Corina Sas

Bio: Corina Sas is an academic researcher from Lancaster University. The author has contributed to research in topics: Interaction design & Mental health. The author has an hindex of 29, co-authored 159 publications receiving 2917 citations. Previous affiliations of Corina Sas include University College Dublin.

Exploring the facebook experience: a new approach to usability

Abstract: The focus of this paper is to explore social networking sites such as Facebook in order to understand their recent success and popularity. Recent developments within Web 2.0 have provided users with more freedom to create their own unique user experiences. The conflict between traditional usability methods and user experiences are addressed through carrying out a Heuristic Evaluation to assess how well Facebook complies with usability guidelines and by conducting a user study to unveil unique user experiences. The findings of this study calls for a more holistic method of evaluation that redefines usability to encompass the user experience in line with future technology.

Presence equation: an investigation into cognitive factors underlying presence

Abstract: The relationship between presence and cognitive factors such as absorption, creative imagination, empathy, and willingness to experience presence was investigated. Presence was defined, operationalized, and measured using a questionnaire that we devised. Absorption and creative imagination were measured using questionnaires developed in the area of hypnosis, and empathy was assessed through an interpersonal reactivity index. Results indicated significant correlations between presence and each cognitive factor. They showed that persons who are highly fantasy prone, more empathic, more absorbed, more creative, or more willing to be transported to the virtual world experienced a greater sense of presence. Regression analysis led to a presence equation, which could be used to predict presence based on the investigated cognitive factors. Findings are congruent with user characteristics presented by the presence iterature and support the position that individual differences are important for the study of presence.

Introduction to this special issue on designing for personal memories: past, present and future

Abstract: This special issue focuses on new uses of digital media to help people remember in everyday situations. We begin this introduction by describing the field's origins (personal memories past), using ...

Design for forgetting: disposing of digital possessions after a breakup

Abstract: People are increasingly acquiring huge collections of digital possessions. Despite some pleas for 'forgetting', most theorists argue for retaining all these possessions to enhance 'total recall' of our everyday lives. However, there has been little exploration of the negative role of digital possessions when people want to forget aspects of their lives. We report on interviews with 24 people about their possessions after a romantic breakup. We found that digital possessions were often evocative and upsetting in this context, leading to distinct disposal strategies with different outcomes. We advance theory by finding strong evidence for the value of intentional forgetting and provide new data about complex practices associated with the disposal of digital possessions. Our findings led to a number of design implications to help people better manage this process, including automatic harvesting of digital possessions, tools for self-control, artifact crafting as sense-making, and digital spaces for shared possessions.

Public Ubiquitous Computing Systems: Lessons from the e-Campus Display Deployments

Abstract: Lessons learned from building and deploying three experimental public display systems have general app to many types of public ubicomp deployments. Lancaster University's e-Campus project is exploring the creation of large-scale networked displays as part of a public, interactive pervasive computing environment. For the project, we built and deployed three experimental display systems that vary in technology, location, scale, and user community, and they've given us a rich set of experiences. We've summarized 13 lessons learned from this experience. The lessons certainly apply to researchers planning similar deployments. We also believe they will generalize to other public ubicomp installations. The e-Campus project is embarking on a major new set of deployments. We're using these lessons to help guide our work and we believe the lessons learned will be valuable to all researchers deploying ubicomp systems in public spaces

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Human cognitive abilities: A survey of factor analytic studies

Abstract: (1998). Human cognitive abilities: A survey of factor analytic studies. Gifted and Talented International: Vol. 13, No. 2, pp. 97-98.