Call for Chapters for New Elsevier Book
INTELLIGENT SYSTEMS AND LEARNING DATA ANALYTICS IN ONLINE EDUCATION
colMOOC Elsevier Book
- 2-5 page chapter proposal deadline: March 15, 2020
- Notification of proposal acceptance/rejection: March 30, 2020
- Full chapters submission deadline: June 15, 2020
- Notification of final acceptance/rejection: July 30, 2020
- Camera-ready submission deadline: September 15, 2020
- Stavros N. Demetriadis, Aristotle University of Thessaloniki, Greece.
- Santi Caballe, Universitat Oberta de Catalunya, Barcelona, Spain.
- Eduardo Gómez-Sánchez, Universidad de Valladolid, Spain.
- Pantelis M. Papadopoulos, Aarhus University, Denmark.
- Armin Weinberger, University of Saarland, Germany.
Online education and especially Massive Open Online Courses (MOOCs) arose as a way of transcending formal higher education by realizing technology-enhanced formats of learning and instruction and by granting access to an audience way beyond students enrolled in any one Higher Education Institution (HEI). However, the potential for European HEIs to scale up and reach an international audience of diverse backgrounds has not been realized yet. MOOCs have been reported as an efficient and important educational tool, yet there is a number of issues and problems related to their educational impact. More specifically, there is an important number of dropouts during a course, little participation, and lack of students’ motivation and engagement overall. This may be due to one-size-fits-all instructional approaches and very limited commitment to student-student and teacher-student collaboration.
Previous studies combine Artificial Intelligence (AI) based approaches, such as the use of conversational agents, chatbots and data analytics in order to face the above challenges. However, these studies explore these and other AI approaches separately, thus having less impact in the learning process. Therefore, the effective integration of AI novel approaches in education in terms of pedagogical Conversational Agents (CA) and Learning Analytics (LA) will create beneficial synergies to relevant learning dimensions, resulting in students’ greater participation and performance while lowering drop-out rates and improving satisfaction and retention levels. In addition, tutors, academic coordinators and managers will be provided with tools that will facilitate the formative and monitoring processes.
Specifically, the book aims to provide novel AI and analytics-based methods to improve online teaching and learning, addressing key problems such as the problem of attrition in MOOCs and online learning in general. To this end, the book pursues to contribute to the educational sector at different levels:
- Deliver new learning and teaching methods for online learning (with a specific focus on MOOCs), building on novel technologies in collaborative learning, such as CA and LA, that are capable of boosting learner interaction and facilitate learners’ self-regulation and –assessment.
- Demonstrate and validate the built capacity for innovative teaching and learning methods and mainstream them to the existing education and training systems, by the design, execution and assessment of pilots that orchestrate individual and collaborative learning activities.
- Promote highly innovative solutions and beyond the state-of-the-art models for Online and MOOC-based learning and implementations with the integration of AI services, such as, for example, based on CA and LA, to face current and future challenges and for sustainable impact on online educational and training systems.
- Demonstrate and exemplify efficient teaching techniques leveraging the power of analyzing data generated by smart AI-based interfaces, such as those promoting interactions with CA in learning environments.
- Deepen our understanding of how CA tools can contribute to increasing the transactional quality of peers’ dialogue and, consequently, the quality of learning, in various situations, such as learning in Academic settings and also corporate training in Business environments.
The ultimate aim of this book is to stimulate research from both theoretical and practical views, including experiences with open source tools, which will allow other educational institutions and organizations to apply, evaluate and reproduce the book’s contributions. Industry and academic researchers, professionals and practitioners will be invited to exchange their experiences and share their ideas in this field.
The book will follow the ongoing research project colMOOC funded by the European Commission devoted to the integration of Conversational Agents and Learning Analytics in MOOCs. Project web site: https://colmooc.eu/
This edited volume will focus on the following topics
- Learning Engineering, Online education, eLearning
- Massive online open courses (MOOCs)
- Computer-supported collaborative learning (CSCL)
- Conversational pedagogical agents
- Learning data analytics
- Agent-based software for education
- Monitoring and analysis of individual and group data interactions
- Knowledge-based technologies for CSCL
- Information visualization techniques for supporting and analyzing group learning data and processes
- Discursive data analysis for CSCL
- Assessment data analytics for MOOCs
- Feedback generation, support and automation for MOOCs
- Methodologies for the design of MOOC activities, resources and tools
- Best practices, user experiences and case studies of MOOCs
- Self, peer and collaborative learning in MOOCs
- Human-Computer interaction in MOOCs
Submission Procedure & Guidelines
Researchers and practitioners are invited to submit on or before March 15, 2020, a 2-5 page manuscript proposal (Word or PDF) explaining the mission and concerns of the proposed chapter. All submitted chapter proposals will be reviewed by the editors of the book.
The edition of this book is partly funded by the European Commission through the project “colMOOC: Integrating Conversational Agents and Learning Analytics in MOOCs” (588438-EPP-1-2017-1-EL-EPPKA2-KA). Project web site: https://colmooc.eu/
Inquiries and submissions can be forwarded by e-mail to:
Santi Caballe (firstname.lastname@example.org)
Stavros Demetriadis (email@example.com)