The elaboration of future narratives in Design Fiction has always been an individual activity, showing the unique vision of those who elaborated them and proposed the diegetic artifacts. Creating narratives collectively became possible with the Participatory Design Fiction methodology, which recommends, however, that participants are in the same physical location. We present a methodology to enable the creation of future narratives and their smart devices by a group of geographically dispersed volunteers, the Participatory Design Fiction Extend. With it, it was possible to train people, without knowledge about Design Fiction, in the elaboration of narratives, as well as about smart devices in the future and diegetic artifacts. Volunteers produced narratives and proposed intelligent devices that were later analyzed looking for similarities, differences and complementary ideas, in the light of the concepts of the collective unconscious, enabling the construction of a unique narrative and an intelligent device that contemplated most of the foreseen functionalities.

Abstract Algorithms designed to translate textual content into sign language (SL) expressed through avatars have been used to reduce accessibility barriers. Our research aimed to identify whether the VLibras tool, widely adopted on Brazilian government websites, is an effective accessibility solution for automatic translation into SL. It is an exploratory and applied qualitative research project involving a bibliographic review and support from expert interpreters. We conducted two experimental studies using sequential chronological cuts and applying prescriptive and semantic analyses. We present evidence that there is no actual translation into SL in the automatic translation process performed by the VLibras translation algorithm (TA) but only a transposition of part of the SL lexicon to the Portuguese morphosyntactic structure. The automatic translation of long texts and texts with complex syntactic structures results in excessive pauses and dactylology for words that have a sign registered in the basic SL dictionary. Using human–computer interaction concepts to evaluate automatic translation into sign language by the VLibras TA expands the existing theoretical discussion. It also contributes to minimizing communication problems caused by the discrepancy between the original message and the machine translation, a practical applicability of this study.

Fake news is a problem for society as some people trust the message and react accordingly, propagating the lie. The consequences vary from a simple laugh to a death sentence. Research has focused on distinguishing between fake and fact through machine learning techniques. Most literature reviews and mappings focus on techniques that identify the fake over the message content. In a complementary way, we investigate fake news detection approaches based on the senders’ behavior on social media. We present a conceptual framework that deep dives into different aspects of the main categories of persuader, clarifier, and gullible actors. We also consider automatic and manual profiles. The results provide an overview of existing works on profile trustworthiness in fake news spread and shed light on possible research directions.

Linking data by finding matching instances in different datasets requires considering many characteristics, such as structural heterogeneity, implicit knowledge, and URI (Uniform Resource Identifier)-oriented identification. The authors propose a context-independent approach to align Linked data through an alignment process based on the ontological model’s components and considering data’s multidimensionality. The researchers experimented with the proposed approach against two methods for aligning linked data in two datasets and evaluated precision, recall, and f-measure metrics. The authors also conducted a case study in a real scenario considering a Brazilian publication dataset on computers and education. This study’s results indicate that the proposed approach overcomes the other methods (regarding the precision, recall, and f-measure metrics), requiring less work when changing the dataset domain. This work’s main contributions include enabling real datasets to be semi-automatically linked, presenting an approach capable of calculating resource similarity.

Social media searches are frequently employed by users to keep them up to date about ongoing events and learn broadly about public opinion on topics that are unfamiliar to them. Nevertheless, there are rising concerns about the results returned that can reinforce users' existing biases - the inclination to one opinion over another. This paper introduces a tool, called BiasChecker, that contributes to the check for bias in search results on a social media platform. BiasChecker follows a distributed and extendable architecture that allows us to simulate users following and unfollowing accounts, search for different polarised topics in a concurrent manner and measure bias. It may be applied to multiple social media platforms. The proposed tool takes into account several factors that can interfere with the detection of bias, e.g., the cross-over effect, geolocation, IP address, and language.

Simplification is a common practice to allow students to understand complex concepts. However, such practice may lead to oversimplification and knowledge compartmentalization problems. This paper proposes a method to minimize the effects of oversimplification and knowledge compartmentalization through a five-step processing chain, following instructional design principles, to foster advanced knowledge acquisition. The proposed method was applied to online courses and a discussion for an example of a Data Science course is provided to show its applicability.

