Collaboration and Technology

Although illiteracy has been in constant decline over the last decades, there are too many reports about people having problems to identify the main ideas contained in texts they read. Reading comprehension is essential for students, because it is a predictor of their academic or professional success. Researchers have developed computer supported learning activities for supporting students develop their reading comprehension skills with varying degrees of success. One of the various advantages of having students work on electronic documents is that computers can help teachers monitor students’ work. One of the problems of these systems is poor usability due to sophisticated human-computer interaction paradigms emulating activities students perform in traditional learning activities for improving reading comprehension with pen and paper. In this paper we report on a research which implements a learning activity based on answers with multiple choice similar to a questionnaire, which is easy to implement in computers and easy to interact with. Although multiple choice questionnaires are associated to summative evaluations, the implemented learning activity uses them within a collaborative learning activity in which students have to justify, first individually then collaboratively, their choice with a short text. The developed system was used and evaluated in a real learning situation; one of the most interesting findings is not only that students who have to justify their option with a text perform better than those who have not, but that the pertinence of the text to the question does not play a major role. This suggests that just asking the students to justify their answers requires them to do a thinking process which otherwise they would not do.

This article seeks to present a case study regarding collaborative learning (CL) in a higher education environment. CL is an approach of teaching and learning where students at a various performance levels work together in small groups with the aim to solve a problem. With CL students developing not only higher-level thinking skills, but also enhancing the soft skills, such as oral communication, social interaction, among others. Currently, the labour market demands professionals with transversal abilities. In this context, the Polytechnic Institute of Leiria changed the curriculum of Web and Multimedia Development (DWM) technological course to create a collaborative learning environment. The process stated on the 2017/2018 scholar year. The results show that students working in a collaborative environment are more motivated and committed. Furthermore, the importance of real projects and the contact with clients were highlighted during the study.

A virtual community is a social group of any size that shares common interests and communicates through the Internet. A user joins a virtual community not only because of its popularity or the quality of its contents, but also owing to the user experience that the platform offers. Anticipated User eXperience (AUX) allows knowing the idealisations, hopes, and desires of the users in a very early stage of any development. Participation is a crucial component in the growth and survival of any virtual community. An essential element for people to participate in a virtual community is that the platform should provide suitable user tools, which are widgets that allow users to interact with their peers. We propose an AUX evaluation framework for user tools, whose intention is to improve their design, and through it, the participation of users.

The increasing amount of scholarly literature and the diversity of dissemination channels are challenging several fields and research communities. A continuous interplay between researchers and citizen scientists creates a vast set of possibilities to integrate hybrid, crowd-machine interaction features into crowd science projects for improving knowledge acquisition from large volumes of scientific data. This paper presents SciCrowd, an experimental crowd-powered system under development “from the ground up” to support data-driven research. The system combines automatic data indexing and crowd-based processing of data for detecting topic evolution by fostering a knowledge base of concepts, methods, and results categorized according to the particular needs of each field. We describe the prototype and discuss its main implications as a mixed-initiative approach for leveraging the analysis of academic literature.

Technological evolution impacts the research and development of new solutions, as well as consumers’ expectations and behaviors. With the advent of the new millennium, collaboration systems and technologies were introduced to support ordinary cooperative work and inter-dependent, socially and culturally mediated practices as integral units of everyday life settings. Nevertheless, existing classification systems are limited in scope to analyze technological developments and capture the intellectual structure of a field, understood as an abstraction of the collective knowledge of its researchers and their socially mediated activities. Ten years after the introduction of Mittleman et al.’s taxonomy, we build upon earlier work and adopt this classification scheme to provide a descriptive, taxonomy-based analysis of four distinct venues focused on collaborative computing research: ACM CSCW, ACM GROUP, ECSCW, and CRIWG. The proposal consists of achieving evidence on technical attributes and impacts towards characterizing the evolution of socio-technical systems via (and for) taxonomic modeling. This study can also constitute an important step towards the emergence of new, potentially more valid and robust evaluation studies combining Grounded Theory with alternative methods and techniques.

Collaborative chat tools and large text corpora are ubiquitous in today’s world of real-time communication. As micro teams and start-ups adopt such tools, there is a need to understand the meaning (even at a high level) of chat conversations within collaborative teams. In this study, we propose a technique to segment chat conversations to increase the number of words available (19% on average) for text mining purposes. Using an open source dataset, we answer the question of whether having more words available for text mining can produce more useful information to the end user. Our technique can help micro-teams and start-ups with limited resources to efficiently model their conversations to afford a higher degree of readability and comprehension.

