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Intelligence Support for Mentoring Processes in Higher Education
(and beyond)
Research Topic in Frontiers in Artificial Intelligence (AI for
Human
Learning and Behavior Change)
https://www.frontiersin.org/research-topics/14009/intelligence-support-for-mentoring-processes-in-higher-education-and-beyond
Submission deadline: 30 September 2021
Mentoring is the activity of a senior person (the mentor)
supporting a
less experienced person (the mentee) in learning. It is based on a
trustful, protected and private atmosphere between the mentor and
the
mentee. The goal is to develop a professional identity and to
reflect the
current situation. At universities, mentors are senior academics
or
skilled employees while mentees are mostly students with different
competences. Outside universities, mentors and mentees are
professionals.
Intelligent tutoring systems have a long tradition, focusing on
cognitive
aspects of learning in a selected domain. They were successfully
applied
especially in such areas, where the domain knowledge can be well
formalised with the help of experts. Nevertheless, in the learning
process
also motivations, emotions and meta-cognitive competences play a
crucial
role. These can be nowadays quite well recognised and monitored
through
big educational data and a wide spectrum of available sensors.
This
enables the support also for the mentoring process, which is more
spontaneous, holistic and depends on the needs and interests of
the
mentee. Psychological and emotional support are at the heart of
the
mentoring relationship, underpinned by empathy and trust. Various
roles
and success factors for mentoring have been identified.
We want to look at these aspects and investigate how they were
technologically supported, in order to specify the requirements
for
intelligent mentoring systems. This should help us to answer the
following
questions: How can we design educational concepts that enable a
scalable
individual mentoring in the development of competences? How can we
design
intelligent mentoring systems to cover typical challenges and to
scale up
mentoring support in universities and outside? How can we design
an
infrastructure to exchange data between universities in a private
and
secure way to scale up on the inter-university level? How can we
integrate
heterogeneous data sources (learning management systems, sensors,
social
networking sites) to facilitate learning analytics supporting
mentoring
processes?
Topics include but are not limited to:
• Pedagogical models of mentoring
• Peer mentoring & crowdsourcing mentoring
• Workplace & career mentoring
• Meta-cognitive competences of mentoring
• Chatbots in Mentoring
• Mixed Reality Mentoring
• Wearables and Sensors for mentoring
• Self-regulated mentoring, nudging & behaviour change
• Mentoring analytics
• Mentoring support in learning management systems
• Mobile mentoring support
• Design and research methodologies for mentoring support
• Measuring and Analysing mentoring support
• Visualization techniques for mentoring support
• Motivation and gamification of mentoring support
• Deep learning, machine learning and data mining in mentoring
support
• Recommender technologies for mentoring support (mentor-mentee
matching)
• Semantic technologies for mentoring support (ontologies, domain
&
mentoring models)
• Distributed mentoring environments (cloud & p2p platforms)
• Mentoring for specific domains & subjects (math,
engineering, social
sciences, pedagogy)
• Affective computing for mentoring
• Requirements of intelligent mentoring systems
If you decide to submit a manuscript within our collection, your
contribution will be peer-reviewed and judged on originality,
interest,
clarity, relevance, correctness, language, and presentation (inter
alia)
by our editorial board members. Immediately upon publication, your
paper
will be free to read for everyone, increasing visibility, and
citations.
We encourage authors to submit Abstracts ahead of the full
manuscript
submission.
Topic Editors:
• Ralf Klamma (RWTH Aachen University, Germany)
• Milos Kravcik (German Research Center for Artificial
Intelligence -
DFKI, Germany)
• Elvira Popescu (University of Craiova, Romania)
• Viktoria Pammer-Schindler (Graz University of Technology,
Austria)
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