| 15:00-16:00, Friday 18th November, 2022
Video recording
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The A3AM Decision Support Platform: Accessible AI Assistance for
Additive Manufacturing |
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Cathal Hoare
School of Computer Science & IT, University College Cork/
I-Form
Centre, University College Dublin
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The European Commission through its recent document ‘Industry 5.0, a
transformative vision for Europe’, has highlighted the role that digital
technologies will play in enabling more sustainable economic models. A
separate EIT report went on to highlight the need to pilot AI in production
processes, in areas such as predictive maintenance, particularly for SMEs,
which as the report highlighted, are more conservative and risk averse.
However, the ability of SMEs to adopt this
technology is challenged by knowledge shortages with the need to up-skill
considered crucial.
The A3AM Decision Support Platform provides SMEs with a means to pilot the
adoption of AI without a need to develop organic artificial intelligence
expertise. Advanced forms of analysis are made accessible, while rules can
be created to capture in-house manufacturing expertise, allowing it to be
reapplied and scaled to larger manufacturing endeavours. The platform
successfully facilitates almost real-time feedback on process data to
operators, with decision support based on data analysis, as well as the
results of previous process runs. This system has been successfully trialled
for additive manufacturing processes (laser powder bed fusion), on
production scale processes, including those within industry.
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Cathal Hoare is a commercialisation researcher in I-Form Centre and is based in
UCD. Mr Hoare is a graduate of Computer Science at University College Cork. He
has worked for several companies, including multinationals and start-ups,
working on data management solutions in domains ranging from telecoms to
commercial analytics. On returning to academia, Mr Hoare has worked on SFI, NSF
and Europa funded projects across a range of functions, but primarily working to
build both data integration, high volume high velocity data management solutions
and analytics platforms that promote AI adoption by SMEs in the area of
manufacturing.
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| 15:00-16:00, Friday 11th November, 2022
Video recording
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Building User Trust in Recommender Systems: Progress and Challenges |
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Dr. Li Chen,
Associate Head (Research) and Associate Professor,
Department of Computer Science,
Hong Kong Baptist University, China
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With the popular applications of AI technologies in various aspects of our daily
life, building human trust in an AI system becomes necessary and important.
However, though increasing attention has been paid to trustworthy AI, most are
from a computational perspective, rather than from the end user's perspective.
In this talk, I will introduce our endeavors to promote user trust in
recommender systems (RS) by considering three particular types of factors:
system-related factors (such as recommendation explanation), user-related
factors (such as personality), and context-related factors (such as task
complexity). The findings from a series of user studies in terms of the impact
of each factor as well as their combined effects on user trust formation will be
shared. The work may contribute to deriving practical design implications for
trustworthy RS, and leads to several challenging issues that are worth further
investigation.
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Dr. Li Chen’s recent research focus is on human-centered AI with the main
emphasis on recommender systems and complex decision support systems. The
applications cover multiple domains including social media, e-commerce, online
education, and public health. She has authored and co-authored over 120
publications (with 7,600 citations so far) and received several awards, such as
the SIGCHI’22 Honorable Mention Award, UMAP’20 Best Student Paper Award, UMUAI
2018 Best Paper Award, and UMAP’15 Best Student Paper Award. She is included in
the list of the world’s top 2% most-cited scientists by Stanford University.
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| 15:00-16:00, Friday 4th November, 2022
Video recording
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CommPAL: A Decision Support System for Palliative Care in the
Community |
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Ciara Heavin,
Professor of Business Information Systems at Cork University Business
School,
University College Cork, Ireland
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By 2050 the global over 65s population will double to 1.5 billion and as people
live longer, the relative size of the labour force will shrink meaning there
will be less healthcare budget per older person. In Ireland, it is expected
there will be an 84% increase in the number of people requiring palliative care
by 2046. Current healthcare delivery models overly rely on the acute hospital
system where challenges with infection control, increased mortality, and other
adverse effects are more likely. National health decision makers have no choice
but to deliver care in the community because as a cost-effective, scalable, and
safe solution. To achieve this, community-based specialist palliative care
services must develop a flexible, innovative response. CommPAL is an AI-driven
decision support system (DSS) for use by healthcare staff to support the
delivery of specialised palliative healthcare in the community. The predictive
analytics component assesses patient care needs and the stability of a patient’s
condition to help triage patients in their homes, while the prescriptive
analytics component allocates resources in a fair and transparent way. These
components work together in a novel DSS to present healthcare decision-makers
with the right data at the right time with the aim of maximising the number of
patients cared for to a high standard of care, while also supporting Clinical
Nursing Specialists (CNS) with an optimum and transparent plan to discharge
their roles. Our aim is to develop a data-driven approach to support changing
complex health needs by enabling the assessment and triaging of the sickest
patients in the community.
