The Prevalence of Reading Difficulties among Children in Scholar Age

The study investigates the prevalence of reading difficulties among children in scholar age and analyses the socio-demographic characteristics of learners who presented reading difficulties in central Italy. A sample of 623 students 7-11 aged, was assessed with the Italian MT standardized tests. Information on gender, age, handedness, and other socio-demographic variables were also gathered. The study showed that 11% of learners presented poor comprehension skills. The reading speed difficulties were more common than the reading correctness problems: about 7% of children vs 1% were dyslexics due to slow reading. There were no significant differences regarding gender, age. However, dominant hand and the school location seemed to affect the speed difficulties and the comprehension problems. The analyses showed that attending a school located in a rural area was statistically associated with the reading difficulties. Left-handed children were more likely to be slow decoders and/or poor comprehenders. These findings may be used in the early diagnosis of poor readers. These difficulties often have a chronic progression with substantial psychosocial limitations and psychological stress, so children with reading difficulties should be identified as early as possible

SmartHeart CABG Edu

The paper reports on the SmartHeart CABG Edu Android app. The app was conceived to be an innovative and up-to-date tool for patient education, the first of its kind in the Italian context. In particular, the app was developed to provide educational material for patients about to undergo Coronary Artery Bypass Graft (CABG) surgery, a set of self-assessment tools concerning health status (i.e., BMI calculator, LDL cholesterol calculator and anxiety assessment tool) and usability questionnaires (i.e., SEQ and SUS). The paper initially describes the app, then reports on its evaluation, concerning both the app usability and the pre-operative anxiety, and ends by showing the improvements -- derived from the usability evaluation -- put into practice

Pedagogy-Driven Smart Games for Primary School Children

TERENCE is an FP7 ICT European project, highly multi-disciplinary, that is developing an adaptive learning system for supporting poor comprehenders and their educators. Their learning materials are stories and games, explicitly designed for classes of primary schools poor comprehenders, where classes were created via an extensive analysis of the context of use and user requirements. The games are specialised into smart games, which stimulate inference-making for story comprehension, and relaxing games, which stimulate visual perception and which train the interaction with devices (e.g., PC and tablet PC). In this paper we focus on how we used the pedagogical underpinnings and the acquired requirements to design the games of the system

COFLEX: FLEXIBLE BRACELET ANTI COVID-19 TO PROTECT CONSTRUCTION WORKERS

Abstract. To implement the protocol contrasting the diffusion of Covid-19, the employer is required, to ensure the safety and health of the worker at work, to adopt measures related to the control of body temperature (with respect for privacy), the minimum distance during work and all other activities such as breaks, canteen breaks, access to toilets, in addition to the adoption of specifically developed safety procedures, such as e.g. the use of man-down detection devices. In this context, the project aims to illustrate a system able of providing support in the safeguarding of workers' health on construction sites. This system, based on information received from sensors capable of identifying workers' positions (e.g., if less than 1m away) and their vital parameters (e.g., body temperature, gasped breathing), as well as moving objects inside the construction site area (e.g., to check if a worker is passing under a moving crane), will raise early alerts directly to the workers and/or to the central software, with respect for privacy, to immediately activate all the necessary measures to mitigate the risk. The system, based on the data communicated by the various sensors, will store and process them for the purpose of extracting useful information for risk management. The proposed system configured itself as a new product taking advantage from a high Technology Readiness Level maturated from the Smart Safety Belt already developed by some of the authors

2nd International Workshop on Evidence-based Technology Enhanced Learning

Research on Technology Enhanced Learning (TEL) investigates how information and communication technologies can be designed in order to support pedagogical activities. The Evidence Based Design (EBD) of a system bases its decisions on empirical evidence and effectiveness. The evidence-based TEL workshop (ebTEL) brings together TEL and EBD. The first edition of ebTEL collected contributions in the area of TEL from computer science, artificial intelligence, evidence-based medicine, educational psychology and pedagogy. Like the previous edition, this second edition, ebTEL’13, wants to be a forum in which TEL researchers and practitioners alike can discuss innovative evidence-based ideas, projects, and lessons related to TEL. The workshop took place in Salamanca, Spain, on May 22nd-24th 2013

The TERENCE Smart Games: Automatic Generation and Supporting Architecture

TERENCE is an FP7 ICT European project that is developing an adaptive learning system for supporting poor comprehenders and their educators. Its learning material are stories and games. The games are specialised into smart games, which stimulate inference-making for story comprehension, and relaxing games, which stimulate visual perception and not story comprehension. The paper focuses on smart games. It first shows the current prototypes, then it describes the TERENCE system architecture, thus it delves into the generation of smart games instances, by highlighting the role of the constraint-based module therein. Finally, it ends with short conclusions about the planned improvements.

