About this Research Topic
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
The contributions presented at the IMHE2020 workshop are particularly welcome in this Research Topic. Additionally, other contributions fitting in the scope of the Topic as outlined above are also encouraged.
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
The contributions presented at the IMHE2020 workshop are particularly welcome in this Research Topic. Additionally, other contributions fitting in the scope of the Topic as outlined above are also encouraged.
Keywords: mentoring process, mentoring, chatbots, mentoring support, deep learning, mixed reality, mentoring analytics, intelligent systems
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
