Abstract In recent years, we have seen a significant proliferation of e-learning platforms. E-learning platforms allow teachers to create digital courses in a more effective and time-saving way, but several flaws hinder their actual success. One main problem is that teachers have difficulties finding and combining open-access learning materials that match their specific needs precisely when there are so many to choose from. This paper proposes a new strategy for creating digital courses that use learning objects (LOs) as primary elements. The idea consists of using an intelligent chatbot to assist teachers in their activities. Defined using RASA technology, the chatbot asks for information about the course the teacher has to create based on her/his profile and needs. It suggests the best LOs and how to combine them according to their prerequisites and outcomes. A chatbot-based recommendation system provides suggestions through BERT, a machine-learning model based on Transformers, to define the semantic similarity between the entered data and the LOs metadata. In addition, the chatbot also suggests how to combine the LOs into a final learning path. Finally, the paper presents some preliminary results about tests carried out by teachers in creating their digital courses.

The use of learning objects (LOs) to create digital courses has been widely advocated by learning strategists and by teachers engaged in the e-learning domain. The ability to combine chunks of learning material as to meet complex educational requirements is still a challenge. This paper explores the idea that a learning assistant advises teachers about the e-learning modules to take into account for their courses. An AI-based digital assistant can provide significant opportunities, but might be perceived as a threat. The paper presents how teacher could perceive a virtual assistant as more trustworthy when it applies interactive visual strategies. To analyze teachers’ acceptance of the digital assistant, our proposal aims at extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model in order to incorporate three new constructors: Communicability, perceived trust and experience. To this end, 14 teachers have been involved in a user tests.

Recent studies in the context of machine learning have shown the effectiveness of deep attentional mechanisms for identifying important communities and relationships within a given input network. These studies can be effectively applied in those contexts where capturing specific dependencies, while downloading useless content, is essential to take decisions and provide accurate inference. This is the case, for example, of current recommender systems that exploit social information as a clever source of recommendations and / or explanations. In this paper we extend the social engine of our educational platform “WhoTeach” to leverage social information for educational services. In particular, we report our work in progress for providing “WhoTeach” with an attentional-based recommander system oriented to the design of programmes and courses for new teachers.

Recommender Systems have became extremely appealing for all technology enhanced learning researches aimed to design, develop and test technical innovations which support and enhance learning and teaching practices of both individuals and organizations. In this scenario a new emerging paradigm of explainable Recommander Systems leverages social friend information to provide (social) explanations in order to supply users with his/her friends’ public interests as explained recommendation. In this paper we introduce our educational platform called “WhoTeach”, an innovative and original system to integrate knowledge discovery, social networks analysis, and educational services. In particular, we report here our work in progress for providing “WhoTeach” environment with optimized Social Explainable Recommandations oriented to design new teachers’ programmes and courses.

With the ambition of providing teachers with a concrete tool for worldwide exploiting didactic contents to feature their courses, we face the problem of creating a social platform with adequate functionalities to satisfy the teacher expectations. Starting with a well designed architecture we endow it with three key functionalities that become the stakeholders of the emerging social network: 1) a quality system ensuring the value of the materials the users put in the platform repository as their contribution to the social business, 2) a recommender system based on computational intelligence techniques constituting the principal tool to guide teachers along the assembling of materials into courses, and 3) a gamification system, root of the no-profit business plan of the platform, to involve teachers in the social network. As a result we delineate an ecosystem where teachers exploit contents of a repository to which contribute by themselves. They are encouraged in exploiting and contributing because the contents are of high quality; they are wisely assisted in the exploration of the repository by platform services yet under their full control; and they are variously reworded by this involvement.

In this paper we introduce a platform tailored to give teachers and trainers appropriate knowledge, skills, and innovative tools in the domain of the entrepreneurial education. To this end, a social network is created where teachers can formally or informally share experiences supporting their peers with technical training, along with theory and practical examples deriving from mutual and practical experiences in entrepreneurship. In this perspective, the platform through a systematic and intelligent use of metadata is able to offer an innovative social network specially tailored for teachers in order to valorize their competencies and to fit their expectations.

This paper discusses a statistical analysis emerging in the field entrepreneurship education, as a result of a survey conducted in the frame of the European Project NETT (Networked Entrepreneurship Training of Teachers, http://nett-project.eu). The analysis concerns both the quality of data and the emergence of some special patterns denoting some interesting features of the entrepreneurship perception and its teaching. In the authors’ intention this note should constitute a simple, yet concrete, tool to foster a scientific discussion on a discipline that is deeply rooted on knowledge management and represents a strategic lane in the improvement of modern societies.

A Recommender System (RS) filters a large amount of information to identify the items that are likely to be more interesting and attractive to a user. Recommendations are inferred on the basis of different user profile characteristics, in most cases including explicit ratings on a sample of suggested elements. RS research highlights that profile length, i. e., the number of collected ratings, is positively correlated to the accuracy of recommendations, which is considered an important quality factor for RSs. Still, gathering ratings adds a burden on the user, which may negatively affect the UX. A design tension seems to exist, induced by two conflicting requirements -- to raise accuracy by increasing the profile length, and to make the profiling process smooth for the user by limiting the number of ratings. The paper presents a wide empirical study (1080 users involved) which explores this issue. Our work attempts to identify which of the two contrasting forces influenced by profile length -- recommendations accuracy and burden of the rating process - has stronger effects on the perceived quality of the UX with a RS.

The growth of the social web poses new challenges and opportunities for recommender systems. The goal of Recommender Systems (RSs) is to filter information from a large data set and to recommend to users only the items that are most likely to interest and/or appeal to them. The quality of a RS is typically defined in terms of different attributes, the principal ones being relevance, novelty, serendipity and global satisfaction. Most existing works evaluate quality of Recommender Systems in terms of statistical factors that are algorithmically measured. This paper aims to explore whether (i) algorithmic measures of RS quality are in accordance with user-based measure and (ii) the user perceived quality of a RS is affected by the number of movies rated by the user. For this purpose we designed, developed and tested a web recommender system, TheBestMovie4You (http://www.moviers.it), which allows us to collect questionnaires about the quality of recommendations. We made a questionnaire and gave it to 240 subjects and we wanted to have as wide a set of users as possible using social web. In a experiment we asked the users to choose five movies (short profile), in a second to choose ten (long profile). Our results show that statistical properties fail in fully describing the quality of algorithms, because with user-centered metrics we can outline an algorithm's features that otherwise could not be detected. The comparison between the two phases highlighted a difference only in three cases out of twenty.