Knowledge communities are composed of individuals who share the same interests and voluntarily work together to expand knowledge and understanding of a domain through learning and sharing. When a user asks a question in a knowledge community, he or she may usually add tags to categorize the question. These tags make it easy for experts to find the question and then answer it. One of the problems with this tagging system is that there is no hierarchy to aid in the search for broader themes. This study proposes a way of hierarchizing the folksonomy of an online knowledge community. In order to verify the applicability of the proposed approach, we used the Stack Exchange's biology community. Through a crowdsourcing evaluation it was possible to show that the developed approach performed better than other heuristics and it was also possible to identify quality issues on the description of WordNet synsets.

Learning basic concepts before complex ones is a natural form of learning. This paper addresses the specific problem of identifying concept prerequisites to inform about the basic knowledge required to understand a particular concept. Briefly, given a target concept c, the goal is to (a) find candidate concepts in a Knowledge Graph (KG) that serve as possible prerequisite for c; and, (b) evaluate the prerequisite relation between the target and candidates concepts via a supervised learning model. Our approach explores the DBpedia Knowledge Graph and its semantic relations to find candidate concepts as well as a pruning step to reduce the candidate concept set. Finally, we employ supervised learning algorithms to evaluate and generate a list of prerequisites for the target concept. A ground truth created based on expert knowledge is used to validate our approach, exhibiting promising results with a precision varying between 83% and 92.9%.

English learning comprises the development of four abilities: listening, speaking, reading, and writing. Commonly, English teachers use songs to develop students' listening because it provides a fun approach for improving vocabulary and perceiving different accents. However, every time a teacher needs to prepare a listening lesson using songs, s/he needs to search for songs that contain the words that are being studied in that lesson, and also download and edit the lyrics, substituting the words by blank spaces to be filled by the students while they listen to the music. This paper presents the application of Information Retrieval (IR) techniques to index and retrieve songs according to a set of intended words, besides a module that prepares the songs for listening lessons, substituting the terms by blank spaces. Results reveal that the tool is well-succeeded to provide the aforementioned functionalities. Moreover, a teacher used the tool in the context of her English class, approving the solution provided and contributing to reduce the effort for preparing a pedagogical activity for a listening lesson.

Exploratory search is a specialized form of information retrieval and discovery. The main aim of the exploratory search is to investigate given information domain to acquire knowledge about it - that is, to learn something about that domain. The level of difficulty of information can affect the learning process and its success. Information with low complexity and relative ease may be uninteresting to learners, who might find it tedious and unrewarding. On the other hand, information with high complexity and difficulty can demoralize learners who perceive it as being impossible, triggering feelings of anxiety. Whether too complex or not complex enough, the seek for information can result in little or no learning. This paper aims to (i) identify features that indicate the complexity of the information represented by a concept; (ii) analyze the features in order to provide a coherent sequence of concepts that balances complexity and learners interest; and, (iii) discuss the challenges, difficulties, and validity of sequencing concepts based on features that indicate complexity. The results show that diversity and variety of concepts as well as detailed semantic descriptions and relations act as indicators for the ideal order of presentation of the information.

Online Q&A communities play a key role in informal learning. This paper reports on an investigation in Q&A communities aiming at bringing a new perspective to the problem of detecting trustworthy users through feature learning methods. These users write useful posts and contribute to the community growth, thus to knowledge dissemination. We propose two feature learning methods and demonstrate that they outperform the state of the as well as are competitive with works that addressed the same problem via hand-engineering process.

Web search engines have gained importance as tools capable of connecting informal and self-learning with formal learning by aiding individuals in retrieving relevant information through the formulation and modification of their queries. Understand the differences between query states and their transitions becomes increasingly important, as doing so makes the optimization of search engines' results according to educational uses and needs possible. This paper introduces the ESKiP Taxonomy of Query States, a classification framework validated in an experiment involving two different query log datasets. It enables the comparison between the behaviors of users in search for knowledge (learners) and users performing transactional or factual searches in Web search engines.

