DRAI Digitalized resources and Artificial Intelligence
TitleDRAI Digitalized resources and Artificial Intelligence
Acronym
DRAI
Complete Call for Papers
Overview
Because of the current possibilities of providing massive, digitalized resources and analysing an increasing number of students and teachers interactions (within our own institutions and elsewhere), this workshop is focused on collecting evidences on progress in managerial, methodological and operational issues involved developing these resources. There are many issues involved, from building digitalized infrastructures to modelling adaptive and personalised learning solutions to providing students and teachers with the appropriate support in their activities in new ways.
In 2018, the UNED set out a new strategic Plan for the Digitalization of the University to be developed in four years’ time. Being conscious of the current possibilities of taking advantage of its big numbers, its main goal is the development of a model of adaptive and personalised learning to support its students in new ways not yet possible. This strategic objective takes mainly shape in the ED3 project (Digital Distance Education based on Data). The ED3 project will make possible to build a framework that promotes evidence-based interventions to improve teaching and learning processes through an intelligent and responsible exploitation of data. The project is structured in the following phases:
- To develop and establish policies that will ensure that any application making use of data considers the possible social and ethical consequences.
- To identify, collect in a single repository, cure and make accessible all sources of existing data related to teaching and learning processes, with differentiated levels of access for different actors in the university. To analyse these data sources and make them available for the creation of useful knowledge for the improvement of teaching/learning processes, through analysis exploratory studies and operational models that facilitate the interpretation of results.
- To develop and promote interventions on the different elements of the teaching and learning processes, based on the evidence provided by data and the knowledge of the different actors, to obtain a positive impact on the academic success of our students.
- To develop and apply data mining techniques and predictive models for teaching and learning processes, focused on the personalisation of learning resources, so that they are automatically adapted to each student.
- Problems and solutions related to collecting, curing and making accessible all sources of existing data related to teaching and learning processes, with differentiated levels of access for different actors in the university.
- Advancement in modelling adaptive and personalised resources to support students and teachers in new ways.
- Challenges for screening digitalized resources of value to support the creation of useful materials based on knowledge coming from previous teaching/learning successful experiences.
- Standards based tools to cope with the wide range of massive data and resources that can be collected to enrich the teaching and learning experiences.
- Authoring support for the creation of new digitalized materials that can combine different information sources coming from multimedia learning objects repositories and written contents which deals with the interoperability and scalability issues involved.
- Standards based tools to cope with the wide range of massive data and resources that can be collected to enrich the teaching and learning experiences.
- Authoring support for the creation of new digitalized materials that can combine different information sources coming from multimedia learning objects repositories and written contents which deals with the interoperability and scalability issues involved.
- Developing support programmes for the academic staff to provide them with all the necessary training in the use of the new tools and possibilities.
- Improving communication strategies and the training of students in the use of the new tools and resources available bearing in mind ethical and interaction issues involved such as those related to privacy, accessibility and explainability of artificial intelligence-based solutions.
- Combining exploratory studies and operational models that facilitate the interpretation of results.
- Promoting participation of teachers and learners in a symbiotic manner so that both get enriched with sharing successful usage of digitalized resources either from the teaching or learning viewpoints.
- Outcomes from developing data mining techniques and predictive models for teaching and learning processes, focused on the personalisation of learning resources, so that they are automatically adapted to each student.
- Inclusiveness, Fairness and Explanations in deploying massive digitalisation at HE.
- Institutional policies to promote transparency and consent, responsibility, privacy, validity and access in managing data.
- Challenges of Assessment at Higher Education Institutions: impact of massive deployment of learning technologies, sustainability, adequacy, inclusiveness, equality, efficacy, scalability…
- Digital competences.
- Chair, Jesus G. Boticario, UNED, Spain, Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.
- José Luis Aznarte-Mellado, UNED, Spain, Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.
- Llanos Tobarra Abad, UNED, Spain, Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.
- Miguel Santamaría Lancho, UNED, Spain, Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.
- Ángeles Sánchez-Elvira Paniagua, UNED, Spain, Esta dirección de correo electrónico está siendo protegida contra los robots de spam. Necesita tener JavaScript habilitado para poder verlo.
- Antonio Teixeira, Universidade Aberta, Portugal
- Timothy Read, UNED, Spain
- Faraon Llorens, Universidad de Alicante, Spain
- Gurstaf Neuman, Wirtschaftsuniversität Wien, Austria
- Mª Begoña Peña Lang, UPV/EHU, Spain
- Klaus-Dieter Rossade, Open University, United Kingdom
- Tommi Kärkkäinen, University of Jyväskylä, Finland
- Mirka Saarela, University of Jyväskylä, Finland
- Ana Guerrero, UOC, Spain
- George Ubachs, EADTU, The Netherlands
- Lluis Pastor, UOC, Spain
- Emmanuelle Gutiérrez y Restrepo, SIDAR-UNED, Spain
- Joao Sarraipa, Uninova, Portugal
- Ana Serrano-Mamolar, UNED, Spain
- Deadline for complete paper submissions: June 30, 2021 using
https://www.conftool.org/weefgedc2021/ - Notification of paper acceptance: September 10, 2021
- Submissions final camera-ready papers: September 20, 2021
- Online authors and early registration deadline September 20, 2021
- Conference: November 15-18, 2021
