YourMOOC4all: a MOOCs inclusive design and useful feedback research project

User feedback can be of great value for the development of guidelines to design MOOC platforms, courses, and open educational resources. Considering other learners’ experiences may benefit the development of course recommender systems that consider not only the quality of the content but also the level of accessibility to address disabled learners needs. In this paper a novel design for a recommender website is introduced which collects user feedback requests for Massive Open Online Courses (MOOCs), offering the possibility to freely rate the taken courses following Universal Design for Learning (UDL) principles. The development of this website, which is currently in a pilot process by UNED, will gather valuable information directly from the learners themselves to improve aspects such as the educational quality, accessibility, and usability of this open learning environment advising about the missing means regarding inclusive design

The Effect of Security Education and Expertise on Security Assessments: the Case of Software Vulnerabilities

In spite of the growing importance of software security and the industry demand for more cyber security expertise in the workforce, the effect of security education and experience on the ability to assess complex software security problems has only been recently investigated. As proxy for the full range of software security skills, we considered the problem of assessing the severity of software vulnerabilities by means of a structured analysis methodology widely used in industry (i.e. the Common Vulnerability Scoring System (\CVSS) v3), and designed a study to compare how accurately individuals with background in information technology but different professional experience and education in cyber security are able to assess the severity of software vulnerabilities. Our results provide some structural insights into the complex relationship between education or experience of assessors and the quality of their assessments. In particular we find that individual characteristics matter more than professional experience or formal education; apparently it is the \emph{combination} of skills that one owns (including the actual knowledge of the system under study), rather than the specialization or the years of experience, to influence more the assessment quality. Similarly, we find that the overall advantage given by professional expertise significantly depends on the composition of the individual security skills as well as on the available information.Comment: Presented at the Workshop on the Economics of Information Security (WEIS 2018), Innsbruck, Austria, June 201

Quantifying Privacy: A Novel Entropy-Based Measure of Disclosure Risk

It is well recognised that data mining and statistical analysis pose a serious treat to privacy. This is true for financial, medical, criminal and marketing research. Numerous techniques have been proposed to protect privacy, including restriction and data modification. Recently proposed privacy models such as differential privacy and k-anonymity received a lot of attention and for the latter there are now several improvements of the original scheme, each removing some security shortcomings of the previous one. However, the challenge lies in evaluating and comparing privacy provided by various techniques. In this paper we propose a novel entropy based security measure that can be applied to any generalisation, restriction or data modification technique. We use our measure to empirically evaluate and compare a few popular methods, namely query restriction, sampling and noise addition.Comment: 20 pages, 4 figure

CAPTCHaStar! A novel CAPTCHA based on interactive shape discovery

Over the last years, most websites on which users can register (e.g., email providers and social networks) adopted CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) as a countermeasure against automated attacks. The battle of wits between designers and attackers of CAPTCHAs led to current ones being annoying and hard to solve for users, while still being vulnerable to automated attacks. In this paper, we propose CAPTCHaStar, a new image-based CAPTCHA that relies on user interaction. This novel CAPTCHA leverages the innate human ability to recognize shapes in a confused environment. We assess the effectiveness of our proposal for the two key aspects for CAPTCHAs, i.e., usability, and resiliency to automated attacks. In particular, we evaluated the usability, carrying out a thorough user study, and we tested the resiliency of our proposal against several types of automated attacks: traditional ones; designed ad-hoc for our proposal; and based on machine learning. Compared to the state of the art, our proposal is more user friendly (e.g., only some 35% of the users prefer current solutions, such as text-based CAPTCHAs) and more resilient to automated attacks.Comment: 15 page

Trends in virtual reality technologies for the learning patient

NextMed convened the Medicine Meets Virtual Reality 22 (MMVR 22) conference in 2016. Since 1992, the conference has brought together a diverse group of researchers to share creative solutions for the evolving challenge of integrating virtual reality tools into medical education. Virtual reality (VR) and its enabling technologies utilize hardware and software to simulate environments and encounters where users can interact and learn. The MMVR 22 symposium proceedings contain projects that support a variety of learners: medical students, practitioners, soldiers, and patients. This report will contemplate the trends in virtual reality technologies for patients navigating their medical and healthcare learning. The learning patient seeks more than intervention; they seek prevention. From virtual humans and environments to motion sensors and haptic devices, patients are surrounded by increasingly rich and transformative data-driven tools. Applied data enables VR applications to simulate experience, predict health outcomes, and motivate new behavior. The MMVR 22 presents investigations into the usability of wearable devices, the efficacy of avatar inclusion, and the viability of multi-player gaming. With increasing need for individualized and scalable programming, only committed open source efforts will align instructional designers, technology integrators, trainers, and clinicians. Curriculum and InstructionCurriculum and Instructio

Design as conversation with digital materials

This paper explores Donald Schön's concept of design as a conversation with materials, in the context of designing digital systems. It proposes material utterance as a central event in designing. A material utterance is a situated communication act that depends on the particularities of speaker, audience, material and genre. The paper argues that, if digital designing differs from other forms of designing, then accounts for such differences must be sought by understanding the material properties of digital systems and the genres of practice that surround their use. Perspectives from human-computer interaction (HCI) and the psychology of programming are used to examine how such an understanding might be constructed.</p

Automated Generation of User Guidance by Combining Computation and Deduction

Herewith, a fairly old concept is published for the first time and named "Lucas Interpretation". This has been implemented in a prototype, which has been proved useful in educational practice and has gained academic relevance with an emerging generation of educational mathematics assistants (EMA) based on Computer Theorem Proving (CTP). Automated Theorem Proving (ATP), i.e. deduction, is the most reliable technology used to check user input. However ATP is inherently weak in automatically generating solutions for arbitrary problems in applied mathematics. This weakness is crucial for EMAs: when ATP checks user input as incorrect and the learner gets stuck then the system should be able to suggest possible next steps. The key idea of Lucas Interpretation is to compute the steps of a calculation following a program written in a novel CTP-based programming language, i.e. computation provides the next steps. User guidance is generated by combining deduction and computation: the latter is performed by a specific language interpreter, which works like a debugger and hands over control to the learner at breakpoints, i.e. tactics generating the steps of calculation. The interpreter also builds up logical contexts providing ATP with the data required for checking user input, thus combining computation and deduction. The paper describes the concepts underlying Lucas Interpretation so that open questions can adequately be addressed, and prerequisites for further work are provided.Comment: In Proceedings THedu'11, arXiv:1202.453