Search

Objective: The primary aim of the study was to test the allegedly facilitating role of insight-like strategy in the detection of masked solutions Compound Remote Associates problems (CRA problems). Method: A sample of 114 participants solved 19 CRA problems presented online. Participants were requested to solve the problems in which either the solution to the CRA problems was randomly presented in a masked condition or no solution was provided. After each trial participants were requested to report whether they used insight or analytical strategy.Participants were also required to complete a sensation seeking scale and a measure of creativity. Results: The results showed a small, but robust correlation between the CRA problems accuracy and the degree of insight-type strategy used for their solution. The degree of sensation seeking, the score of creativity and the outcome of the manipulation check did not reveal any influence on the CRA problems solution.Conclusion: The use of intuitive strategies may facilitate psi-related creative problem solving, but confirmatory research is needed.

In this paper, we present SaR-Web, a multimodal web search tool that provides automatic support to searching as learning processes. Inspired by the work of Richard Rogers and the Digital Methods Initiative, SaR-Web compares the results of queries across search engine language domains, and visualizes search results with a semantic added value, thus facilitating cross-linguistic and cross-cultural comparisons of results. The comparison between search results in different languages is enabled through the visualization of semantic concepts extracted by means of a NER tool from the search results. The SaR-Web system has the potential to support highlevel learning activities described in Bloom's taxonomy such as: identifying and analyzing patterns, comparing, integrating, and creating new ideas.

Concept maps are significant tools able to support several tasks in the educational area such as curriculum design, knowledge organization and modeling, students' assessment and many others. They are also successfully used in learning activities in which students have to represent domain knowledge according to teacher's assignment. In this context, the development of Learning Analytics approaches would benefit of methods that automatically compare concept maps. Detecting concept maps similarities is relevant to identify how the same concepts are used in different knowledge representations. Algorithms for comparing graphs have been extensively studied in the literature, but they do not appear appropriate for concept maps. In concept maps, concepts exposed are at least as relevant as the structure that contains them. Neglecting the semantic and didactic aspect inevitably causes inaccuracies and the consequently limited applicability in Learning Analytics approaches. In this work, starting from an algorithm which compares didactic characteristic of concept maps, we present an extension which exploits a semantic approach to catch the actual meaning of the concepts expressed in the nodes of the map.

The retrieval and composition of educational material are topics that attract many studies from the field of Information Retrieval and Artificial Intelligence. The Web is gradually gaining popularity among teachers and students as a source of learning resources. This transition is, however, facing skepticism from some scholars in the field of education. The main concern is about the quality and reliability of the teaching on the Web. While online educational repositories are explicitly built for educational purposes by competent teachers, web pages are designed and created for offering different services, not only education. In this study, we analyse if the Internet is a good source of teaching material compared to the currently available repositories in education. Using a collection of 50 queries related to educational topics, we compare how many useful learning resources a teacher can retrieve in Google and three popular learning object repositories. The results are very insightful and in favour of Google supported by the t-tests. For most of the queries, Google retrieves a larger number of useful web pages than the repositories (\(p < .01\)), and no queries resulted in zero useful items. Instead, the repositories struggle to find even one relevant material for many queries. This study is clear evidence that even though the repositories offer a richer description of the learning resources through metadata, it is time to undertake more research towards the retrieval of web pages for educational applications.

There are numerous commercial tools and research prototypes that offer support for measuring technical debt. However, different tools adopt different terms, metrics, and ways to identify and measure technical debt. These tools offer diverse features, and their popularity / community support varies significantly. Therefore, (a) practitioners face difficulties when trying to select a tool matching their needs; and (b) the concept of technical debt and its role in software development is blurred. We attempt to clarify the situation by comparing the features and popularity of technical debt measurement tools, and analyzing the existing empirical evidence on their validity. Our findings can help practitioners to find the most suitable tool for their purposes, and researchers by highlighting the current tool shortcomings.

A patient, nowadays, acquires on-line health information mainly by means of a search engine. Generic search engines have been shown to be limited and unsatisfactory, at times, because of their generic searches that overload users with the amount of results. Moreover, they are not able to provide customized information to different types of users. At the same time, specific search engines mostly work on medical literature and provide extracts from medical journals that are mainly useful for medical researchers and experts. As a consequence, the found health information may or may not help a user (mainly a non-expert one) for a full comprehension of what he/she is looking for (e.g., a condition, a therapy or a drug). This may negatively affect his/her empowerment process and the interaction with healthcare professionals such as doctors. This work presents a custom search engine, FACILE, that allows to overcome the limitations discussed above by facilitating finding and comprehension of on-line information and thus positively affecting the empowerment process and communication/interaction with healthcare professionals. A user, both a medical expert or non-expert, can specify his/her information requirements in terms of language level, specific information searched, and information quality. FACILE will provide the links to the web pages that comply with such requirements. In particular, FACILE will provide links to web pages with different language levels (simple or complex) and, for each link, the response page will show the requested specific information (condition, therapy, drug, etc.) and a graphical indication (0–5 stars) of the information quality.