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Ciara Heavin is Professor of Business Information Systems at Cork University
Business School, University College Cork, Ireland. Her research focuses on
opportunities for using information systems (IS) in the global healthcare
ecosystem and in digital transformation. Ciara Heavin has directed funded
research in the investigation, development, and implementation of innovative
technology solutions in the healthcare domain. She has published articles in top
international IS/HIS journals and conference proceedings.
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| 15:00-16:00, Friday 14th October, 2022
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Counterfactual Explanations and How to Find Them |
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Professor Riccardo Guidotti
University of Pisa
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Interpretable machine learning aims at unveiling the reasons behind predictions
returned by uninterpretable classifiers. One of the most valuable types of
explanation consists of counterfactuals. A counterfactual explanation reveals
what should have been different in an instance to observe a diverse outcome. For
instance, a bank customer asks for a loan that is rejected. The counterfactual
explanation consists of what should have been different for the customer in
order to have the loan accepted. Recently, there has been an explosion of
proposals for counterfactual explainers. The aim of this work is to survey the
most recent explainers returning counterfactual explanations. We categorize
explainers based on the approach adopted to return the counterfactuals, and we
label them according to characteristics of the method and properties of the
counterfactuals returned. In addition, we visually compare the explanations, and
we report quantitative benchmarking assessing minimality, actionability,
stability, diversity, discriminative power, and running time. The results make
evident that the current state of the art does not provide a counterfactual
explainer able to guarantee all these properties simultaneously.
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Riccardo Guidotti graduated cum laude in Computer Science (MS and BS) at
University of Pisa in 2013 and 2010. He received the PhD in Computer Science
with a thesis on Personal Data Analytics in the same institution. He is
currently an Assistant Professor at the Department of Computer Science
University of Pisa, Italy, and a member of the Knowledge Discovery and Data
Mining Laboratory (KDDLab), a joint research group with the Information Science
and Technology Institute of the National Research Council in Pisa. His research
interests are in explainable artificial intelligence, interpretable machine
learning, quantum computing, fairness, and bias detection, data generation and
causal models, personal data mining, clustering, analysis of transactional data.
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| 15:00-16:00, Friday 7th October, 2022
Video recording
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Abstraction and Analogy are the Keys to Robust AI |
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Melanie Mitchell
Davis Professor, Santa Fe Institute
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In 1955, John McCarthy and colleagues proposed an AI summer research project
with the following aim: “An attempt will be made to find how to make
machines use language, form abstractions and concepts, solve kinds of
problems now reserved for humans, and improve themselves.” More than six
decades later, all of these research topics remain open and actively
investigated in the AI community. While AI has made dramatic progress over
the last decade in areas such as vision, natural language processing, and
robotics, current AI systems still almost entirely lack the ability to form
humanlike concepts and abstractions.
In this talk, I will argue that the inability to form conceptual
abstractions—and to make abstraction-driven analogies—is a primary source of
brittleness in state-of-the-art AI systems, which often struggle in adapting
what they have learned to situations outside their training regimes. I will
reflect on the role played by analogy-making at all levels of intelligence,
and on the prospects for developing AI systems with humanlike abilities for
abstraction and analogy.
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Melanie Mitchell is the Davis Professor at the Santa Fe Institute. Her current
research focuses on conceptual abstraction, analogy-making, and visual
recognition in artificial intelligence systems. Melanie is the author or editor
of six books and numerous scholarly papers in the fields of artificial
intelligence, cognitive science, and complex systems. Her book Complexity: A
Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book
Award and was named by Amazon.com as one of the ten best science books of 2009.
Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar,
Straus, and Giroux).
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| 15:00-16:00, Friday 1st April, 2022
Video recording
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Sensor interpolation data wrangling |
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Professor Simon Dobson
School of Computer Science, University of St Andrews, Scotland UK
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Sensor networks are becoming increasingly common, but the torrent of data they
provide is not without its problems. It's intuitively clear that issues such as
the placement of the sensors, their accuracy, the degradations caused by
physical wear and tear, and deliberate attacks will all affect the confidence we
should place in the conclusions we draw from the data collected, but we have
only a limited understanding of how these issues affect what we observe. This
talk describes work in progress to explore the effects of placement and error
using rainfall datasets collected across the UK, and the challenges that come
from working with such data in terms of access, quality, and scale.
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Simon Dobson is a Professor of Computer Science at the University of St Andrews,
interested in complex systems, sensors, and data analytics.
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15:00-16:00, Friday 11th March, 2022
Video recording
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Succinct tree sequences for megasample genomics |
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Dr. Jerome Kelleher,
Group Leader in Biomedical Data Science. Big Data Institute, University of
Oxford
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Genomics is now one of the largest producers of data in the world, with several
countries sequencing the genomes of a significant fractions of their
populations. This data tsunami has led to tremendous advances in our
understanding of human disease and evolutionary processes in general, but it
presents stark challenges to computational methods. In this talk, Jerome will
discuss a recently introduced data structure, the Succinct Tree Sequence, which
has already led to performance advances of several orders of magnitude in genome
simulation, ancestry inference and statistical calculation, and can potentially
solve some of the most pressing problems of scale in genomics.
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Jerome develops genealogy-based methods and software platforms for large scale
genomics, combining classical computer science, population genetics theory and
modern software development practices.
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| 15:00-16:00, Friday 4th March, 2022
Video recording
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Extending the Data Science Pipeline: Integrating Machine Learning
into Edge Environments |
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Professor Omer Rana, Cardiff University, UK
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Internet of Things (IoT) applications today involve data capture from sensors
and devices that are close to the phenomenon being measured, with such data
subsequently being transmitted to Cloud data centre for storage, analysis and
visualisation. Currently devices used for data capture often differ from those
that are used to subsequently carry out analysis on such data. Increasing
availability of storage and processing devices closer to the data capture
device, perhaps over a one-hop network connection or even directly connected to
the IoT device itself, requires more efficient allocation of processing across
such edge devices and data centres. Supporting machine learning & data analytics
directly on edge devices also enables support for distributed (federated)
learning, enabling user devices to be used directly in the inference or learning
process. Scalability in this context needs to consider both cloud resources,
data distribution and initial processing on edge resources closer to the user.
This talk investigates how a data analytics pipeline can be deployed across the
cloud-edge continuum. Understanding what should be executed at a data centre and
what can be moved to an edge resource remains an important challenge --
especially with increasing capability of our edge devices. The following
questions are addressed in this talk:
- How do we partition machine learning algorithms across
Edge-Network-Cloud resources (often referred to as the "Cloud-Edge
Continuum") based on constraints such as privacy, capacity and
resilience?
- Can machine learning algorithms be adapted based on the characteristics
of devices on which they are hosted? What does this mean for stability/
convergence vs. performance?
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Omer F. Rana is a Professor of Performance Engineering at Cardiff University,
with research interests in high performance distributed computing, data
analysis/mining and multi-agent systems. He is the Dean of International for the
Physical Sciences and Engineering College.
Web link: Professor Omer
Rana - People - Cardiff University
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| 15:00-16:00, Friday 18th February, 2022
Video recording
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Anomaly Detection in Electric Motors with Variational Autoencoders
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Dr Andrea Visentin,
Lecturer at the School of Computer Science and Information Technology,
University College Cork
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Induction motors are the primary way to convert electrical power into mechanical
power. They are a fundamental component of industrial processes and equipment.
