PRET: Prerequisite-enriched terminology. A case study on educational texts

In this paper we present PRET, a gold dataset annotated for prerequisite relations between educational concepts extracted from a computer science textbook, and we describe the language and domain independent approach for the creation of the resource. Additionally, we have created an annotation tool to support, validate and analyze the annotation

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

Influence of cookies composition on temperature profiles and qualitative parameters during baking

During baking of bakery products temperature of baking, temperature profiles, moisture content, volume and colour changes are strongly coupled. The objective of this paper was to study the influence of the cookies composition on temperature profiles and quality parameters (width and thickness, colour formation and textural properties: hardness, fracturability and work of breaking force) during baking process. Composition of cookies differs due to flour type and initial moisture content. Cookies were baked at 205 °C and temperature was measured in the centre of samples which were 7 mm thick with a 60 mm diameter. The results of temperature profiles of the cookies during baking have shown the same trend for all of the 18 samples. Samples with the higher initial water content have lower values of total colour difference and also significantly affect textural properties

Aggregazione di Risorse e loro Annotazione Ontologica e Sociale: proposta di un modello e suoi usi

In questo lavoro viene presentato e discusso un modello per aggregare risorse di qualunque tipo e classificarle secondo una ontologia definita dall\u2019utente. Tale modello, ispirato al progetto LearnWeb2.0 sviluppato nell\u2019ambito per progetto UE TENCompetence, permette di gestire risorse locali oppure risorse di rete di qualunque tipo al fine di creare un proprio portfolio da condividere eventualmente su web con altri utenti che possono esprimere giudizi sulle risorse classificate ed eventualmente aggiungere propri tag ed esprimere giudizi. Tale modello \ue8 stato implementato nel sistema \u201cAggregatore di Risorse su base Ontologica\u201d, che permette una doppia funzionalit\ue0: 1) poter operare offline sul proprio computer, fungendo da aggregatore ontologico personale, oppure 2) poter operare come servizio su web, condividendo socialmente le risorse aggregate

Digital Storytelling in a Museum Application Using the Web of Things

The traditional notion of museum has changed. Museums\u2019 mute character is transformed into storytellers that communicate experience to their audience. Innovative technologies enhance the interactivity of storytelling and enable the audience to experience learning and entertainment. In this paper, we present WoTEdu, an interactive storytelling project based on the Web of Things (WoT) paradigm. WoTEdu enhances the audience edutainment experience through interaction with museum artefacts

PRELEARN @ EVALITA 2020: Overview of the prerequisite relation learning task for Italian

The Prerequisite Relation Learning (PRELEARN) task is the EVALITA 2020 shared task on concept prerequisite learning, which consists of classifying prerequisite relations between pairs of concepts distinguishing between prerequisite pairs and non-prerequisite pairs. Four sub-tasks were defined: two of them define different types of features that participants are allowed to use when training their model, while the other two define the classification scenarios where the proposed models would be tested. In total, 14 runs were submitted by 3 teams comprising 9 total individual participants

Digging into prerequisite annotation

Intelligent textbooks are often engineered with an explicit representation of their concepts and prerequisite relations (PR). PR identification is hence crucial for intelligent textbooks but still presents some challenges, also when performed by human experts. This may cause PR-annotated datasets to be inconsistent and compromise the accuracy of automatic creation of enhanced learning materials. This paper investigates possible reasons for PR disagreement and the nature of PR itself, with the aim of contributing to the development of shared strategies for PR annotation, analysis and modelling in textbooks

Towards the identification of propaedeutic relations in textbooks

As well-known, structuring knowledge and digital content has a tremendous potential to enhance meaningful learning. A straightforward approach is representing key concepts of the subject matter and organizing them in a knowledge structure by means of semantic relations. This results in hypergraphs with typed n-ary relationships, including the so-called prerequisite or propaedeutic relations among concepts. While extracting the whole concept graph from a textbook is our final goal, the focus of this paper is the identification of the propaedeutic relations among concepts. To this aim, we employ a method based on burst analysis and co-occurrence which recognizes, by means of temporal reasoning, prerequisite relations among concepts that share intense periods in the text. The experimental evaluation shows promising results for the extraction of propaedeutic relations without the support of external knowledge