For some years, motivation has been object of study in Psychology and Education. The main contribution of this work was to create an ontology that combines concepts related to motivation from two theories: the Self-Determination Theory and the Functional Approach to Volunteer Motivations. Through this alignment of concepts it is possible to understand better whether the factors that lead the individual to remain in volunteering are extrinsic or intrinsic. Results show that the process of unification of these theories has a high degree of association between the concepts studied. However, it demands more study on other Psychology theories, although it has great potential for capturing the users' motivation characteristics in Information Systems and promote adaptability.

Classifying content on the Web has been a common subject of research, since the amount of available data on the Web, especially in text format, grows every day. In this paper it is proposed a correlation index to measure how close a classification system based on Wikipedia categorization is of a service provided by Watson IBM that has the same purpose: text and resourceclassification on the Web.

Q&A forum relies on people with common interest to provide answers to questions. Q&A communities present a large number of unanswered questions and an automatic methodology could recommend related answers or establish relationships between associated topics. In this paper we propose to investigate the correlation of semantic entities of questions and answers in Q&A communities. The results obtained can be used as support for future recommendations for answers to unanswered questions in Q&A communities, without the intervention of experts.

This paper presents an exploratory search model capable of assisting the visualization of search patterns and clarifying best practices associated to users' decision-making process, with implications in areas related to information retrieval, humancomputer interaction, Web searching and educational technology. The Exploratory Search Knowledge-Intensive Process model considers tasks and search activities as part of a chain of actions that help clarify the reasons why a subject is searched. It also supports the visualization on how the information retrieved is used to define decision criteria about which data is worth extracting, to draw inferences, and to create a shortcut to understanding.

This paper reports on an investigation into outstanding and ordinary users of two Question & Answer (Q&A) communities. Considering some learning-related perspectives such as participation, linguistic traits, social ties, influence, and focus, we found that outstanding users are (i) more likely to engage in discussions; (ii) they tend to use more sophisticated linguistic traits; (iii) their inclusion into a discussion results in longer debates; (iv) they value the diversity of their connections; (v) they participate in several topics, rather than one specialist niche. These findings allow us to use behavioral patterns to predict whether a given user is outstanding and also predict which answer gives a definitive solution for a question.

Searching as Learning and Information Seeking require exploratory search to be modeled for supporting learning. The present paper introduces a model of exploratory search that was applied on web searching in language teacher education, which promoted its evolution and validation, and enabled a visualization of search pattern and learning process. This model was able to help clarify best practices associated to users' decision-making process regarding suitable and not suitable information and to capture the relevance of context variables, personal skills and expertise that users utilize as filters for the search.

This paper investigates expert behaviour in Q&A communities in order to understand their influence in online discussions. Our evaluation shows that experts are more likely to provide help than non-experts, and when they participate in a discussion, the quality and length of the discussions tend to increase. In addition, we propose the usage of two models (Artificial Neural Network and Stochastic Gradient Boosting) to predict worthy discussions in the community. The results show that some adjustments in the models' parameters and in the input data can significantly improve the quality of the predictions.

To reduce the complexity intrinsic to LD manipulation, software tools are used to publish or consume data associated to LD activities. However, few developers have a broad understanding of how software tools may be used in publication or consumption of Linked Data. The goal of this work is to investigate the use of software tools in Linked Data publication and consumption processes. More specifically, understanding how these software tools are related to process of publication or consumption of LD. In order to meet their goal, the authors conducted a Systematic Literature Review (SLR) to identify the studies on the use of software tools in these processes. The SLR gathered 6473 studies, of which only 80 studies remained for final analysis (1.25% of the original sample). The highlights of the study are: (1) initial steps of the publication process are fairly supported by the software tools; (2) Non-RDF serialization is fairly supported in publication and consumptions process by the software tools; and (3) there are non-supported steps in consumption and publication processes by the tools.