Faults can occur due to misusage, wearing or external conditions. Early fault
detection and preventive maintenance are of great concern. Faults generally
manifest with symptoms such as excessive mechanical vibrations, asymmetry of
current and voltages, unbalanced air gap, etc. In the last few years, many
noninvasive data-driven approaches have been used to detect faults in electric
motors. This approach presents two shortcomings: some faults are easier to
detect using a specific sensor (e.g., vibration); in industrial applications, it
is hard to obtain fault measurements. To overcome these limits, we are working
in a multi-signal unsupervised anomaly detection system. This sensor samples the
motor vibration, current and magnetic flux at 20 minutes intervals. We used a
series of variational autoencoders to detect the anomalies. During the training
phase, the software learns the normal working condition of the motor. The system
is currently deployed in three industrial motors at the Tyndall National
Institute. The experimental results show that the system is accurate in anomaly
detection up to one hour earlier than the existing sensor and can recognize a
change in the motor working conditions.
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Andrea's research focuses on deep learning and stochastic optimisation applied
to inventory control, demand prediction, fault detection, and boolean
satisfiability. He collaborates with the Insight Centre for Data Analytics, the
CRT AI Center, the Confirm Centre for Smart Manufacturing, and the TAILOR
research network.
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15:00-16:00, Friday 11th February, 2022
Video recording
Slides
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Software requirements for vulnerable groups – the value of Design
patterns |
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Ita Richardson
Professor of Software Quality, Department of Computer Science and Information
Systems, University of Limerick and
Principal Investigator, Lero – the
Science Foundation Ireland Research Centre for Software
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As software becomes more pervasive, it is important that we, as software
engineers, do not make the assumption that ‘one size fits all’. Our research has
shown that, different people and groups of people have different requirements.
Furthermore, there are groups in society who are particularly vulnerable from a
healthcare perspective. Of concern is that, as technology develops, these groups
are becoming more forgotten where the development of software is concerned. To
overcome these issues, we have undertaken research to develop ‘generic’ software
requirements for two such groups - older adults and people with mild
intellectual and developmental disability. These are presented in design pattern
format which can be easily used as guides by software engineers. In this talk, I
will discuss the research methods and outcomes used to identify these
requirements, and illustrate how the resulting design patterns can be used.
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Prof Ita Richardson, a University of Limerick graduate, commenced her academic
career with UL in 1992, where she teaches undergraduate and postgraduate
students. She has led a Lero research team since 2002, comprising of
post-graduate students, post-doctoral researchers, research fellows and senior
research fellows, and has international recognition in the Software Engineering
community as a Software Quality and Digital Health researcher.
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| 15:00-16:00, Friday 4th February, 2022
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Impact of Scientific Research and Publishing in High Quality Computer Science
Journals
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Dr. Mubashir Husain Rehmani
Assistant Lecturer, Department of Computer Science, Munster Technological
University (MTU), Ireland
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When applying to national, EU-based, or International funding bodies, we often
need to mention the impact of our research. Additionally, PhD students and early
career researchers strive to publish in high quality computer science journals.
In this talk, we will be covering both aspects. We first discuss in detail how
the scientific publication process works - starting from very basic concepts
regarding bibliometrics to peer-review process and from identifying top ranked
journals to publication in those journals. We then discuss what it really means
when we say 'Impact of Scientific Research'. Finally, we will discuss tips to
publish in these top ranked journals along with ethics in scientific research.
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Mubashir Husain Rehmani (M’14-SM’15) received the B.Eng. degree in computer
systems engineering from Mehran University of Engineering and Technology,
Jamshoro, Pakistan, in 2004, the M.S. degree from the University of Paris XI,
Paris, France, in 2008, and the Ph.D. degree from the University Pierre and
Marie Curie, Paris, in 2011. He is currently working as Assistant Lecturer in
the Department of Computer Science, Munster Technological University (MTU),
Ireland. He has been selected for inclusion on the annual Highly Cited
Researchers™ 2020 and 2021 list from Clarivate.
