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

Toward a user-adapted question/answering educational approach

This paper addresses the design of a model for Question/Answering in an interactive and mobile learning environment. The learner's question can be made through vocal interaction or typed text and the answer is the generation of a personalized learning path. This takes into account the focus and type of the question and some personal features of the learner extracted both from the question and prosodic features, in case of vocal questions. The response is a learning path that preserves the precedence of the prerequisite relations and contains all the relevant concepts for answering the user's question. The main contribution of the paper is to investigate the possibility to exploit educational concept maps in a Q/A interactive learning system

WIKI COURSE BUILDER, A SYSTEM FOR MANAGING AND SHARING DIDACTIC MATERIAL AND CONCEPT MAPS

In this article we present an evolution of Wiki course Builder, a system for building courses, sharing and sequencing learning material taken from Wikipedia pages. The system has been expanded through the implementation of two new modules to provide teachers with different tools that optimize the course creation process. Taking advantage of the user model implemented in the system starting from Grasha\u2019s teaching styles, we have implemented a module that visualizes the graph of all the courses created by the teaching community and makes it possible to compare it with others. This graph can be filtered by macro categories of arguments (eg. History, Philosophy...) and by teacher archetypes (eg. expertize delegator ...). The second module is a graphical interface that makes it possible to design and build concept maps for the generation of different courses (alternative learning paths through the map). The comparison between these maps will enrich the model of the teacher who will receive recommendations more refined on the basis of the course method you prefer to make. For the future it will be interesting to extend the user model through the comparison of the concept maps generated by the creation of the course and the clustering of the teachers on the basis of this data. Furthermore, the study of the density of concepts within the materials and the complexity of learning difficulties would complete the user model by optimizing again the recommendation process

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

A Knowledge-Based Model for Instructional Design

This thesis will discuss a knowledge-based model for the design and development of units of learning and teaching aids. The idea behind this work originates from previous theoretical work on ECM - Educational Concept Map (a logical and abstract annotation system, derived from the theories of instructional design), from the open issues in designing instructional authoring system, and from the lack of a well-defined process able to merge pedagogical strategies with systems for the knowledge organization of the domain

Designing a Knowledge Representation Tool for Subject Matter Structuring

Relying on pedagogical theories of subject matter structuring and presentation, this paper focuses on the design of a knowledge representation tool for the scheming and organization of educational materials. The idea originates from the Educational Concept Maps model - logical and abstract annotation system, developed with the aim of guaranteeing the reusability of both teaching materials and knowledge structures; in fact, the knowledge structure could be reused for design of different courses according to the learner target. A sequence of concepts characterizing the subject matter under design (lesson or entire course) define a teaching/learning path through the map. It represents the output of the design process of lesson plan, which could be imported in a text-editor, in a LCMS, or presented as web pages. The final goal is to develop a tool assisting the teacher in the daily design of lesson plans via a pliable structured model of domain knowledge

Editoriale _ DIDAMATICA 2018

Promosso annualmente da AICA, DIDAMATICA (DIDAttica e inforMATICA \u2013 Informatica per la Didattica) da oltre trent\u2019anni \ue8 punto di riferimento per studenti, docenti, istituzioni scolastiche, professionisti ICT, aziende e Pubblica Amministrazione sui temi dell\u2019innovazione digitale per la filiera della formazione

Fitness Trackers and Wearable Devices: How to Prevent Inference Risks?

Wearable and personal devices are becoming more and more part of people's everyday lives. These devices produce enormous amount of personal data which are handled by third parties as authorized by the user. However, such third parties may be able to infer sensitive information based on the collected personal information. In this paper we present a case study based on fitness trackers and we sketch our model for privacy management and inference prevention. For this study, we built a Bayesian Network and used it to compute the risk of inferring unknown data. Specifically, we simulate a case where we show the feasibility of inferring some private data from a set of personal data available to a third party as authorized by the user (i.e., sensor data and profiling data provided by the user while registering for the service). This paper provides a step towards the open issues of privacy and security management in the field of ubiquitous devices