The Design of Learning Material for Poor Comprehenders: Lessons Learnt from Experts

TERENCE is an FP7 ICT European project that is developing an adaptive learning system for poor comprehenders and their educators. The learning material is made of stories and smart games for stimulating reading comprehension. The design of stories and smart games is also based on data collected from experts for the analysis of the context of use of the system, and is incrementally revised via evaluations of prototypes of stories and games, with domain experts of text comprehension or education as participants. In particular, since smart games are semi-automatically generated via artificial intelligence technologies, they contain mistakes that have to be fixed by experts of pedagogy before the games are given to learners. In this paper we focus on the design and evaluations of the TERENCE stories and smart games for poor comprehenders via lessons learnt with domain experts

Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients

Abstract To support physicians in clinical decision process on patients affected by Coronavirus Disease 2019 (COVID-19) in areas with a low vaccination rate, we devised and evaluated the performances of several machine learning (ML) classifiers fed with readily available clinical and laboratory data. Our observational retrospective study collected data from a cohort of 779 COVID-19 patients presenting to three hospitals of the Lazio-Abruzzo area (Italy). Based on a different selection of clinical and respiratory (ROX index and PaO2/FiO2 ratio) variables, we devised an AI-driven tool to predict safe discharge from ED, disease severity and mortality during hospitalization. To predict safe discharge our best classifier is an RF integrated with ROX index that reached AUC of 0.96. To predict disease severity the best classifier was an RF integrated with ROX index that reached an AUC of 0.91. For mortality prediction the best classifier was an RF integrated with ROX index, that reached an AUC of 0.91. The results obtained thanks to our algorithms are consistent with the scientific literature an accomplish significant performances to forecast safe discharge from ED and severe clinical course of COVID-19

The Learners' User Classes in the TERENCE Adaptive Learning System

Nowadays, circa 10% of 7-11 olds turn out to be poor comprehenders: they demonstrate text comprehension difficulties, related to inference making, despite proficiency in low-level cognitive skills like word reading. To improve the reading comprehension of these children, TERENCE, a technology enhanced learning project, aims at stimulating inference-making about stories. In order to design and develop the TERENCE system, we use a user centred design approach that requires an in depth study of the system's main end-users, namely, its learners and educators. This paper reports on the specification of the user classes for the TERENCE learners by means of user-centred design field studies, the resulting global system architecture, and an example use case of the system, with few related GUI's snapshots.European Commision (EC). Funding FP7/SP1/ICT. Project Code: 25741

An AI-Based System for Formative and Summative Assessment in Data Science Courses

Massive open online courses (MOOCs) provide hundreds of students with teaching materials, assessment tools, and collaborative instruments. The assessment activity, in particular, is demanding in terms of both time and effort; thus, the use of artificial intelligence can be useful to address and reduce the time and effort required. This paper reports on a system and related experiments finalised to improve both the performance and quality of formative and summative assessments in specific data science courses. The system is developed to automatically grade assignments composed of R commands commented with short sentences written in natural language. In our opinion, the use of the system can (i) shorten the correction times and reduce the possibility of errors and (ii) support the students while solving the exercises assigned during the course through automated feedback. To investigate these aims, an ad-hoc experiment was conducted in three courses containing the specific topic of statistical analysis of health data. Our evaluation demonstrated that automated grading has an acceptable correlation with human grading. Furthermore, the students who used the tool did not report usability issues, and those that used it for more than half of the exercises obtained (on average) higher grades in the exam. Finally, the use of the system reduced the correction time and assisted the professor in identifying correction errors