DeepEva: A deep neural network architecture for assessing sentence complexity in Italian and English languages

Automatic Text Complexity Evaluation (ATE) is a research field that aims at creating new methodologies to make autonomous the process of the text complexity evaluation, that is the study of the text-linguistic features (e.g., lexical, syntactical, morphological) to measure the grade of comprehensibility of a text. ATE can affect positively several different contexts such as Finance, Health, and Education. Moreover, it can support the research on Automatic Text Simplification (ATS), a research area that deals with the study of new methods for transforming a text by changing its lexicon and structure to meet specific reader needs. In this paper, we illustrate an ATE approach named DeepEva, a Deep Learning based system capable of classifying both Italian and English sentences on the basis of their complexity. The system exploits the Treetagger annotation tool, two Long Short Term Memory (LSTM) neural unit layers, and a fully connected one. The last layer outputs the probability of a sentence belonging to the easy or complex class. The experimental results show the effectiveness of the approach for both languages, compared with several baselines such as Support Vector Machine, Gradient Boosting, and Random Forest

Deep learning for knowledge tracing in learning analytics: An overview

Learning Analytics (LA) is a recent research branch that refers to methods for measuring, collecting, analyzing, and reporting learners’ data, in order to better understand and optimize the processes and the environments. Knowledge Tracing (KT) deals with the modeling of the evolution, during the time, of the students’ learning process. Particularly its aim is to predict students’ outcomes in order to avoid failures and to support both students and teachers. Recently, KT has been tackled by exploiting Deep Learning (DL) models and generating a new, ongoing, research line that is known as Deep Knowledge Tracing (DKT). This was made possible by the digitalization process that has simplified the gathering of educational data from many different sources such as online learning platforms, intelligent objects, and mainstream IT-based systems for education. DKT predicts the student’s performances by using the information embedded in the collected data. Moreover, it has been shown to be able to outperform the state-of-the-art models for KT. In this paper, we briefly describe the most promising DL models, by focusing on their prominent contribution in solving the KT task

Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology, and Framework

This paper describes an approach for supporting automatic satire detection through effective deep learning (DL) architecture that has been shown to be useful for addressing sarcasm/irony detection problems. We both trained and tested the system exploiting articles derived from two important satiric blogs, Lercio and IlFattoQuotidiano, and significant Italian newspapers

A Pipeline for the Implementation of Immersive Experience in Cultural Heritage Sites in Sicily

Modern digital technologies allow potentially to explore Cultural Heritage sites in immersive virtual environments. This is surely an advantage for the users that can better experiment and understand a specific site, also before a real visit. This specific approach has gained increasing attention during the extreme conditions of the recent COVID-19 pandemic. In this work, we present the processes that lead to the implementation of an immersive app for different kinds of low and highcost devices, which have been attained in the context of the 3DLab-Sicilia project. 3DLab-Sicilia’s main objective is to sponsor the creation, development, and validation of a sustainable infrastructure that interconnects three main Sicilian centers specialized in augmented and virtual reality. The project gives great importance to the cultural heritage, as well as to the tourism related areas. Despite the presentation of the case study of the Santa Maria La Vetere church, the process of the final app implementation guided by the general pipeline here presented is general and can be applied to other cultural heritage sites

A Case Study for the Design and Implementation of Immersive Experiences in support of Sicilian Cultural Heritage

Virtual Reality (VR) is a robust tool for sponsoring Cultural Heritage sites. It enables immersive experiences in which the user can enjoy the cultural assets virtually, behaving as he/she would do in the real world. Covid-19 pandemic has shed light on the importance of using VR in cultural heritage, showing advantages for the users that can visit the site safely through specific devices. In this work, we present the processes that lead to the creation of an immersive app that makes explorable a famous cultural asset in Sicily, the church of SS. Crocifisso al Calvario. The application creation process will be described in each of its parts, beginning from the digital acquisition of the cultural asset to the development of the user interface. The application is provided for three different VR devices: smartphones equipped with cardboards, headsets, and CAVE. The paper is supported by the 3dLab-Sicilia project, whose main objective is to sponsor the creation, development, and validation of a sustainable infrastructure that interconnects three main Sicilian centres specialized in augmented and virtual reality

