IJAIED
International Journal of Artificial Intelligence in Education
SPECIAL ISSUE
AI4MOOCs: Artificial Intelligence, Sensoring, Modeling and Assessment for MOOCs
A Step Beyond
(Deadline Extended)
(Deadline Extended)
The demand for Distance Education has been dramatically growing in recent years, also as a consequence of the huge and increasing availability of systems supporting e-learning through the internet. People, geographically and culturally spread across the globe, companies, practitioners, students, and Communities of Practice with thousands of learners, are involved in networked learning programs. Thanks to the Internet, the 21st century seems to be the Century of lifelong learning.
Massive Open Online Courses (MOOCs), are courses characterized by having a very high number (in the thousands, or more) of students. These courses, mostly free, are offered through special web-based platforms, with the provision of video-based teaching materials, interactive assessment tools, and some social interaction or collaboration means. While, on the one hand, these courses help thousands of students, on the other hand they introduce a strong problem for tutors, who can have a hard life at monitoring the learning process of such an extended class of students. This tutoring support presents challenges that relate to cognitive, affective and even psychomotor aspects (in this last case, towards supporting embodied learning in massive online learning contexts).
Artificial Intelligence in general and Machine Learning in particular propose techniques and tools to study, model, and manage such a complex reality.
We encourage in particular the submission of articles where AI techniques and methods are used to provide automated support to the cognitive, affective and even when possible, psychomotor modeling of students and to the assessing of competences in a MOOC scenario that can be enriched with traditional interaction devices and/or emerging sensors, which are available in traditional computerized learning scenarios (such as webcam, keyboard and mouse), in mobile learning context (which in addition to mobile cameras can also collect information from the inertial and physiological sensors available in smart phones) and even in smarter learning scenarios which can also make use of other sensors such as smart bracelets or virtual reality head mounted displays. Such support would be directed to teachers, in order to allow monitoring the learning process; to students, such as in the case of course adaptation, or for the support to individual self-reflection, about own performances, and decision-taking about what learning experience to select; to course managers, or teachers again, to appreciate the MOOC’s inner dynamics, and wisely guide them.
Topics of interest include but are not strictly limited to:
First round (mandatory): extended abstract submission (easychair.org)
Second round: full paper submission (IJAIED editorial manager)
Filippo Sciarrone (lead guest editor) is a fellow researcher since 1994, at the Roma Tre University, where he has been collaborating in many research activities of the Artificial Intelligence research group and received his Ph.D. with a dissertation on user modeling. Since several years he has also been collaborating with Sapienza University in Rome, giving his main contribute in the application of Machine Learning techniques to educational research projects. He has led several research laboratories of private companies for the production of algorithms and innovative systems for human resource management and for teaching-oriented recommendation systems. His research interests are in the design of hybrid architectures, machine learning and systems to support learning and teaching.
Carla Limongelli is a tenured Associate Professor of Engineering in Computer Science at ROMA TRE University. Her research focuses on Intelligent Adaptive Learning Environments, User Modeling and User-Adapted Interaction, collaborative learning environments, and intelligent and adaptive retrieval of didactic materials from the Internet. She has been developing social-based approach for retrieving and sequencing didactic materials from the web and from Learning Objects Repositories. Currently, she leads the AI research group in Education, at Roma Tre University, Engineering Department.
Olga C. Santos current research focuses on combining Artificial Intelligence with Ambient Intelligence and Internet of Things to support personalized affective psychomotor learning that ubiquitously and dynamically adapts to the evolving user needs. She has participated in 16 research projects (UE, National), published over 150 papers and co-chaired several workshop series (TUMAS-A, RecSysTEL/EdRecSys, PALE, RSyL) and conferences (AIED, EDM, UMAP, EC-TEL). She received the Best Doctoral Thesis Award by the IEEE Spanish Chapter of the Education Society and the 2014 Young Researcher Award of the IEEE Technical Committee on Learning Technology. She has been involved in the AIED community since 2003, with diverse contributions both at AIED conference and IJAIED.
Marco Temperini is a tenured Associate Professor of Engineering in Computer Science, with the Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Italy. His research activity is focused on Technology Enhanced Learning, and on the design and implementation of computer and network based systems for personalized and adaptive learning, social collaborative learning, game based learning, automated evaluation of programming tasks, peer assessment, and on the pedagogical aspects of such systems. Since 1999 he is involved in international (EU funded) research projects, as National Unit leader, and workshop/Intellectual Output leader.