Ricerc@Sapienza

One standing problem in the area of web-based e-learning is how to support instructional
designers to effectively and efficiently retrieve learning materials, appropriate for their
educational purposes. Learning materials can be retrieved from structured repositories,
such as repositories of Learning Objects and Massive Open Online Courses; they could also
come from unstructured sources, such as web hypertext pages. Platforms for distance education
often implement algorithms for recommending specific educational resources and

Peer Assessment is a powerful tool to enhance students high level meta-cognitive skills. In this paper we deal with a simulation framework (K-OpenAnswer) allowing to support peer assessment sessions, in which peers answer a question and assess some of their peers’ answers, with the enrichment of “teacher mediation”. Teacher mediation consists in the possibility for the teacher to add information into the network of data built by the peer assessment, by grading some answers.

The smartness of a city is given by the technologies it put to use, and more than that, by the people empowered by such technologies; it is worth thinking about how people can be trained to be empowered by smart technologies, and how cities can become “educational.” So, while sustainability and technology solutions for smart cities are strategic challenges, one of these is surely distance education and training. In this field, the Web offers many opportunities, such as the e-learning platforms where students can learn, according to their own needs and pace.

The field of education has never been indifferent to the new technologies, and eventually to the Internet. Technology-Enhanced Learning, progressively, has grown to be the area for research and practice on the application of information and communication technologies to teaching and learning.

The recent diffusion of online education (both MOOCs and e-courses) has led to an increased economic and scientific interest in e-learning environments. As widely documented, online students have a much higher chance of dropping out than those attending conventional classrooms. It is of paramount interest for institutions, students, and faculty members to find more efficient methodologies to mitigate withdrawals. Following the rise of attention on the Student Dropout Prediction (SDP) problem, the literature has witnessed a significant increase in contributions to this subject.

Social isolation during the pandemic contributed to the transition of educational processes to e-learning. A short-term e-marketing education program for a variety of students was introduced in May 2020 and is taught entirely online. A survey was conducted regularly in the last week of training using Google Forms, and three cohorts were surveyed in July, September, and December 2020.

The Random-Weight Fuzzy Neural Network is an inference system where the fuzzy rule parameters of antecedents (i.e., membership functions) are randomly generated and the ones of consequents are estimated using a Regularized Least Squares algorithm. In this regard, we propose an on-line learning algorithm under the hypothesis of training data distributed across a network of interconnected agents. In particular, we assume that each agent in the network receives a stream of data as a sequence of mini-batches.