Framing automatic grading techniques for open-ended questionnaires responses. A short survey

The assessment of students' performances is one of the essential components of teaching activities, and it poses different challenges to teachers and instructors, especially when considering the grading of responses to open-ended questions (i.e., short-answers or essays). Open-ended tasks allow a more in-depth assessment of students' learning levels, but their evaluation and grading are time-consuming and prone to subjective bias. For these reasons, automatic grading techniques have been studied for a long time, focusing mainly on short-answers rather than long essays. Given the growing popularity of Massive Online Open Courses and the shifting from physical to virtual classrooms environments due to the Covid-19 pandemic, the adoption of questionnaires for evaluating learning performances has rapidly increased. Hence, it is of particular interest to analyze the recent effort of researchers in the development of techniques designed to grade students' responses to open-ended questions. In our work, we consider a systematic literature review focusing on automatic grading of open-ended written assignments. The study encompasses 488 articles published from 1984 to 2021 and aims at understanding the research trends and the techniques to tackle essay automatic grading. Lastly, inferences and recommendations are given for future works in the Learning Analytics field

A sentence based system for measuring syntax complexity using a recurrent deep neural network

In this paper we present a deep neural network model capable of inducing the rules that identify the syntax complexity of an Italian sentence. Our system, beyond the ability of choosing if a sentence needs of simplification, gives a score that represent the confidence of the model during the process of decision making which could be representative of the sentence complexity. Experiments have been carried out on one public corpus created specifically for the problem of text-simplification

Deep neural attention-based model for the evaluation of italian sentences complexity

In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems

Attention-based Model for Evaluating the Complexity of Sentences in English Language

The automation of text complexity evaluation (ATCE) is an emerging problem which has been tackled by means of different methodologies. We present an effective deep learning- based solution which leverages both Recurrent Neural and the Attention mechanism. The developed system is capable of classifying sentences written in the English language by analysing their syntactical and lexical complexity. An accurate test phase has been carried out, and the system has been compared with a baseline tool based on the Support Vector Machine. This paper represents an extension of a previous deep learning model, which allows showing the suitability of Neural Networks to evaluate sentence complexity in two different languages: Italian and English

Intelligent Knowledge understanding from students questionnaires

Different categories of online assessments are used by Learning Analytics approaches to monitor students’ progresses. Questionnaires are a valid tool to assess students’ performance. In this perspective, it is important to investigate the relationship between questionnaire answers and students’ skills. Classical Test Theory (CTT) and Item Response Theory (IRT) study the relationships between questionnaires’ answers and hidden latent concepts leading at those results. However, several studies have proven the superiority of IRT in respect to CTT [1]. In the educational domain, the aim of IRT is to provide a mathematical model to predict and evaluate students’ abilities that are measured through a questionnaire. The main idea behind this theory is that latent abilities underlie both the students’ performances and the test items. The term “ability” embeds different cognitive students’ skills, that are strictly related to the topic under evaluation (e.g. solving a mathematical problem requires different skills compared to text understanding). Understanding students’ skills or the lack of these skills can be used as feedback for supporting students to identify their learning needs, to measure teaching effectiveness and discover difficult topics [2]. However, latent skills are difficult to be manually extracted since they are topic specific, thus requiring expert analysis. Non-negative matrix factorizations (NMFs) are dimensionality reduction techniques that are able to describe original data as an additive linear combination of hidden factors [3]. In the educational domain they have been proven to be effective for extracting hidden skills from questionnaires items responses, and for profiling students in terms of these skills [4]. NMFs are highly interpretable since hidden skills are described in the same space of the original data, thus helping an intelligent analysis of the results from domain experts. In this work, students’ answers to the Maths Challenge competition, that has been carried out in 2021 at the University of Foggia, have been analyzed through NMF. Moreover, results have been explained in terms of a-priori knowledge on item’s topics, and mathematical assessment measures. The article is structured as follows: data and the use of NMF algorithms in educational domain will be briefly described in paragraph 2. Results will be summarized in paragraph 3 and conclusions and future works will be detailed in paragraph 4