393,093 research outputs found
Soft Sides of Software
Software is a field of rapid changes: the best technology today becomes obsolete in the near future. If we review the graduate attributes of any of the software engineering programs across the world, life-long learning is one of them. The social and psychological aspects of professional development is linked with rewards. In organizations, where people are provided with learning opportunities and there is a culture that rewards learning, people embrace changes easily. However, the software industry tends to be short-sighted and its primary focus is more on current project success; it usually ignores the capacity building of the individual or team. It is hoped that our software engineering colleagues will be motivated to conduct more research into the area of software psychology so as to understand more completely the possibilities for increased effectiveness and personal fulfillment among software engineers working alone and in teams
Pervasive learning analytics for fostering learners' self-regulation
Today's tertiary STEM (Science, Technology, Engineering and Mathematics) education in Europe poses problems to both teachers and students.
With growing enrolment numbers, and numbers of teaching staff that are outmatched by this growth, student-teacher contact becomes more and more difficult to provide. Therefore, students are required to quickly adopt self-regulated and autonomous learning styles when entering European universities.
Furthermore, teachers are required to divide their attention between large numbers of students. As a consequence, classical teaching formats of STEM education which often encompass experimentation or active exploration, become harder to implement.
Educational software holds the promise of easing these problems, or, if not fully solving, at least of making them less acute: Learning Analytics generated by such software can foster self-regulation by providing students with both formative feedback and assessments. Educational software, in form of collaborative social media, makes it easier for teachers to collaborate, allows to reduce their workload and enables learning and teaching formats otherwise infeasible in large classes.
The contribution of this thesis is threefold: Firstly, it reports on a social medium for tertiary STEM education called "Backstage2 / Projects" aimed specifically at these points: Improving learners' self-regulation by providing pervasive Learning Analytics, fostering teacher collaboration so as to reduce their workload, and providing means to deploy a variety of classical and novel learning and teaching formats in large classes. Secondly, it reports on several case studies conducted with that medium which point at the effectiveness of the medium and its provided Learning Analytics to increase learners' self-regulation, reduce teachers' workload, and improve how students learn.
Thirdly, this thesis reports on findings from Learning Analytics which could be used in the future in designing further teaching and learning formats or case studies, yielding a rich perspective for future research and indications for improving tertiary STEM education
Collaborative tagging : folksonomy, metadata, visualization, e-learning, thesis
Collaborative tagging is a simple and effective method for organizing and sharing web resources using human created metadata. It has arisen out of the need for an efficient method of personal organization, as the number of digital resources in everyday lives increases. While tagging has become a proven organization scheme through its popularity and widespread use on the Web, little is known about its implications and how it may effectively be applied in different situations. This is due to the fact that tagging has evolved through several iterations of use on social software websites, rather than through a scientific or an engineering design process. The research presented in this thesis, through investigations in the domain of e-learning, seeks to understand more about the scientific nature of collaborative tagging through a number of human subject studies. While broad in scope, touching on issues in human computer interaction, knowledge representation, Web system architecture, e-learning, metadata, and information visualization, this thesis focuses on how collaborative tagging can supplement the growing metadata requirements of e-learning. I conclude by looking at how the findings may be used in future research, through using information based in the emergent social networks of social software, to automatically adapt to the needs of individual users
Application of Collaborative Learning Paradigms within Software Engineering Education: A Systematic Mapping Study
Collaboration is used in Software Engineering (SE) to develop software.
Industry seeks SE graduates with collaboration skills to contribute to
productive software development. SE educators can use Collaborative Learning
(CL) to help students develop collaboration skills. This paper uses a
Systematic Mapping Study (SMS) to examine the application of the CL educational
theory in SE Education. The SMS identified 14 papers published between 2011 and
2022. We used qualitative analysis to classify the papers into four CL
paradigms: Conditions, Effect, Interactions, and Computer-Supported
Collaborative Learning (CSCL). We found a high interest in CSCL, with a shift
in student interaction research to computer-mediated technologies. We discussed
the 14 papers in depth, describing their goals and further analysing the CSCL
research. Almost half the papers did not achieve the appropriate level of
supporting evidence; however, calibrating the instruments presented could
strengthen findings and support multiple CL paradigms, especially opportunities
to learn at the social and community levels, where research was lacking. Though
our results demonstrate limited CL educational theory applied in SE Education,
we discuss future work to layer the theory on existing study designs for more
effective teaching strategies.Comment: 7 page
Making Fair ML Software using Trustworthy Explanation
Machine learning software is being used in many applications (finance,
hiring, admissions, criminal justice) having a huge social impact. But
sometimes the behavior of this software is biased and it shows discrimination
based on some sensitive attributes such as sex, race, etc. Prior works
concentrated on finding and mitigating bias in ML models. A recent trend is
using instance-based model-agnostic explanation methods such as LIME to find
out bias in the model prediction. Our work concentrates on finding shortcomings
of current bias measures and explanation methods. We show how our proposed
method based on K nearest neighbors can overcome those shortcomings and find
the underlying bias of black-box models. Our results are more trustworthy and
helpful for the practitioners. Finally, We describe our future framework
combining explanation and planning to build fair software.Comment: New Ideas and Emerging Results (NIER) track; The 35th IEEE/ACM
International Conference on Automated Software Engineering; Melbourne,
Australi
Multi Agent Systems
Research on multi-agent systems is enlarging our future technical capabilities as humans and as an intelligent society. During recent years many effective applications have been implemented and are part of our daily life. These applications have agent-based models and methods as an important ingredient. Markets, finance world, robotics, medical technology, social negotiation, video games, big-data science, etc. are some of the branches where the knowledge gained through multi-agent simulations is necessary and where new software engineering tools are continuously created and tested in order to reach an effective technology transfer to impact our lives. This book brings together researchers working in several fields that cover the techniques, the challenges and the applications of multi-agent systems in a wide variety of aspects related to learning algorithms for different devices such as vehicles, robots and drones, computational optimization to reach a more efficient energy distribution in power grids and the use of social networks and decision strategies applied to the smart learning and education environments in emergent countries. We hope that this book can be useful and become a guide or reference to an audience interested in the developments and applications of multi-agent systems
Build Your Own Robot Friend: An Open-Source Learning Module for Accessible and Engaging AI Education
As artificial intelligence (AI) is playing an increasingly important role in
our society and global economy, AI education and literacy have become necessary
components in college and K-12 education to prepare students for an AI-powered
society. However, current AI curricula have not yet been made accessible and
engaging enough for students and schools from all socio-economic backgrounds
with different educational goals. In this work, we developed an open-source
learning module for college and high school students, which allows students to
build their own robot companion from the ground up. This open platform can be
used to provide hands-on experience and introductory knowledge about various
aspects of AI, including robotics, machine learning (ML), software engineering,
and mechanical engineering. Because of the social and personal nature of a
socially assistive robot companion, this module also puts a special emphasis on
human-centered AI, enabling students to develop a better understanding of
human-AI interaction and AI ethics through hands-on learning activities. With
open-source documentation, assembling manuals and affordable materials,
students from different socio-economic backgrounds can personalize their
learning experience based on their individual educational goals. To evaluate
the student-perceived quality of our module, we conducted a usability testing
workshop with 15 college students recruited from a minority-serving
institution. Our results indicate that our AI module is effective,
easy-to-follow, and engaging, and it increases student interest in studying
AI/ML and robotics in the future. We hope that this work will contribute toward
accessible and engaging AI education in human-AI interaction for college and
high school students.Comment: Accepted to the Proceedings of the AAAI Conference on Artificial
Intelligence (2024
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