Globalization and the Marginalization of Educational Purpose: Preparation of Workers and Citizens for the 21st Century and the Vision of Sustainable Futures

Human capital development remains a primary goal of modern schooling. This paper raises questions concerning the link between global economic needs and school-based human capital development. The primary mission of preparing students for the workplace may weaken other educational missions vital in achieving a more sustainable future for humanity.

The early development and pilot -testing of a case management intervention with victims of stalking: An evaluation study

This study evaluated the early development and pilot-testing of Project IMPACT, a case management intervention for victims of stalking. The Design and Development framework (Rothman & Thomas, 1994) was used as a guide for program development and evaluation. Nine research questions examined the processes and outcomes associated with program implementation. ^ The sample included all 36 clients who participated in Project IMPACT between February of 2000 and June of 2001, as well as the victim advocates who provided them with services. Quantitative and qualitative data were drawn from client case files, participant observation field notes and interview transcriptions. Quantitative data were entered into three databases where: (1) clients were the units of analysis (n = 36), (2) services were the units of analysis (n = 1146), and (3) goals were the units of analysis (n = 149). These data were analyzed using descriptive statistics, Pearson's Chi-square, Spearman's Rho, Phi, Cramer's V, Wilcoxon's Matched Pairs Signed-Ranked Test and McNemar's Test Statistic. Qualitative data were reduced via open, axial and selective coding methods. Grounded theory and case study frameworks were utilized to analyze these data. ^ Results showed that most clients noted an improved sense of well-being and safety, although residual symptoms of trauma remained for numerous individuals. Stalkers appeared to respond to criminal and civil justice-based interventions by reducing violent and threatening behaviors; however, covert behaviors continued. The study produced findings that provided preliminary support for the use of several intervention components including support services, psycho-education, safety planning, and boundary spanning. The psycho-education and safety planning in particular seemed to help clients cognitively reframe their perceptions of the stalking experience and gain a sense of increased safety and well-being. A 65% level of satisfactory goal achievement was observed overall, although goals involving justice-based organizations were associated with lower achievement. High service usage was related to low-income clients and those lacking in social support. Numerous inconsistencies in program implementation were found to be associated with the skills and experiences of victim advocates. Thus, recommendations were made to further refine, develop and evaluate the intervention. ^

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^