Building Virtual Community in a K-6 School: An Action Research Project

Virtual communities (VCs) potentially increase community interaction, social support, civic engagement, and social capital. Concurrently, education research suggests that one important factor in improving student achievement is community and parent involvement. Would building a VC in a K-6 school to support information exchange facilitate parents’ involvement in school activities? How can a K-6 public school with limited resources build a VC? This paper reports on an action research project conducted in a public school to build a VC including such components as dynamic website and discussion forum. Findings suggest that school and community’s access to technology, teacher training and static websites are not enough to attract parents. Extensive analysis of needs and facilitation by a change agent is necessary to design and implement VC technology, to encourage community information exchange and interaction, and to ultimately realize the benefits of building social capital and increasing parental involvement

Closing the Loop: Development of a Dashboard for Quality Improvement of Business Education Programs

Business schools continuously improve their processes and program assessment activities, but fall short in achieving faculty\u27s awareness of process steps and disseminating results to inform and trigger continuous improvement actions. The assessment process of the Craig School of Business at California State University, Fresno had worked well. It evaluated student learning of core competencies of its Business Administration degree programs. The assessment process had a sound set of program goals and student learning outcomes and metrics. Learning outcomes were measured semiannually, and reports were written annually. Assessment results consistently surpassed benchmarks. Still, the assessment process did not effectively disseminate results and findings, which often led to a lack of process awareness and the inability to motivate and engage faculty in the overall assessment and improvement process. The Assessment Team launched an initiative to design and implement a dashboard that would track assessment scores as a means to address these shortcomings and ensure closing the loop with actual improvement results. This article presents the development process of the Assurance of Learning Dashboard and the effects of using the dashboard to facilitate the analysis and dissemination of assessment results. Findings suggest that the dashboard was successful. However, documentation for closing the loop activities continue to be a challenge. Implications for academic institutions and future research opportunities are presented

A Life Cycle Perspective on Online Community Success

Using the information systems lifecycle as a unifying framework, we review online communities research and propose a sequence for incorporating success conditions during initiation and development to increase their chances of becoming a successful community, one in which members participate actively and develop lasting relationships. Online communities evolve following distinctive lifecycle stages and recommendations for success are more or less relevant depending on the developmental stage of the online community. In addition, the goal of the online community under study determines the components to include in the development of a successful online community. Online community builders and researchers will benefit from this review of the conditions that help online communities succeed

Conflicts and Facework in Online Discussions

The purpose of this study is to explore the relation between conflict and the outcomes of online discussions, the moderating effect of facework behaviors on the relation between conflict and online discussion outcomes, the effect of gender in this relation, and in the moderating effect of facework behaviors. During a two-week period, participants (149) in groups of three to four members used an online discussion board to discuss topics commonly controversial. The results indicate that conflict influenced the outcomes of online discussions; facework behaviors moderated the relation between conflict and outcomes; and gender influenced the choice of facework behaviors each member adopted during discussions

Open Source Alternatives for Business Intelligence: Critical Success Factors for Adoption

The purpose of this research is to identify critical factors that affect the adoption of Open Source Business Intelligence (OPBI) tools and to compare the differences between OPBI and Proprietary Business Intelligence (PBI) tools. Based on the Technology Acceptance Model, an organizational adoption model was designed to analyze four cases of organizations that have adopted Business Intelligence (BI) tools. The cases were documented using a tested protocol and a set of interviews. The analysis of the cases shows that organizations with fewer resources and simpler IT selection processes tend to adopt OPBI. The most cited reason for using OPBI software is cost savings. The results also reveal that for most users OPBI does not require sophisticated BI specialists and offers as many useful features as PBI tools. These findings are important to BI vendors, users, developers, and organizations interested in adopting BI technologies

Natural Language Processing and E-Government: Extracting Reusable Crime Report Information

Crime reporting needs to be possible 24/7. Although 911 and tip-lines are the most publicized reporting mechanisms, several other options exist, ranging from in-person reporting to online submissions. Internet-based crime reporting systems allow victims and witnesses of crime to report incidents to police 24/7 from any location. However, these existing e-mail and text-based systems provide little support for witnesses\u27 memory recall leading to reports with less information and lower accuracy. These systems also do not facilitate reuse and integration of the reported information with other information systems. We are developing an anonymous Online Crime Reporting System that is designed to extract relevant crime information from witness\u27 narratives and to ask additional questions based on that information. We leverage natural language processing and investigative interviewing techniques to support memory recall and map the information directly to a database to support information reuse. We report on the evaluation of the Suspect Description Module (SDM) of the system. Our interface captures 70% (recall) of information from witness narratives with 100% precision. Additional modules will follow the design and development methods used with this module

Reporting On-Campus Crime Online: User Intention to Use

National surveys demonstrate that millions of crimes go unreported in the United States. Several reasons may contribute to this lack of reporting and we are investigating these potential reasons and how they may be addressed. We are developing an online system that provides an anonymous and secure mechanism for both victims and witnesses to report crimes to police. The system is being implemented and tested on a university campus. Potential users (i.e., students, staff) were surveyed to determine their intent to use the system. Respondents claimed to report crimes already, which is in contrast with the findings from the national surveys. Our respondents found the online system useful, accessible, and safe to report crime, but the type of crime and the urgency of response is a determinant in the decision to use the system versus reporting it to a live person

Enabling Synergy between Psychology and Natural Language Processing for e-Government: Crime Reporting and Investigative Interview System

We are developing an automated crime reporting and investigative interview system. The system incorporates cognitive interview techniques to maximize witness memory recall, and information extraction technology to extract and annotate crime entities from witness narratives and interview responses. Evaluations of the IE components of the system show that it captures 70 to 77% of information from witness narratives with 93 to 100% precision. Our development goal is for the system to approximate progressively the performance effectiveness of a human investigative interviewer and to generate graphical visualizations of crime report information

Natural Language Processing and e-Government: Crime Information Extraction from Heterogeneous Data Sources

Much information that could help solve and prevent crimes is never gathered because the reporting methods available to citizens and law enforcement personnel are not optimal. Detectives do not have sufficient time to interview crime victims and witnesses. Moreover, many victims and witnesses are too scared or embarrassed to report incidents. We are developing an interviewing system that will help collect such information. We report here on one component, the crime information extraction module, which uses natural language processing to extract crime information from police reports, newspaper articles, and victims’ and witnesses’ crime narratives. We tested our approach with two types of document: police and witness narrative reports. Our algorithms extract crime-related information, namely weapons, vehicles, time, people, clothes, and locations. We achieved high precision (96%) and recall (83%) for police narrative reports and comparable precision (93%) but somewhat lower recall (77%) for witness narrative reports. The difference in recall was significant at p \u3c .05. We then used a spell checker to evaluate if this would help with witness narrative processing. We found that both precision (94 %) and recall (79%) improved slightly