Longitudinal study of computer usage in flexible engineering education

A longitudinal analysis of computer usage by commencing students in Deakin University\u27s undergraduate engineering and technology programs over the period 1998 to 2001 revealed that; access to computers was at high levels; mean computer usage for off campus students had not changed significantly, but had risen significantly for on campus students; while access to the Internet / WWW had not increased significantly, reported regular use of the Internet / WWW had risen significantly; while most students continued to report their source of Internet / WWW access as either home or university, the proportion reporting home as their source of access had risen significantly; and the reported regular use of email rose significantly. Other results are also presented.These results imply that commencing engineering and technology students are well placed to adopt online delivery and support of teaching and learning. However, while it might now be reasonable to assume that all students have access to computers and the Internet, the experiences of on campus students in computer laboratories with broadband network access will be different from off campus students accessing the Internet via a dialup modem connection. A small proportion of commencing students were unaware of the computing facilities provided by the university; an orientation program covering computing facilities and services would benefit all commencing students. <br /

Pain Level Detection From Facial Image Captured by Smartphone

Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high

Experiences of in-home evaluation of independent living technologies for older adults

Evaluating home-based independent living technologies for older adults is essential. Whilst older adults are a diverse group with a range of computing experiences, it is likely that many of this user group may have little experience with technology and may be challenged with age-related impairments that can further impact upon their interaction with technology. However, the evaluation life cycle of independent living technologies does not only involve usability testing of such technologies in the home. It must also consider the evaluation of the older adult’s living space to ensure technologies can be easily integrated into their homes and daily routines. Assessing the impact of these technologies on older adults is equally critical as they can only be successful if older adults are willing to accept and adopt them. In this paper we present three case studies that illustrate the evaluation life cycle of independent living technologies within TRIL, which include ethnographic assessment of participant attitudes and expectations, evaluation of the living space prior to the deployment of any technology, to the final evaluation of usability and participant perspectives

Early Determinants of Women in the IT Workforce: A Model of Girls’ Career Choices

Purpose – To develop a testable model for girls’ career choices in technology fields based on past research and hypotheses about the future of the information technology (IT) workforce. Design/Methodology/Approach – Review and assimilation of literature from education, psychology, sociology, computer science, IT, and business in a model that identifies factors that can potentially influence a girl’s choice towards or against IT careers. The factors are categorized into social factors (family, peers, and media), structural factors (computer use, teacher/counselor influence, same sex versus coeducational schools), and individual differences. The impact of culture on these various factors is also explored. Findings – The model indicates that parents, particularly fathers, are the key influencers of girls’ choice of IT careers. Teachers and counselors provide little or no career direction. Hypotheses propose that early access to computers may reduce intimidation with technology and that same-sex education may serve to reduce career bias against IT. Research Limitations/Implications – While the model is multidisciplinary, much of research from which it draws is five to eight years old. Patterns of career choices, availability of technology, increased independence of women and girls, offshore/nearshore outsourcings of IT jobs are just some of the factors that may be insufficiently addressed in this study. Practical Implications – A “Recommendations” section provides some practical steps to increase the involvement of girls in IT-related careers and activities at an early age. The article identifies cultural research as a limitation and ways to address this. Originality/value – The paper is an assimilation of literature from diverse fields and provides a testable model for research on gender and IT

Social media mining for identification and exploration of health-related information from pregnant women

Widespread use of social media has led to the generation of substantial amounts of information about individuals, including health-related information. Social media provides the opportunity to study health-related information about selected population groups who may be of interest for a particular study. In this paper, we explore the possibility of utilizing social media to perform targeted data collection and analysis from a particular population group -- pregnant women. We hypothesize that we can use social media to identify cohorts of pregnant women and follow them over time to analyze crucial health-related information. To identify potentially pregnant women, we employ simple rule-based searches that attempt to detect pregnancy announcements with moderate precision. To further filter out false positives and noise, we employ a supervised classifier using a small number of hand-annotated data. We then collect their posts over time to create longitudinal health timelines and attempt to divide the timelines into different pregnancy trimesters. Finally, we assess the usefulness of the timelines by performing a preliminary analysis to estimate drug intake patterns of our cohort at different trimesters. Our rule-based cohort identification technique collected 53,820 users over thirty months from Twitter. Our pregnancy announcement classification technique achieved an F-measure of 0.81 for the pregnancy class, resulting in 34,895 user timelines. Analysis of the timelines revealed that pertinent health-related information, such as drug-intake and adverse reactions can be mined from the data. Our approach to using user timelines in this fashion has produced very encouraging results and can be employed for other important tasks where cohorts, for which health-related information may not be available from other sources, are required to be followed over time to derive population-based estimates.Comment: 9 page

Monitoring and detection of agitation in dementia: towards real-time and big-data solutions

The changing demographic profile of the population has potentially challenging social, geopolitical, and financial consequences for individuals, families, the wider society, and governments globally. The demographic change will result in a rapidly growing elderly population with healthcare implications which importantly include Alzheimer type conditions (a leading cause of dementia). Dementia requires long term care to manage the negative behavioral symptoms which are primarily exhibited in terms of agitation and aggression as the condition develops. This paper considers the nature of dementia along with the issues and challenges implicit in its management. The Behavioral and Psychological Symptoms of Dementia (BPSD) are introduced with factors (precursors) to the onset of agitation and aggression. Independent living is considered, health monitoring and implementation in context-aware decision-support systems is discussed with consideration of data analytics. Implicit in health monitoring are technical and ethical constraints, we briefly consider these constraints with the ability to generalize to a range of medical conditions. We postulate that health monitoring offers exciting potential opportunities however the challenges lie in the effective realization of independent assisted living while meeting the ethical challenges, achieving this remains an open research question remains.Peer ReviewedPostprint (author's final draft