Preface to the Special Issue AI4MOOCs: Artificial Intelligence, Sensoring, Modeling and Assessment for MOOCs. A Step Beyond

• Published in: International journal of artificial intelligence in education Vol. 31; no. 2; pp. 157 - 158
• Main Authors: Sciarrone, Filippo, Limongelli, Carla, Santos, Olga C., Temperini, Marco
• Format: Journal Article
• Language: English
• Published: New York Springer New York 01-06-2021; Springer Nature B.V
• Subjects: AI4MOOCs: Artificial Intelligence; Artificial Intelligence; Computer Science; Computers and Education; Data science; Deep learning; Distance learning; Editorial; Educational Environment; Educational Technology; Learning Processes; Machine learning; modeling and assessment for MOOCs. A step beyond; MOOCs; Natural language processing; Online instruction; Prediction models; Recurrent neural networks; Science Curriculum; sensoring; Teachers; Teaching Methods; User Interfaces and Human Computer Interaction
• ISSN: 1560-4292; 1560-4306
• DOI: 10.1007/s40593-021-00255-1

In particular, this study examined the use of state-of-the-art deep learning models such as recurrent neural networks and generative pretrained transformer 2 (GPT-2). The third and last paper, “Towards Designing Profitable Courses: Predicting Student Purchasing Behaviour in MOOCs”, by Mohammad Alshehri, Ahmed Alamri, Alexandra Cristea and Craig D. Stewart, presents a work that, starting from data generated by the various student activities in a MOOC’s learning environment, builds a predictive model for student success. [...]they are a starting point for new discussions concerning MOOCs in a context where teachers, due to the large numbers of students, would have difficulty in effectively monitoring each learner individually.