|
Showing 1 - 5 of
5 matches in All Departments
|
Artificial Intelligence in Education - 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings (1st ed. 2023)
Ning Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, Vania Dimitrova
|
R3,647
Discovery Miles 36 470
|
Ships in 10 - 15 working days
|
This book constitutes the refereed proceedings of the 24th
International Conference on Artificial Intelligence in Education,
AIED 2023, held in Tokyo, Japan, during July 3-7, 2023. This event
took place in hybrid mode. The 53 full papers and 26 short papers
presented in this book were carefully reviewed and selected from
311 submissions. The papers present result in high-quality research
on intelligent systems and the cognitive sciences for the
improvement and advancement of education. The conference was hosted
by the prestigious International Artificial Intelligence in
Education Society, a global association of researchers and
academics specializing in the many fields that comprise AIED,
including, but not limited to, computer science, learning sciences,
and education.
As an area, Technology Enhanced Learning (TEL) aims to design,
develop and test socio-technical innovations that will support and
enhance learning practices of individuals and organizations.
Information retrieval is a pivotal activity in TEL and the
deployment of recommender systems has attracted increased interest
during the past years. Recommendation methods, techniques and
systems open an interesting new approach to facilitate and support
learning and teaching. The goal is to develop, deploy and evaluate
systems that provide learners and teachers with meaningful guidance
in order to help identify suitable learning resources from a
potentially overwhelming variety of choices. Contributions address
the following topics: i) user and item data that can be used to
support learning recommendation systems and scenarios, ii)
innovative methods and techniques for recommendation purposes in
educational settings and iii) examples of educational platforms and
tools where recommendations are incorporated.
As an area, Technology Enhanced Learning (TEL) aims to design,
develop and test socio-technical innovations that will support and
enhance learning practices of individuals and organizations.
Information retrieval is a pivotal activity in TEL and the
deployment of recommender systems has attracted increased interest
during the past years. Recommendation methods, techniques and
systems open an interesting new approach to facilitate and support
learning and teaching. The goal is to develop, deploy and evaluate
systems that provide learners and teachers with meaningful guidance
in order to help identify suitable learning resources from a
potentially overwhelming variety of choices. Contributions address
the following topics: i) user and item data that can be used to
support learning recommendation systems and scenarios, ii)
innovative methods and techniques for recommendation purposes in
educational settings and iii) examples of educational platforms and
tools where recommendations are incorporated.
|
Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners, Doctoral Consortium and Blue Sky - 24th International Conference, AIED 2023, Tokyo, Japan, July 3–7, 2023, Proceedings (1st ed. 2023)
Ning Wang, Genaro Rebolledo-Mendez, Vania Dimitrova, Noboru Matsuda, Olga C. Santos
|
R3,217
Discovery Miles 32 170
|
Ships in 12 - 17 working days
|
This volume constitutes poster papers and late breaking results
presented during the 24th International Conference on
Artificial Intelligence in Education, AIED 2023, Tokyo, Japan, July
3–7, 2023. The 65 poster papers presented were carefully reviewed
and selected from 311 submissions. This set of posters was
complemented with the other poster contributions submitted for the
Poster and Late Breaking results track of the AIED 2023
conference.Â
Recommender systems have shown to be successful in many domains
where information overload exists. This success has motivated
research on how to deploy recommender systems in educational
scenarios to facilitate access to a wide spectrum of information.
Tackling open issues in their deployment is gaining importance as
lifelong learning becomes a necessity of the current
knowledge-based society. Although Educational Recommender Systems
(ERS) share the same key objectives as recommenders for e-commerce
applications, there are some particularities that should be
considered before directly applying existing solutions from those
applications. Educational Recommender Systems and Technologies:
Practices and Challenges aims to provide a comprehensive review of
state-of-the-art practices for ERS, as well as the challenges to
achieve their actual deployment. Discussing such topics as the
state-of-the-art of ERS, methodologies to develop ERS, and
architectures to support the recommendation process, this book
covers researchers interested in recommendation strategies for
educational scenarios and in evaluating the impact of
recommendations in learning, as well as academics and practitioners
in the area of technology enhanced learning.
|
You may like...
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
|