The Impact of Cultural Familiarity on Students’ Social Media Usage in Higher Education

Using social media (SM) in Higher education (HE) becomes unavoidable in the new teaching and learning pedagogy. The current generation of students creates their groups on SM for collaboration. However, SM can be a primary source of learning distraction due to its nature, which does not support structured learning. Hence, derived from the literature, this study proposes three learning customised system features, to be implemented on SM when used in Higher Education HE. Nevertheless, some psychological factors appear to have a stronger impact on students’ adoption of SM in learning than the proposed features. A Quantitative survey was conducted at a university in Uzbekistan to collect 52 undergraduate students’ perception of proposed SM learning customised features in Moodle. These features aim to provide localised, personalised, and privacy control self-management environment for collaboration in Moodle. These features could be significant in predicting students’ engagement with SM in HE. The data analysis showed a majority of positive feedback towards the proposed learning customised SM. However, the surveyed students’ engagement with these features was observed as minimal. The course leader initiated a semi-structured interview to investigate the reason. Although the students confirmed their acceptance of the learning customised features, their preferences to alternate SM, which is Telegram overridden their usage of the proposed learning customized SM, which is Twitter. The students avoided the Moodle integrated Twitter (which provided highly accepted features) and chose to use the Telegram as an external collaboration platform driven by their familiarity and social preferences with the Telegram since it is the popular SM in Uzbekistan. This study is part of an ongoing PhD research which involves deeper frame of learners’ cognitive usage of the learning management system. However, this paper exclusively discusses the cultural familiarity impact of student’s adoption of SM in HE

Data-driven personalisation and the law - a primer: collective interests engaged by personalisation in markets, politics and law

Interdisciplinary Workshop on â��Data-Driven Personalisation in Markets, Politics and Law' on 28 June 2019Southampton Law School will be hosting an interdisciplinary workshop on the topic of â��Data-Driven Personalisation in Markets, Politics and Law' on Friday 28 June 2019, which will explore the pervasive and growing phenomenon of â��personalisationâ�� â�� from behavioural advertising in commerce and micro-targeting in politics, to personalised pricing and contracting and predictive policing and recruitment. This is a huge area which touches upon many legal disciplines as well as social science concerns and, of course, computer science and mathematics. Within law, it goes well beyond data protection law, raising questions for criminal law, consumer protection, competition and IP law, tort law, administrative law, human rights and anti-discrimination law, law and economics as well as legal and constitutional theory. Weâ��ve written a position paper, https://eprints.soton.ac.uk/428082/1/Data_Driven_Personalisation_and_the_Law_A_Primer.pdf which is designed to give focus and structure to a workshop that we expect will be strongly interdisciplinary, creative, thought-provoking and entertaining. We like to hear your thoughts! Call for papers! Should you be interested in disagreeing, elaborating, confirming, contradicting, dismissing or just reflecting on anything in the paper and present those ideas at the workshop, send us an abstract by Friday 5 April 2019 (Ms Clare Brady [email protected] ). We aim to publish an edited popular law/social science book with the most compelling contributions after the workshop.Prof Uta Kohl, Prof James Davey, Dr Jacob Eisler<br/

Systematic Review on Privacy Categorization

In the modern digital world users need to make privacy and security choices that have far-reaching consequences. Researchers are increasingly studying people's decisions when facing with privacy and security trade-offs, the pressing and time consuming disincentives that influence those decisions, and methods to mitigate them. This work aims to present a systematic review of the literature on privacy categorization, which has been defined in terms of profile, profiling, segmentation, clustering and personae. Privacy categorization involves the possibility to classify users according to specific prerequisites, such as their ability to manage privacy issues, or in terms of which type of and how many personal information they decide or do not decide to disclose. Privacy categorization has been defined and used for different purposes. The systematic review focuses on three main research questions that investigate the study contexts, i.e. the motivations and research questions, that propose privacy categorisations; the methodologies and results of privacy categorisations; the evolution of privacy categorisations over time. Ultimately it tries to provide an answer whether privacy categorization as a research attempt is still meaningful and may have a future

Personalised privacy in pervasive and ubiquitous systems

Our world is edging closer to the realisation of pervasive systems and their integration in our everyday life. While pervasive systems are capable of offering many benefits for everyone, the amount and quality of personal information that becomes available raise concerns about maintaining user privacy and create a real need to reform existing privacy practices and provide appropriate safeguards for the user of pervasive environments. This thesis presents the PERSOnalised Negotiation, Identity Selection and Management (PersoNISM) system; a comprehensive approach to privacy protection in pervasive environments using context aware dynamic personalisation and behaviour learning. The aim of the PersoNISM system is twofold: to provide the user with a comprehensive set of privacy protecting tools and to help them make the best use of these tools according to their privacy needs. The PersoNISM system allows users to: a) configure the terms and conditions of data disclosure through the process of privacy policy negotiation, which addresses the current “take it or leave it” approach; b) use multiple identities to interact with pervasive services to avoid the accumulation of vast amounts of personal information in a single user profile; and c) selectively disclose information based on the type of information, who requests it, under what context, for what purpose and how the information will be treated. The PersoNISM system learns user privacy preferences by monitoring the behaviour of the user and uses them to personalise and/or automate the decision making processes in order to unburden the user from manually controlling these complex mechanisms. The PersoNISM system has been designed, implemented, demonstrated and evaluated during three EU funded projects

Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influence

New consent management platforms (CMPs) have been introduced to the web to conform with the EU's General Data Protection Regulation, particularly its requirements for consent when companies collect and process users' personal data. This work analyses how the most prevalent CMP designs affect people's consent choices. We scraped the designs of the five most popular CMPs on the top 10,000 websites in the UK (n=680). We found that dark patterns and implied consent are ubiquitous; only 11.8% meet the minimal requirements that we set based on European law. Second, we conducted a field experiment with 40 participants to investigate how the eight most common designs affect consent choices. We found that notification style (banner or barrier) has no effect; removing the opt-out button from the first page increases consent by 22--23 percentage points; and providing more granular controls on the first page decreases consent by 8--20 percentage points. This study provides an empirical basis for the necessary regulatory action to enforce the GDPR, in particular the possibility of focusing on the centralised, third-party CMP services as an effective way to increase compliance.Comment: 13 pages, 3 figures. To appear in the Proceedings of CHI '20 CHI Conference on Human Factors in Computing Systems, April 25--30, 2020, Honolulu, HI, US

Psychological interference, liberty and technology

Power and the use of force are central concerns in liberal political theory. Yet, it is claimed that liberal theory fails to account for the power that is exerted through, for example, personalised nudging based on Big Data. We dispute this claim and through a reappraisal of the concept of negative liberty we show how psychological interference can be both coercive and manipulative. This reappraisal is partly achieved through an examination of psychological equivalents of physical interference, in order to highlight the arbitrariness of excluding the non-physical from consideration. The liberal understanding of liberty and interference here provided enables us to see how power is exerted through technology. It thus provides a novel contribution to the analysis of the liberty reducing effects of new technologies combined with a lack of privacy and a will to manipulate, based on an individualist liberal theory argued to be incapable of just that.publishedVersio

Australian community pharmacy services: a survey of what people with chronic conditions and their carers use versus what they consider important

Objective To explore the purpose/s for which people with chronic conditions and their carers use Australian community pharmacies, and compare this to what pharmacy services they consider important, from the perspectives of both consumers and pharmacists. Design An exploratory study involving a survey, which asked participants to indicate the pharmacy services they had ever used, and rate the importance of 22 pharmacy services to them, or the person they care for, or for their consumers if a pharmacist. Setting Four regions of Australia: Logan-Beaudesert and Mt Isa/North West region, Queensland, Northern Rivers, New South Wales, and the Greater Perth area, Western Australia. Participants Surveys were undertaken with 602 consumers and 91 community pharmacists. Results Community pharmacy is predominantly used to obtain advice about medication and whether a doctor's visit is necessary, as well as for monitoring and screening services. Pharmacy services that were patient centric were important, such as individualised medication advice and respectful care, as well as tools or procedures to facilitate streamlined medication access. Less important services included adult vaccinations and health and wellness programmes. Carers identified services that assisted them with their specific role/s to be important. Overall, community pharmacists had a good understanding of the services that were important to people with chronic conditions and their carers. Conclusions People with chronic conditions and their carers not only care about what services are delivered, but how they are delivered; they sought services that generally improved their access to medication and information, but in a way that was patient centred. Ultimately, pharmacists understood the importance of patient-centred care for people with chronic conditions and their carers, perhaps indicating a greater acceptance of integrating patient-centred care into their everyday practice

Consumer Privacy and Product Steering versus Price Discrimination in the Online Market

The following master thesis aims to study consumer privacy, where I focus on price discrimination and steering in the online market. I look closer at whether consumers will benefit from revealing their preferences or not to the seller. The consumer will need to consider the benefits of more accurate recommendations and possible consequences of higher product prices. My topic question is as follows: Will consumers benefit from voluntarily disclosing their information to the seller? I present two models by Hidir and Vellodi (2021) and Ichihashi (2020), to help find answers to the topic question. The models study the price implications of consumers’ privacy and welfare in the online market. Hidir and Vellodi (2021) focus on price discrimination and introduce incentive-compatible market segmentation. To ensure trade over relevant products, Hidir and Vellodi (2021) state that the consumer needs to partially reveal their information with pooling segments wide enough to keep the prices low and narrow enough to get trade with relevant products. Ichihashi (2020), with a focus on steering, studies a multi-product seller either with a commitment or no-commitment pricing regimes. A consumer discloses information to the seller, which learns the consumer’s preferences, sets prices, and makes product recommendations.MasteroppgaveECON391MASV-SØKPROF-SØ