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| 15:00-16:00, Friday 21st January, 2022
Video recording
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Envisioning, designing, and rapid prototyping heritage installations with a
tangible interaction toolkit
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Professor Luigina Ciolfi
Professor of Human Computer Interaction, School of Applied Psychology, UCC
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A body of HCI work focuses on cultural heritage settings, such as museums,
galleries, and historic sites. In recent years, the focus broadened from
creating interventions and evaluating them with visitors, to realizing
open-ended technological platforms that cultural heritage professionals (such as
exhibition designers and interpretation officers) can adapt and appropriate. A
common approach is the creation of toolkits, where software and – in some cases
– hardware, and related support materials are made available to non-experts to
realize digital experiences in museums and exhibitions. This talk will discuss
the Tangible Interaction Toolkit, an online content editing environment and
related hardware kit, that enables cultural heritage professionals (CHPs) and
exhibition designers to create and adapt tangible installations in museums.
Tangible interaction “encompasses a broad range of systems and interfaces
relying on embodied interaction, tangible manipulation and physical
representation (of data), embeddedness in real space and digitally augmenting
physical spaces” (Hornecker & Buur, 2006, p. 437). The Toolkit is the outcome of
meSch (Material EncounterS with Digital Cultural Heritage), an extended research
project that involved over twenty researchers (HCI and computer scientists,
designers, and museum professionals) in an iterative process of co-design and
evaluation that lasted 4 years. Our effort was to make powerful and complex
platforms and technologies, such as the Internet of Things and personalisation
techniques, usable by CHPs. The meSch Toolkit offers multiple entry points to
accommodate different levels of expertise and attitudes: from the simple
repurposing of existing blueprints of interactive installations, to the
extension of the hardware platform with new sensors and actuators. The talk will
focus particularly on the methodological framing of evaluation workshops with
external users using the Toolkit for envisioning, designing and prototyping
tangible interactive installations.
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Luigina Ciolfi is Professor of Human Computer Interaction in the School of
Applied Psychology at UCC. An experienced scholar in Human-Computer Interaction
(HCI) and Computer-Supported Cooperative Work (CSCW), she researches the
understanding, practicing and designing of digital interactive systems from a
socio-technical perspective. She is an ACM Senior Member and ACM Distinguished
Speaker, and a Member of the British Psychological Society.
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| 15:00-16:00, Friday 14th January, 2022
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Using Students' Data Footprints for their own Wellness and Wellbeing
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Prof Alan Smeaton,
Insight Centre for Data Analytics, Dublin City University
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The presentation will cover a range of topics and will be of interest to
those interested in data analytics and machine learning, chronobiology,
personal data, student wellness, and educational analytics. Our students
leave digital footprints all over their social media and their web browsing
and their other online and offline (real world) activities. They also leave
digital footprints from their online University activities and that offers
the opportunity of whether there is anything useful we as a University can
do with this. In the past we have used out-of-the box machine learning
techniques to use these University-based digital footprints to predict
students' module/course outcomes and we've shown for 000's of students in
DCU in their first year, how we can help them by alerting them as to their
progress and likely outcomes for those high-failure modules. That results in
an increased pass rate in DCU and students heading to the US on J1 visas
instead of having to repeat modules during their Summertime. If all we are
interested in is helping students pass exams that's fine, but can we do more
?
The FLOURISH module at DCU and UCD focus on overall student wellness and
wellbeing and is like similar modules taught elsewhere at places like Yale
and Berkeley. However I believe that those wellness or science of happiness
courses elsewhere miss a trick in that they do not use students' personal
data in any way. The syllabus for FLOURISH at DCU covers topics including
sleep, nutrition, physical activity, behaviour change, digital footprints,
cognitive psychology, healthy choices and more. For most of these there is a
personal data aspect where students get to see how their own personal data
can be used by them as a force for their own good as they learn about those
wellness-related topics. In addition to gathering and using their own
personal data for these topics, we also use some of the digital footprints
that the University gathers to feed back indicators of their overall
periodicity intensity. The overall outcome is that students learn about
aspects of their wellness and wellbeing which are not taught elsewhere and
which they may mis-learn from unreliable sources such as social media or
their peers. Additionally, the personal data aspects of their own digital
literacy is enhanced by them seeing their data used for good.
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Alan Smeaton has been Professor of Computing at DCU since 1997. He is an IEEE
Fellow and Principal Fellow of the AdvanceHE as well as a member of the RIA and
RIA Gold Medal Winner. He has a broad range of research interests, mostly around
helping people to find information and helping information to find people.
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Andrea Visentin, Ph.D Student, Insight@UCC