ISCOOL is an interactive educational game for text analysis and interpretation. It draws from several reference datasets, providing users with information about people, organisations and locations, as well as word definitions and historical facts that serve as the basis for reading comprehension and provide a wider context for information to be accessed, interpreted and understood. In game-play, users choose images to best illustrate a read text. The interactivity of the game encourages users to test their knowledge, and critically analyse what was read. ISCOOL was assessed by students with low literacy levels in English. The results show high levels of acceptance and applicability to genuine learning activities.

Understanding students' strengths and weaknesses can help in the design of teaching materials to successfully bridge identified gaps. Formal exams are useful to gauge the extent of learners' memorised information, but have been critiqued for not reflecting the true extent of learners knowledge. Diagnostic assessment assists teachers in setting task-specific plans and goals, for both individual students, and the learner-group as a whole. In this paper, we describe Treasure Explorers, a Game With a Purpose (GWAP) with a multilayered structure that facilitates the learning process, promotes user retention, and is designed to reward contributing players. A comprehensive evaluation based on game log and TAM model was conducted. The log of over 5,500 records reveals that aspects of engagement and learning had occurred, and qualitative evaluations show the applicability and usability of games as diagnostic assessment tools. Treasure Explorers assists teachers in identifying student difficulties, and has significant potential to help educators plan for lesson content.

Question and Answer (Q&A) communities (such as Stackoverflow) have become important places for information exchange and knowledge creation. Their success relies predominantly on two aspects of the feedback generated by their members: quality and speed. Of these, the former reflects on the reputation of the community, whilst the latter is indicative of the efficiency of the Q&A system to correctly answer a given question. In this paper, we present a three phase study for identifying and recommending topical experts in Q&A communities. The first phase investigates the most relevant criteria for identifying reputable members of the community (often experts in a given field), the second phase introduces an approach based on semantic annotations to ascertain their area of specialism, and the last phase presents a method to recommend experts to answer questions in their areas of expertise. Our evaluation (carried out using real-world data from the Biology Stack Exchange Q&A community) shows that the numbers of answers provided by each member can be used as reliable indicators of expertise, and semantic annotations can be successfully used to identify the topics in which they specialize. Furthermore, on average, 74% of the recommendations suggested by our method were successful.

Microblogs have been used in the educational context. However, differently from following friends' status messages, in the educational scenario it is important to follow the discussions, to understand the flow of messages. You Flow microblog was developed for that purpose. It is a microblog that provides the main structures of discourse that are available on communication systems as well as messages' categorization according to a lesson plan. An exploratory case study allowed analyzing the use of the structures of discourse on the microblog in an educational context. Then, an explanatory case study showed an increase in the participation on discussions of topics according to a lesson plan.

A Learning Object composition model based on semiotic aspects of narrative is proposed with the specification of space, time and semiotic constraints. Learning objects are considered as signs, since they convey meaning through a physical support. Space, time, semiotic and pedagogical requirements are addressed to satisfy constraints in order to prevent overlapping medias so that the cognitive load can be reduced.

The Internet is an invaluable source of information that can and should be used to help education. One of the possibilities of Internet in this area is the search for resources that complement the learning process, usually done with the support of the search engines. This search is generally performed using keywords, which implies on contextless results. This work uses information extraction techniques applied to educational resources to expand the queries done by students, adding contextual information in the search and thus recovering more appropriate educational resources. A prototype was developed according to the proposed architecture and a case study conducted in a Brazilian university presented results showing that this proposal can be used in an educational environment to improve the search of educational resources.

In order to maintain their replay value and entertain the users, interactive storytelling systems demand the support of algorithms that, besides providing diversity of plots (without losing coherence) and possibilities of interaction, are capable of generating stories in a suitable amount of time, so that the user experience doesn't result in frustration. In this context, automated planning algorithms are interesting alternatives, as they can create multiple plots that are coherent with the intended genre. A well established approach used to improve the performance of planners is the use of HTN planning techniques. In this paper, we present NDetHPlan, a general purpose planning algorithm that combines HTN and nondeterminism to cope with such requirements. The planner presented here can also deal with failed attempts to achieve goals, an especially valuable feature to create dramatic tension in the context of interactive storytelling. NDetHPlan has been incorporated to Log tell, a logic-based tool for interactive generation and dramatization of stories.

