Ricerc@Sapienza

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.

Peer assessment has increasingly proven its benefits for the learning process and several educational platforms have been proposed to support it. Rather than developing yet another standalone tool, in this paper we aim to integrate an existing Bayesian Network-based peer evaluation approach in a widely used learning management system, Moodle. This allows to capitalize both on the successful peer assessment model and on the broad range of educational functionalities provided by the learning management system.

Thanks to the exponential growth of the Internet, Distance Education is becoming more and more strategic in many fields of daily life. Its main advantage is that students can learn through appropriate web platforms that allow them to take advantage of multimedia and interactive teaching materials, without constraints neither of time nor of space. Today, in fact, the Internet offers many platforms suitable for this purpose, such as Moodle, ATutor and others. Coursera is another example of a platform that offers different courses to thousands of enrolled students.

In the last years, the design and implementation of web-based education systems has grown exponentially, spurred by the fact that neither students nor teachers are bound to a specific location and that this form of computer-based education is virtually independent of any specific hardware platform. These systems accumulate a large amount of data: educational data mining and learning analytics are the two much related fields of research with the aim of using these educational data to improve the learning process.

In the OpenAnswer system it is possible to compute grades for/to the answers to open-ended questions given to a class of students, based on the students’ peer-evaluation and on the teacher’s grading work, performed on a subset of the answers. Here we analyze the systems’ performances, expressed as the capability to infer correct grades based on a limited amount of grading work by the teacher.

Peer-assessment entails, for students, a very beneficial learning activity, from a pedagogical point of view. The peer-evaluation can be performed over a variety of peer-produced resources, the principle being that the more articulated such resource is, the better. Here we focus, in particular, on the automated support to grading open answers, via a peer-evaluation-based approach, which is mediated by the (partial) grading work of the teacher, and produces a (partial, as well) automated grading. We propose to support such automated grading by means of a method based on the K-NN technique.

The paper investigates if and how the topology of the peer assessment network can affect the performance of the Bayesian model adopted in Ope