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The purpose of this paper is to examine current approaches to interpretation of student evaluation data and present an innovative approach to developing benchmark targets for the effective and efficient use of these data.

This article discusses traditional approaches to gathering and using student feedback across the tertiary sector. The limitations of the customary use of the statistical mean as a quality measure of performance are presented and examined. An alternative method of interpreting student evaluation data is proposed and examples given.

The traditional use of the statistical mean to interpret student evaluation data has limitations. Focusing on data at the macro level provides subject teaching staff and managers with a clearer indication of student satisfaction. The use of a percentage satisfied and percentage dissatisfied metric to classify and rank subjects is presented as an efficient alternative to the traditional approach, while recognising the value of the statistical mean to interpret data at the micro level.

In light of the important role student feedback plays in determining university ranking, prioritising staff development and its potential function as an academic performance indicator, the effective interpretation of student evaluation data is critical. As economic factors become increasingly important to higher education providers, the role of evaluation data obtained from students will continue to gain traction. The identification of methods to fully capitalise on the value of these data, such as the one proposed in this article, is therefore crucial.

The use of mobile devices in handling our daily activities that involve the storage or access of sensitive data (e.g. on-line banking, paperless prescription services, etc.) is becoming very common. These mobile electronic services typically use a knowledge-based authentication method to authenticate a user (claimed identity). However, this authentication method is vulnerable to several security attacks. To counter the attacks and to make the authentication process more secure, this paper aims to investigate the use of touch dynamics biometrics in conjunction with a personal identification number (PIN)-based authentication method, and demonstrate its benefits in terms of strengthening the security of authentication services for mobile devices.

The investigation has made use of three light-weighted matching functions and a comprehensive reference data set collected from 150 subjects.

The investigative results show that, with this multi-factor authentication approach, even when the PIN is exposed, as much as nine out of ten impersonation attempts can be successfully identified. It has also been discovered that the accuracy performance can be increased by combining different feature data types and by increasing the input string length.

The novel contributions of this paper are twofold. Firstly, it describes how a comprehensive experiment is set up to collect touch dynamics biometrics data, and the set of collected data is being made publically available, which may facilitate further research in the problem domain. Secondly, the paper demonstrates how the data set may be used to strengthen the protection of resources that are accessible via mobile devices.

Reusing existing data sets of health information for public health or medical research has much to recommend it. Much data repurposing in medical or public health research or practice involves information that has been stripped of individual identifiers but some does not. In some cases, there may have been consent to the reuse but in other cases consent may be absent and people may be entirely unaware of how the data about them are being used. Data sets are also being combined and may contain information with very different sources, consent histories, and individual identifiers. Much of the ethical and policy discussion about the permissibility of data reuse has centered on two questions: for identifiable data, the scope of the original consent and whether the reuse is permissible in light of that scope, and for de-identified data, whether there are unacceptable risks that the data will be reidentified in a manner that is harmful to any data subjects. Prioritizing these questions rests on a picture of the ethics of data use as primarily about respecting the choices of the data subject. We contend that this picture is mistaken; data repurposing, especially when data sets are combined, raises novel questions about the impacts of research on groups and their implications for individuals regarded as falling within these groups. These impacts suggest that the controversies about de-identification or reconsent for reuse are to some extent beside the point. Serious ethical questions are also raised by the inferences that may be drawn about individuals from the research and resulting risks of stigmatization. These risks may arise even when individuals were not part of the original data set being repurposed. Data reuse, repurposing, and recombination may have damaging effects on others not included within the original data sets. These issues of justice for individuals who might be regarded as indirect subjects of research are not even raised by approaches that consider only the implications for or agreement of the original data subject. This chapter argues that health information should be available for reuse, information should be available for use, but in a way that does not yield unexpected surprises, produce direct harm to individuals, or violate warranted trust.

Single subject research has long been employed to evaluate intervention effectiveness with students with learning or behavioral disabilities. Typically, the results of single subject research are presented on graphic displays and analyzed by a method of visual inspection, in which analysts simultaneously consider such data elements as level change, slope change, and variability in baseline and treatment data. However, over the years several concerns regarding visual inspection have emerged, including relatively low inter-rater reliabilities. This chapter reviews the arguments in favor of visual inspection as an analytic tool, and also summarizes the arguments favoring statistical analysis of single case data. The use of randomization tests is recommended, and an example is provided of its use in research with students with learning and behavioral disorders.

This research aims to explore the research data ethics governance framework and collaborative network to optimize research data ethics governance practices, to balance the relationship between data development and utilization, open sharing, data security and to reduce the ethical risks that may arise from data sharing and utilization.

This study explores the framework and collaborative network of research data ethics policies by using the UK as an example. 78 policies from the UK government, university, research institution, funding agency, publisher, database, library and third-party organization are obtained. Adopting grounded theory (GT) and social network analysis (SNA), Nvivo12 is used to analyze these samples and summarize the research data ethics governance framework. Ucinet and Netdraw are used to reveal collaborative networks in policy.

Results indicate that the framework covers governance context, subject and measure. The content of governance context contains context description and data ethics issues analysis. Governance subject consists of defining subjects and facilitating their collaboration. Governance measure includes governance guidance and ethics governance initiatives in the data lifecycle. The collaborative network indicates that research institution plays a central role in ethics governance. The core of the governance content are ethics governance initiatives, governance guidance and governance context description.

This research provides new insights for policy analysis by combining GT and SNA methods. Research data ethics and its governance are conceptualized to complete data governance and research ethics theory.

A research data ethics governance framework and collaborative network are revealed, and actionable guidance for addressing essential aspects of research data ethics and multiple subjects to confer their functions in collaborative governance is provided.

This study analyzes policy text using qualitative and quantitative methods, ensuring fine-grained content profiling and improving policy research. A typical research data ethics governance framework is revealed. Various stakeholders' roles and priorities in collaborative governance are explored. These contribute to improving governance policies and governance levels in both theory and practice.

This chapter analyzes the compliance of some category of Open Data in Politics with EU General Data Protection Regulation (GDPR) requirements. After clarifying the legal basis of this framework, with specific attention to the processing procedures that conform to the legitimate interests pursued by the data controller, including open data licenses or anonymization techniques, that can result in partial application of the GDPR, but there is no generic guarantee, and, as a consequence, an appropriate process of analysis and management of risks is required.

The enforcement of the General Data Protection Regulation imposes specific privacy- and -security related requirements that any organisation that processes European Union citizens’ personal data must comply with. The application of privacy- and security-by-design principles are assisting organisation in achieving compliance with the Regulation. The purpose of this study is to assist data controllers in their effort to achieve compliance with the new Regulation, by proposing the adoption of the privacy level agreement (PLA). A PLA is considered as a formal way for the data controllers and the data subjects to mutually agree the privacy settings of a service provisioned. A PLA supports privacy management, by analysing privacy threats, vulnerabilities and information systems’ trust relationships.

However, the concept of PLA has only been proposed on a theoretical level. To this aim, two different domains have been selected acting as real-life case studies, the public administration and the health care, where special categories of personal data are processed.

The results of the evaluation of the adoption of the PLA by the data controllers are positive. Furthermore, they indicate that the adoption of such an agreement facilitates data controllers in demonstrating transparency of their processes. Regarding data subjects, the evaluation process revealed that the use of the PLA increases trust levels on data controllers.

This paper proposes a novel reference architecture to enable PLA management in practice and reports on the application and evaluation of PLA management.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.