Grounding human vocabulary in robot perception through interaction

This thesis addresses the problem of word learning in computational agents. The motivation behind this work lies in the need to support language-based communication between service robots and their human users, as well as grounded reasoning using symbols relevant for the assigned tasks. The research focuses on the problem of grounding human vocabulary in robotic agent’s sensori-motor perception. Words have to be grounded in bodily experiences, which emphasizes the role of appropriate embodiments. On the other hand, language is a cultural product created and acquired through social interactions. This emphasizes the role of society as a source of linguistic input. Taking these aspects into account, an experimental scenario is set up where a human instructor teaches a robotic agent the names of the objects present in a visually shared environment. The agent grounds the names of these objects in visual perception. Word learning is an open-ended problem. Therefore, the learning architecture of the agent will have to be able to acquire words and categories in an openended manner. In this work, four learning architectures were designed that can be used by robotic agents for long-term and open-ended word and category acquisition. The learning methods used in these architectures are designed for incrementally scaling-up to larger sets of words and categories. A novel experimental evaluation methodology, that takes into account the openended nature of word learning, is proposed and applied. This methodology is based on the realization that a robot’s vocabulary will be limited by its discriminatory capacity which, in turn, depends on its sensors and perceptual capabilities. An extensive set of systematic experiments, in multiple experimental settings, was carried out to thoroughly evaluate the described learning approaches. The results indicate that all approaches were able to incrementally acquire new words and categories. Although some of the approaches could not scale-up to larger vocabularies, one approach was shown to learn up to 293 categories, with potential for learning many more.

Evaluation of online higher education: learning, interaction and technology

The work presents a theoretical framework for the evaluation of e-Teaching that aims at positioning the online activities designed and developed by the teacher as to the Learning, Interaction and Technology Dimensions. The theoretical research that underlies the study was developed reflecting current thinking on the promotion of quality of teaching and of the integration of information and communication tools into the curriculum in Higher Education (HE), i.e., bearing in mind some European guidelines and policies on this subject. This way, an answer was sought to be given to one of the aims put forward in this study, namely to contribute towards the development of a conceptual framework to support research on evaluation of e-teaching in the context of HE. Based on the theoretical research carried out, an evaluation tool (SCAI) was designed, which integrates the two questionnaires developed to collect the teachers' and the students' perceptions regarding the development of e-activities. Consequently, an empirical study was structured and carried out, allowing SCAI tool to be tested and validated in real cases. From the comparison of the theoretical framework established and the analysis of the data obtained, we found that the differences in teaching should be valued and seen as assets by HE institutions rather than annihilated in a globalizing perspective.