon. The authors propose that the issue of why it is that women who report such experiences generally do not indicate that they have been sexually harassed is an important psychological question, and may provide a path through the nested meanings of workplace harassment. The authors argue for the value of moving beyond a descriptive approach to this issue by examining the effects of self-labeling on the psychological, health, and work-related outcomes of unwelcome, sex-related experiences. They present data from female employees working in 3 separate organizations, demonstrating that women exposed to such behaviors report very similar consequences, whether they label their experiences as harassment or not.

Auditors' and Investors' Perceptions of the "Expectation Gap"

Abstract: The auditing “expectation gap” refers to the difference between (1) what the public and other financial statement users perceive auditors' responsibilities to be and (2) what auditors believe their responsibilities entail. The notion of this divergence receives much attention in the accounting literature (i.e., Commission on Auditors' Responsibilities 1978; Guy and Sullivan 1988; AICPA 1993; U.S. Government Accounting Office 1996). Although prior empirical studies encompass certain expectations associated with a range of audit services, these papers often involve the opinions of bankers as the primary user group employed in the research (Nair and Rittenberg 1987; Lowe and Pany 1995). In contrast, this study extends the prior research by directly comparing audit partners' and investors' perceptions of auditors' responsibilities involving various dimensions of the attest function. We conducted the study to determine if an expectation gap currently exists and we find that it does; investors have higher expec...

Computational Thinking in K-9 Education

Abstract: In this report we consider the current status of the coverage of computer science in education at the lowest levels of education in multiple countries. Our focus is on computational thinking (CT), a term meant to encompass a set of concepts and thought processes that aid in formulating problems and their solutions in different fields in a way that could involve computers [130].The main goal of this report is to help teachers, those involved in teacher education, and decision makers to make informed decisions about how and when CT can be included in their local institutions. We begin by defining CT and then discuss the current state of CT in K-9 education in multiple countries in Europe as well as the United States. Since many students are exposed to CT outside of school, we also discuss the current state of informal educational initiatives in the same set of countries.An important contribution of the report is a survey distributed to K-9 teachers, aiming at revealing to what extent different aspects of CT are already part of teachers' classroom practice and how this is done. The survey data suggest that some teachers are already involved in activities that have strong potential for introducing some aspects of CT. In addition to the examples given by teachers participating in the survey, we present some additional sample activities and lesson plans for working with aspects of CT in different subjects. We also discuss ways in which teacher training can be coordinated as well as the issue of repositories. We conclude with future directions for research in CT at school.

Automated classification of non-functional requirements

Abstract: This paper describes a technique for automating the detection and classification of non-functional requirements related to properties such as security, performance, and usability. Early detection of non-functional requirements enables them to be incorporated into the initial architectural design instead of being refactored in at a later date. The approach is used to detect and classify stakeholders’ quality concerns across requirements specifications containing scattered and non-categorized requirements, and also across freeform documents such as meeting minutes, interview notes, and memos. This paper first describes the classification algorithm and then evaluates its effectiveness through reporting a series of experiments based on 30 requirements specifications developed as term projects by MS students at DePaul University. A new and iterative approach is then introduced for training or retraining a classifier to detect and classify non-functional requirements (NFR) in datasets dissimilar to the initial training sets. This approach is evaluated against a large free-form requirements document obtained from Siemens Logistics and Automotive Organization. Although to the NFR classifier is unable to detect all of the NFRs, it is useful for supporting an analyst in the error-prone task of manually discovering NFRs, and furthermore can be used to quickly analyse large and complex documents in order to search for NFRs.