6 research outputs found
RoboREIT: an Interactive Robotic Tutor with Instructive Feedback Component for Requirements Elicitation Interview Training
[Context] Interviewing stakeholders is the most popular requirements
elicitation technique among multiple methods. The success of an interview
depends on the collaboration of the interviewee which can be fostered through
the interviewer's preparedness and communication skills. Mastering these skills
requires experience and practicing interviews. [Problem] Practical training is
resource-heavy as it calls for the time and effort of a stakeholder for each
student which may not be feasible for a large number of students. [Method] To
address this scalability problem, this paper proposes RoboREIT, an interactive
Robotic tutor for Requirements Elicitation Interview Training. The humanoid
robotic component of RoboREIT responds to the questions of the interviewer,
which the interviewer chooses from a set of predefined alternatives for a
particular scenario. After the interview session, RoboREIT provides contextual
feedback to the interviewer on their performance and allows the student to
inspect their mistakes. RoboREIT is extensible with various scenarios.
[Results] We performed an exploratory user study to evaluate RoboREIT and
demonstrate its applicability in requirements elicitation interview training.
The quantitative and qualitative analyses of the users' responses reveal the
appreciation of RoboREIT and provide further suggestions about how to improve
it. [Contribution] Our study is the first in the literature that utilizes a
social robot in requirements elicitation interview education. RoboREIT's
innovative design incorporates replaying faulty interview stages and allows the
student to learn from mistakes by a second time practicing. All participants
praised the feedback component, which is not present in the state-of-the-art,
for being helpful in identifying the mistakes. A favorable response rate of 81%
for the system's usefulness indicates the positive perception of the
participants.Comment: Author submitted manuscrip
Exploring Emerging Technologies for Requirements Elicitation Interview Training: Empirical Assessment of Robotic and Virtual Tutors
Requirements elicitation interviews are a widely adopted technique, where the
interview success heavily depends on the interviewer's preparedness and
communication skills. Students can enhance these skills through practice
interviews. However, organizing practice interviews for many students presents
scalability challenges, given the time and effort required to involve
stakeholders in each session. To address this, we propose REIT, an extensible
architecture for Requirements Elicitation Interview Training system based on
emerging educational technologies. REIT has components to support both the
interview phase, wherein students act as interviewers while the system assumes
the role of an interviewee, and the feedback phase, during which the system
assesses students' performance and offers contextual and behavioral feedback to
enhance their interviewing skills. We demonstrate the applicability of REIT
through two implementations: RoREIT with a physical robotic agent and VoREIT
with a virtual voice-only agent. We empirically evaluated both instances with a
group of graduate students. The participants appreciated both systems. They
demonstrated higher learning gain when trained with RoREIT, but they found
VoREIT more engaging and easier to use. These findings indicate that each
system has distinct benefits and drawbacks, suggesting that REIT can be
realized for various educational settings based on preferences and available
resources.Comment: Author submitted manuscrip
Матеріали 1-го симпозіуму з передових освітніх технологій - Том 1: AET
Матеріали 1-го симпозіуму з передових освітніх технологій.Proceedings of the 1st Symposium on Advances in Educational Technology
Community memories for sustainable societies: The case of environmental noise
Sustainability is the main challenge faced by humanity today on global and local scales. Most environmental problems can be seen as the tragic overexploitation of a commons. In this dissertation we investigate how the latest developments within computer science and ICT can be applied to establish participatory, low-cost tools and practices that enable citizens to monitor, raise awareness about, and contribute to the sustainable management of the commons they rely on, and thereby protect or improve their quality of life. As a general approach we propose the use of community memories – as central data repositories and points of interaction for community members and other stakeholders – and the novel combination of participatory mobile sensing and social tagging – as a low-cost means to collect quantitative and qualitative data about the state of the commons and the health, well-being, behaviour and opinion of those that depend on it. Through applied, interdisciplinary research we develop a concrete solution for a specific, socially relevant problem, namely that of environmental noise – commonly referred to as noise pollution. Under the name NoiseTube we present an operational system that enables a participatory, low-cost approach to the assessment of environmental noise and its impact on citizens’ quality of life. This approach can be applied in the scope of citizen- or authority-led initiatives. The NoiseTube system consists of a sensing application – which turns mobile phones into a sound level meters and allows users to comment on their experience via social tagging – and a community memory – which aggregates and processes data collected by participants anywhere. The system supports and has been tested and deployed at different levels of scale – personal, group and mass sensing. Since May 2009 NoiseTube has been used by hundreds, if not thousands, of people all around the world, allowing us to draw lessons regarding the feasibility of different deployment, collaboration and coordination scenarios for participatory sensing in general. While similar systems have been proposed ours is the completest and most widely used participatory noise mapping solution to date. Our validation experiments demonstrate that the accuracy of mobile phones as sound level meters can be brought to an acceptable level through calibration and statistical reasoning. Through coordinated NoiseTube campaigns with volunteering citizens we establish that participatory noise mapping is a suitable alternative for, or a valuable complement to, conventional methods applied by authorities
Linked Democracy
This open access book shows the factors linking information flow, social intelligence, rights management and modelling with epistemic democracy, offering licensed linked data along with information about the rights involved. This model of democracy for the web of data brings new challenges for the social organisation of knowledge, collective innovation, and the coordination of actions. Licensed linked data, licensed linguistic linked data, right expression languages, semantic web regulatory models, electronic institutions, artificial socio-cognitive systems are examples of regulatory and institutional design (regulations by design). The web has been massively populated with both data and services, and semantically structured data, the linked data cloud, facilitates and fosters human-machine interaction. Linked data aims to create ecosystems to make it possible to browse, discover, exploit and reuse data sets for applications. Rights Expression Languages semi-automatically regulate the use and reuse of content. ; Links information flow, social intelligence, rights management, and modelling with epistemic democracy Presents examples of regulatory and institutional desig