From Texts to Prerequisites. Identifying and Annotating Propaedeutic Relations in Educational Textual Resources

openPrerequisite Relations (PRs) are dependency relations established between two distinct concepts expressing which piece(s) of information a student has to learn first in order to understand a certain target concept. Such relations are one of the most fundamental in Education, playing a crucial role not only for what concerns new knowledge acquisition, but also in the novel applications of Artificial Intelligence to distant and e-learning. Indeed, resources annotated with such information could be used to develop automatic systems able to acquire and organize the knowledge embodied in educational resources, possibly fostering educational applications personalized, e.g., on students' needs and prior knowledge. The present thesis discusses the issues and challenges of identifying PRs in educational textual materials with the purpose of building a shared understanding of the relation among the research community. To this aim, we present a methodology for dealing with prerequisite relations as established in educational textual resources which aims at providing a systematic approach for uncovering PRs in textual materials, both when manually annotating and automatically extracting the PRs. The fundamental principles of our methodology guided the development of a novel framework for PR identification which comprises three components, each tackling a different task: (i) an annotation protocol (PREAP), reporting the set of guidelines and recommendations for building PR-annotated resources; (ii) an annotation tool (PRET), supporting the creation of manually annotated datasets reflecting the principles of PREAP; (iii) an automatic PR learning method based on machine learning (PREL). The main novelty of our methodology and framework lies in the fact that we propose to uncover PRs from textual resources relying solely on the content of the instructional material: differently from other works, rather than creating de-contextualised PRs, we acknowledge the presence of a PR between two concepts only if emerging from the way they are presented in the text. By doing so, we anchor relations to the text while modelling the knowledge structure entailed in the resource. As an original contribution of this work, we explore whether linguistic complexity of the text influences the task of manual identification of PRs. To this aim, we investigate the interplay between text and content in educational texts through a crowd-sourcing experiment on concept sequencing. Our methodology values the content of educational materials as it incorporates the evidence acquired from such investigation which suggests that PR recognition is highly influenced by the way in which concepts are introduced in the resource and by the complexity of the texts. The thesis reports a case study dealing with every component of the PR framework which produced a novel manually-labelled PR-annotated dataset.openXXXIII CICLO - DIGITAL HUMANITIES. TECNOLOGIE DIGITALI, ARTI, LINGUE, CULTURE E COMUNICAZIONE - Lingue, culture e tecnologie digitaliAlzetta, Chiar

Advancing the reform agenda: selected speeches

In the decade and a half since the Productivity Commission was formally established, it has completed 110 inquiries and other commissioned studies and made some 1500 policy recommendations to governments. All of these recommendations were made because the Commission judged that their implementation would enhance Australians’ living standards and quality of life.   In many cases, they would do so by raising the capacity of Australia’s economy to produce valued goods and services — in other words, by raising its ‘productivity’. On a rough reckoning, around two-thirds of the Commission’s recommendations over the years have been accepted and (more or less) implemented by governments. That is not a bad strike rate, given that our reports typically deal with complex and politically contentious areas of public policy, where benefits to the majority can necessitate withdrawing advantages from (vocal) minorities. It nevertheless leaves a sizeable residual, to which Reserve Bank Governor Glenn Stevens was no doubt alluding in his much-reported remarks. Many in the media took him literally though, and were disappointed that the Commission did not in fact have a ‘list’ at the ready

Personalizable Knowledge Integration

Large repositories of data are used daily as knowledge bases (KBs) feeding computer systems that support decision making processes, such as in medical or financial applications. Unfortunately, the larger a KB is, the harder it is to ensure its consistency and completeness. The problem of handling KBs of this kind has been studied in the AI and databases communities, but most approaches focus on computing answers locally to the KB, assuming there is some single, epistemically correct solution. It is important to recognize that for some applications, as part of the decision making process, users consider far more knowledge than that which is contained in the knowledge base, and that sometimes inconsistent data may help in directing reasoning; for instance, inconsistency in taxpayer records can serve as evidence of a possible fraud. Thus, the handling of this type of data needs to be context-sensitive, creating a synergy with the user in order to build useful, flexible data management systems. Inconsistent and incomplete information is ubiquitous and presents a substantial problem when trying to reason about the data: how can we derive an adequate model of the world, from the point of view of a given user, from a KB that may be inconsistent or incomplete? In this thesis we argue that in many cases users need to bring their application-specific knowledge to bear in order to inform the data management process. Therefore, we provide different approaches to handle, in a personalized fashion, some of the most common issues that arise in knowledge management. Specifically, we focus on (1) inconsistency management in relational databases, general knowledge bases, and a special kind of knowledge base designed for news reports; (2) management of incomplete information in the form of different types of null values; and (3) answering queries in the presence of uncertain schema matchings. We allow users to define policies to manage both inconsistent and incomplete information in their application in a way that takes both the user's knowledge of his problem, and his attitude to error/risk, into account. Using the frameworks and tools proposed here, users can specify when and how they want to manage/solve the issues that arise due to inconsistency and incompleteness in their data, in the way that best suits their needs