Abstract

Abstract
Automated assessment is an essential part of eLearning. Although comparatively easy for multiple choice questions (MCQs), automated assessment is more challenging when exercises involve languages used in computer science. In this particular case, the assessment is more than just grading and must include feedback that leads to the improvement of the students’ performance. This paper presents ongoing work to develop Kora, an automated diagram assessment tool with enhanced feedback, targeted to the multiple diagrammatic languages used in computer science. Kora builds on the experience gained with previous research, namely: a diagram assessment tool to compute di erences between graphs; an IDE inspired web learning environment for computer science languages; and an extensible web diagram editor. Kora has several features to enhance feedback: it distinguishes syntactic and semantic errors, providing specialized feedback in each case; it provides progressive feedback disclosure, controlling the quality and quantity shown to each student after a submission; when possible, it integrates feedback within the diagram editor showing actual nodes and edges on the editor itself. © Hélder Correia, José Paulo Leal, and José Carlos Paiva

Abstract

Abstract
Setting: Confirmation of tuberculosis (TB) in children is difficult, so clinicians use different procedures when deciding to treat. Objective: Identify criteria to initiate and maintain TB treatment in children younger than 5 years-old, without diagnosis confirmation. Design: A web-based survey was distributed by email to the corresponding authors of journal articles on childhood TB. The observations were clustered into disjoint groups, and analyzed by Ward's method. Results: We sent out 260 questionnaires and received 64 (24.6%) responses. Forty-six respondents (71.9%) said that microbiological confirmation was not important for initiation of anti-TB treatment, and that the epidemiological context and signs/symptoms suggestive of disease were most important. Sixty-one respondents (95.3%) said that the decision to continue therapy was mainly dependent on clinical improvement. A cluster of older respondents (median age: 52 years-old) who were active at a hospital or primary health care centre placed the most value on immunological test results and chest X-rays. A cluster of younger respondents (median age: 38 years-old) who were less experienced in management of TB placed more value on Interferon Gamma Release Assay (IGRA) results and chest computed tomography (CT) scans. A cluster of respondents with more experience in treating TB and working at specialized TB centres placed greater value on the clinical results and specific radiological alterations ("tree-in-bud" pattern and pleural effusion). Conclusion: TB management varied according to the age, work location and experience of the clinicians. It is necessary to establish standardized guidelines used for the diagnosis and decision to treat TB in children. (C) 2017 Sociedade Portuguesa de Pneumologia. Published by Elsevier Espana, S.L.U.

Abstract
Nowadays using E-learning platforms such as Intelligent Tutoring Systems (ITS) that support users to learn subjects are quite common. Despite the availability and the advantages of these systems, they ignore the learners' time limitation for learning a subject. In this paper we propose RUTICO, that recommends successful learning paths with respect to a learner's knowledge background and under a time constraint. RUTICO, which is an example of Long Term goal Recommender Systems (LTRS), a.er locating a learner in the course graph, it utilizes a Depth-first search (DFS) algorithm to find all possible paths for a learner given a time restriction. RUTICO also estimates learning time and score for the paths and finally, it recommends a path with the maximum score that satisfies the learner time restriction. In order to evaluate the ability of RUTICO in estimating time and score for paths, we used the Mean Absolute Error and Error. Our results show that we are able to generate a learning path that maximizes a learner's score under a time restriction. © 2017 ACM.

Abstract
The analysis of sentiments, emotions and opinions in texts is increasingly important in the current digital world. The existing lexicons with emotional annotations for the Portuguese language are oriented to polarities, classifying words as positive, negative or neutral. To identify the emotional load intended by the author it is necessary also to categorize the emotions expressed by individual words. EmoSpell is an extension of a morphological analyzer with semantic annotations of the emotional value of words. It uses Jspell as the morphological analyzer and a new dictionary with emotional annotations. This dictionary incorporates the lexical base EMOTAIX.PT, which classifies words based on three di erent levels of emotions – global, specific and intermediate. This paper describes the generation of the EmoSpell dictionary using three sources, the Jspell Portuguese dictionary and the lexical bases EMOTAIX.PT and SentiLex-PT. Also, this paper details the web application and web service that exploit this dictionary. It presents also a validation of the proposed approach using a corpus of student texts with di erent emotional loads. The validation compares the analyses provided by EmoSpell with the mentioned emotional lexical bases on the ability to recognize emotional words and extract the dominant emotion from a text. © Maria Inês Maia and José Paulo Leal