Despite all the research efforts in the last decades, information integration is a problem yet to be solved in real organizations, especially when it involves semantic issues. A complete and precise shared representation of all the concepts involved in the integration tasks is required to prevent several kinds of problems, such as misinterpretation of a piece of information by different business stakeholders, inconsistent data integration procedures, and incorrect information exchange between applications. A key goal of any conceptual data model is to provide the best possible understanding of its subjacent domain. Ontologically well-founded conceptual models present themselves as a solution to represent a domain in a more correct and complete scheme. Current well-founded conceptual modeling representation languages, however, focus on the structural perspective. On the other side, there are several restrictions that influence the behavior of the concepts of the domain; these restrictions are frequently represented as business rules. Business rules contribute to restrict the semantics of the domain concepts and their relationships. However, structural and behavioral perspectives are usually represented in distinct and non-integrated artifacts. This separation prevents complete understanding of the underlying semantics of the domain. This work proposes preliminary ideas towards integrating these two domain perspectives in a semantically rich and complete conceptual data model, represented as an ontology. The result is a more complete conceptual model, which may be further used in more reliable information integration processes.

There is an increasingly number of worldwide available content on the Web, which could be reused to support learning processes. However, web content is getting more and more structured as multimedia files and reusing it implies on using the whole file, which limits its applicability and the return on investment of its development. So, an interesting approach is the segmentation of content, structuring it on reusable segments. Furthermore, once created, these segments can be reused in an easy way in different educational contexts. This paper discusses the segmentation process and presents the results obtained from a case study conducted using a prototype to evaluate this approach.

An increasing volume of information is available on the web and usually is expressed as text, representing unstructured or semi-structured data. Thus, semantic information is implicit in these texts, since they are mainly intended for human consumption and interpretation. Therefore, it is not easy to automatically identify concepts or establish relations among them inside the texts. In particular, some web sites contain information on historical data about artistic manifestations like literature or music. This kind of site contains a body of knowledge on the domain, and usually is constructed with some format and content patterns that may be useful for information extraction. In order to make this information available as a structured knowledge base, an information extraction process is necessary. Ontologies are an appropriate way to represent structured knowledge bases, enabling sharing, reuse and inference. In this paper, it is described an information extraction process cycle for populating a domain ontology using texts available on the internet to extract instances of concepts, events and relations, based on existing ontology development methodologies and information extraction techniques. Through this process, latent concepts and relations expressed in natural language can be extracted and represented as an ontology, allowing new uses of the available content. A case study that applies this process is presented.

Developing high quality learning content material is expensive and time-consuming. Nowadays, in order to reduce costs and development time, there is an emphasis on reuse. The approach of object orientation has been used for promoting reuse. When this approach is applied to the development of learning content, it results in learning objects. However, there isn't a well-accepted and used methodology for the development of such objects and usually the reuse is based on the multimedia files that are referenced through metadata. In this paper we describe three levels of information that should be considered when dealing with e-learning content. An analogy to Data Warehouse architectures is proposed. Our approach allows keeping the already existing LO repositories and their autonomy, while the semantics of the content is structured to be reused in a personalized way. In addition, the mature of Data Warehousing technologies enhances the implementation of the 3-level information architecture for e-learning, which leads to a richer scenario for content reuse. Finally, the Data Warehouse idea of navigating (i.e., exploring content) can also stimulate students to go further on the quest for knowledge. We present how existing standard proposals can be used through the three levels and describe a case study.

E-learning has increased on importance as people realize that the use of technology can improve the learning process. Consequently, new learning environments have been developed. The strategy is to develop systems from a "semi-complete" framework, using a mechanism for components configuration, so that it is possible to make available a set of systems with the smallest number of components to each specific application, and to allow components reuse. We present a general view of the proposed e-learning framework, which is composed of following components: data and metadata management, groupware management, content development management, assessment and evaluation management, interface management, role and security management and rule management. In addition there is a control component mechanism that diminishes the number of interdependencies between the services.