Semantic annotation of multilingual learning objects based on a domain ontology

One of the important tasks in the use of learning resources in e-learning is the necessity to annotate learning objects with appropriate metadata. However, annotating resources by hand is time consuming and difficult. Here we explore the problem of automatic extraction of metadata for description of learning resources. First, theoretical constraints for gathering certain types of metadata important for e-learning systems are discussed. Our approach to annotation is then outlined. This is based on a domain ontology, which allows us to annotate learning resources in a language independent way.We are motivated by the fact that the leading providers of learning content in various domains are often spread across countries speaking different languages. As a result, cross-language annotation can facilitate accessibility, sharing and reuse of learning resources

Data mining technology for the evaluation of learning content interaction

Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results

A framework for structuring prerequisite relations between concepts in educational textbooks

In our age we are experiencing an increasing availability of digital educational resources and self-regulated learning. In this scenario, the development of automatic strategies for organizing the knowledge embodied in educational resources has a tremendous potential for building personalized learning paths and applications such as intelligent textbooks and recommender systems of learning materials. To this aim, a straightforward approach consists in enriching the educational materials with a concept graph, i.a. a knowledge structure where key concepts of the subject matter are represented as nodes and prerequisite dependencies among such concepts are also explicitly represented. This thesis focuses therefore on prerequisite relations in textbooks and it has two main research goals. The first goal is to define a methodology for systematically annotating prerequisite relations in textbooks, which is functional for analysing the prerequisite phenomenon and for evaluating and training automatic methods of extraction. The second goal concerns the automatic extraction of prerequisite relations from textbooks. These two research goals will guide towards the design of PRET, i.e. a comprehensive framework for supporting researchers involved in this research issue. The framework described in the present thesis allows indeed researchers to conduct the following tasks: 1) manual annotation of educational texts, in order to create datasets to be used for machine learning algorithms or for evaluation as gold standards; 2) annotation analysis, for investigating inter-annotator agreement, graph metrics and in-context linguistic features; 3) data visualization, for visually exploring datasets and gaining insights of the problem that may lead to improve algorithms; 4) automatic extraction of prerequisite relations. As for the automatic extraction, we developed a method that is based on burst analysis of concepts in the textbook and we used the gold dataset with PR annotation for its evaluation, comparing the method with other metrics for PR extraction

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

Annotation Protocol for Textbook Enrichment with Prerequisite Knowledge Graph

Extracting and formally representing the knowledge embedded in textbooks, such as the concepts explained and the relations between them, can support the provision of advanced knowledge-based services for learning environments and digital libraries. In this paper, we consider a specific type of relation in textbooks referred to as prerequisite relations (PR). PRs represent precedence relations between concepts aimed to provide the reader with the knowledge needed to understand a further concept(s). Their annotation in educational texts produces datasets that can be represented as a graph of concepts connected by PRs. However, building good-quality and reliable datasets of PRs from a textbook is still an open issue, not just for automated annotation methods but even for manual annotation. In turn, the lack of good-quality datasets and well-defined criteria to identify PRs affect the development and validation of automated methods for prerequisite identification. As a contribution to this issue, in this paper, we propose PREAP, a protocol for the annotation of prerequisite relations in textbooks aimed at obtaining reliable annotated data that can be shared, compared, and reused in the research community. PREAP defines a novel textbook-driven annotation method aimed to capture the structure of prerequisites underlying the text. The protocol has been evaluated against baseline methods for manual and automatic annotation. The findings show that PREAP enables the creation of prerequisite knowledge graphs that have higher inter-annotator agreement, accuracy, and alignment with text than the baseline methods. This suggests that the protocol is able to accurately capture the PRs expressed in the text. Furthermore, the findings show that the time required to complete the annotation using PREAP are significantly shorter than with the other manual baseline methods. The paper includes also guidelines for using PREAP in three annotation scenarios, experimentally tested. We also provide example datasets and a user interface that we developed to support prerequisite annotation

Video Augmentation in Education: in-context support for learners through prerequisite graphs

The field of education is experiencing a massive digitisation process that has been ongoing for the past decade. The role played by distance learning and Video-Based Learning, which is even more reinforced by the pandemic crisis, has become an established reality. However, the typical features of video consumption, such as sequential viewing and viewing time proportional to duration, often lead to sub-optimal conditions for the use of video lessons in the process of acquisition, retrieval and consolidation of learning contents. Video augmentation can prove to be an effective support to learners, allowing a more flexible exploration of contents, a better understanding of concepts and relationships between concepts and an optimization of time required for video consumption at different stages of the learning process. This thesis focuses therefore on the study of methods for: 1) enhancing video capabilities through video augmentation features; 2) extracting concept and relationships from video materials; 3) developing intelligent user interfaces based on the knowledge extracted. The main research goal is to understand to what extent video augmentation can improve the learning experience. This research goal inspired the design of EDURELL Framework, within which two applications were developed to enable the testing of augmented methods and their provision. The novelty of this work lies in using the knowledge within the video, without exploiting external materials, to exploit its educational potential. The enhancement of the user interface takes place through various support features among which in particular a map that progressively highlights the prerequisite relationships between the concepts as they are explained, i.e., following the advancement of the video. The proposed approach has been designed following a user-centered iterative approach and the results in terms of effect and impact on video comprehension and learning experience make a contribution to the research in this field

Visualisation analysis for exploring prerequisite relations in textbooks

Building automatic strategies for organising knowledge contained in textbooks has a tremendous potential to enhance meaningful learning. Automatic identification of prerequisite relation (PR) between concepts in a textbook is a well-known way for knowledge structuring, yet it is still an open issue. Our research contributes for better understanding and exploring the phenomenon of PR in textbooks, by providing a collection of visualisation techniques for PR exploration and analysis, that we used for the design of and then the refinement of our algorithm for PR extraction

Neural Graph Transfer Learning in Natural Language Processing Tasks

Natural language is essential in our daily lives as we rely on languages to communicate and exchange information. A fundamental goal for natural language processing (NLP) is to let the machine understand natural language to help or replace human experts to mine knowledge and complete tasks. Many NLP tasks deal with sequential data. For example, a sentence is considered as a sequence of works. Very recently, deep learning-based language models (i.e.,BERT \citep{devlin2018bert}) achieved significant improvement in many existing tasks, including text classification and natural language inference. However, not all tasks can be formulated using sequence models. Specifically, graph-structured data is also fundamental in NLP, including entity linking, entity classification, relation extraction, abstractive meaning representation, and knowledge graphs \citep{santoro2017simple,hamilton2017representation,kipf2016semi}. In this scenario, BERT-based pretrained models may not be suitable. Graph Convolutional Neural Network (GCN) \citep{kipf2016semi} is a deep neural network model designed for graphs. It has shown great potential in text classification, link prediction, question answering and so on. This dissertation presents novel graph models for NLP tasks, including text classification, prerequisite chain learning, and coreference resolution. We focus on different perspectives of graph convolutional network modeling: for text classification, a novel graph construction method is proposed which allows interpretability for the prediction; for prerequisite chain learning, we propose multiple aggregation functions that utilize neighbors for better information exchange; for coreference resolution, we study how graph pretraining can help when labeled data is limited. Moreover, an important branch is to apply pretrained language models for the mentioned tasks. So, this dissertation also focuses on the transfer learning method that generalizes pretrained models to other domains, including medical, cross-lingual, and web data. Finally, we propose a new task called unsupervised cross-domain prerequisite chain learning, and study novel graph-based methods to transfer knowledge over graphs