A patient, nowadays, acquires health information from the Web mainly through a “human-to-machine” communication process with a generic search engine. This, in turn, affects, positively or negatively, his/her empowerment level and the “human-to-human” communication process that occurs between a patient and a healthcare professional such as a doctor. A generic communication process can be modelled by considering its syntactic-technical, semantic-meaning, and pragmatic-effectiveness levels and an efficacious communication occurs when all the communication levels are fully addressed. In the case of retrieval of health information from the Web, although a generic search engine is able to work at the syntactic-technical level, the semantic and pragmatic aspects are left to the user and this can be challenging, especially for elderly people. This work presents a custom search engine, FACILE, that works at the three communication levels and allows to overcome the challenges confronted during the search process. A patient can specify his/her information requirements in a simple way and FACILE will retrieve the “right” amount of Web content in a language that he/she can easily understand. This facilitates the comprehension of the found information and positively affects the empowerment process and communication with healthcare professionals.

Architectural smells can negatively impact on different software qualities and can represent a relevant source of architectural debt. Several architectural smells have been defined by different researchers. Moreover, both academia and industry proposed several tools for software quality analysis, but it is not always clear to understand which tools provide also support for architectural smells detection and if the tools developed for this specific purpose are effectively available or not. In this paper we propose a catalogue of architectural smells for which, at least one tool able to detect the smell exists. We outline the main differences in the detection techniques exploited by the tools and we propose a classification of these architectural smells according to the violation of three design principles. acceptedVersion

Search of "right" health information by patients/citizens is an important step towards their empowerment. The number of health information seekers on the Internet is steadily increasing over the years so it is crucial to understand their information needs and the challenges they face during the search process. However, generic search engines do not make any distinction among the users and overload them with the amount of information. Moreover, specific search engines/sites mostly work on medical literature and are built by hand. This paper analyses the possibility of providing the user with tailored web information by exploiting the web semantic capabilities and, in particular, those of schema.org and its health-lifesci extension. After presenting a short review of the main user requirements when searching for health information on the Internet, an analysis of schema.org and its health-lifesci extension is shown to understand the main properties and semantic capabilities in the health/medical domain. Finally, an initial mapping among user requirements and schema.org elements is presented in order to provide expert and non-expert user categories with web pages that satisfy their specific requirements.

Background. Architectural smells and code smells are symptoms of bad code or design that can cause different quality problems, such as faults, technical debt, or difficulties with maintenance and evolution. Some studies show that code smells and architectural smells often appear together in the same file. The correlation between code smells and architectural smells, however, is not clear yet; some studies on a limited set of projects have claimed that architectural smells can be derived from code smells, while other studies claim the opposite. Objective. The goal of this work is to understand whether architectural smells are independent from code smells or can be derived from a code smell or from one category of them. Method. We conducted a case study analyzing the correlations among 19 code smells, six categories of code smells, and four architectural smells. Results. The results show that architectural smells are correlated with code smells only in a very low number of occurrences and therefore cannot be derived from code smells. Conclusion. Architectural smells are independent from code smells, and therefore deserve special attention by researchers, who should investigate their actual harmfulness, and practitioners, who should consider whether and when to remove them., Comment: (in press)

Health literacy constitutes an important step towards patient empowerment and the Web is presently the biggest repository of medical information and, thus, the biggest medical resource to be used in the learning process. However, at present web medical information is mainly accessed through generic search engines that do not take into account the user specific needs and starting knowledge and so are not able to support learning activities tailored to the specific user requirements. This work presents “ULearn” a meta engine that supports access, understanding and learning on the Web in the medical domain based on specific user requirements and knowledge levels towards what we call “balanced learning”. Balanced learning allows users to perform learning activities based on specific user requirements (understanding, deepinning, widening and exploring) towards his/her empowerment. We have designed and developed ULearn to suggest search keywords correlated to the different user requirements and we have carried out some preliminary experiments to evaluate the effectiveness of the provided information.

Open data is publicly available data that can be universally and readily accessed, used, and redistributed. Open data holds particular potential in the health and social sectors but, presently, health and social data are often published in a ‘closed’ format.There are different tools that allow to ‘open’ data, clean, structure and process them in order to elaborate them and build advanced services but, unfortunately, there is no single tool that can be used to perform all different tasks. We believe that the availability of Open Data in the health and social fields should be greatly increased and a way for creating new health and social services should be provided. In this paper, we present a framework that allows to create health and social Open Data starting from whatever is available on the web and to easily build advanced services based on those data.

The number of people looking for health information on the Internet is constantly growing. When searching for health information, different types of users, such as patients, clinicians or medical researchers, have different needs and should easily find the information they are looking for based on their specific requirements. However, generic search engines do not make any distinction among the users and, often, overload them with the provided amount of information. On the other hand, specific search engines mostly work on medical literature and specialized web sites are often not free and contain focused information built by hand. This paper presents a method to facilitate the search of health information on the web so that users can easily and quickly find information based on their specific requirements. In particular, it allows different types of users to find health web pages with required language complexity levels. To this end, we first use the structured data contained in the web to classify health web pages based on different audience types such as, patients, clinicians and medical researchers. Next, we evaluate the language complexity levels of the different web pages. Finally, we propose a mapping between the language complexity levels and the different audience types that allows us to provide different types of users, e.g., experts and non-experts with tailored web pages in terms of language complexity.