Effective sign language communication requires not only seeing the signer’s hand, but also seeing facial expressions and body position, especially when communicating in groups. Here, we address the needs of those who use sign language in group settings. First, to better understand issues surrounding sign language group communication, we interviewed sign language users and performed in-loco observations of group communication. Then, we devised a support system projecting the signer’s upper body onto a screen and compared group communication with and without the support system. The results revealed that participants found it difficult to see signers sitting adjacent to them, to follow quick turns in conversation, and to identify the next signer in time. Although signers preferred not to employ our system as their principal communication tool, they found it useful to identify the current signer.

This paper presents a study of telepresence robot environment where each of two remote sites has a telepresence robot from the other. By situating the telepresence robots in the relatively same positions of the remote users in both sites, collaborative physical environment can be realized when the users wear head-mounted displays to see the robots’ views. Such an environment has been implemented as a prototype system. Because how to achieve physical cooperative movement between a telepresence robot and a human is an issue to be studied in this environment, an experimentation of passing by each other with the prototype system was then conducted as an example of cooperative movement. It was observed that the movement was smoother when watching the video from the telepresence robot than when watching the other telepresence robot in front of the user. It suggests the usefulness of nonverbal cues through video combined with the recognition of partner’s position.

In this work we characterize Brazilian online population sentiment on different political events using data from Twitter and we also discuss the advantages of the usage of this social media as data source. The results demonstrated that the Brazilian population uses Twitter to manifest their political view, expressing both positive and negative sentiments regarding political events. This kind of characterization may contribute to build a critical opinion of Brazilian people, once they would not be limited by what is being divulgated by typical media, such as television and newspapers. Additionally, we reinforced the applicability of social media, as Twitter, to make this kind of characterization.

Crowdsourcing (CS) has its superiority in regards to the quick access to workers throughout the world. On the other hand, when viewed from the prospect of clients who are seeking workers, it is difficult to estimate workers’ performance prior to ordering a task in CS. Crowdsourcing service providers (CSP) produce some indices which may be useful in estimating workers’ performance, however, the correlation between workers’ performance and these indices has not been verified.

In this study, several new indices are proposed and their effectiveness are tested via an exploratory experiment using the Japanese-English translation work. The experimental result indicates some of the proposed indices such as the contribution of consciousness to clients, ambition, the degree of difficulty workers show in the work, awareness of the reward, and the degree of colloquial tone in writing show the correlation with the quality of deliverables. In particular, these trends are more significant for low-performers in terms of the quality of deliverables. Therefore, clients may be able to avoid low-performers by using the proposed indices when they choose workers for CS.

Current times are denoting unprecedented indicators of scientific data production, and the involvement of the wider public (the crowd) on research has attracted increasing attention. Drawing on review of extant literature, this paper outlines some ways in which crowdsourcing and mass collaboration can leverage the design of intelligent systems to keep pace with the rapid transformation of scientific work. A systematic literature review was performed following the guidelines of evidence-based software engineering and a total of 148 papers were identified as primary after querying digital libraries. From our review, a lack of methodological frameworks and algorithms for enhancing interactive intelligent systems by combining machine and crowd intelligence is clearly manifested and we will need more technical support in the future. We lay out a vision for a cyberinfrastructure that comprises crowd behavior, task features, platform facilities, and integration of human inputs into AI systems.

Conversational commerce has become an emerging global marketing communication trend in the past few years. Recent studies suggested some beneficial aspects of conversational commerce in customer satisfaction, while some claimed different areas that conventional (traditional) commerce still excels in. Therefore, this research examined and compared conversational commerce with conventional commerce in terms of customer satisfaction towards Thai urban office employees, which helped to determine areas of improvement for conversational commerce sellers. Accordingly, a convenient sampling quantitative and qualitative surveys were conducted with the sample size of 50 (n = 50), on Thai office employees aged 22–60 years. Nine different customer satisfaction factors and commentary session were employed to determine the effectiveness and winner of each commerce type via vertically designed ordinal Likert Scales. Mode scores were utilised as an average comparison tool for customer satisfaction of both commerces.

Results suggested that the two commerces rate competitively in terms of customer satisfaction, with the Likert level of 4 (satisfied) in all factors. However, they excelled in different aspects. Accordingly, conversational commerce rates higher in terms of product cost, keeping customers in touch, and product/brand image, while conventional commerce rated higher in terms of meeting customer needs, point-of-purchase condition, and delivery time.

Although both commerces rated close in terms of seller credibility and product expectancy, conventional commerce tended to be more preferred in customers’ minds. Additionally, the areas of chatbot and cryptocurrency are briefly discussed as a forthcoming conversational commerce trend.

Spontaneity, which is an attitude of voluntarily participating in activities, and sociality, which is the tendency to interact with others and to keep good relationships with others, are developed during childhood and are the fundamental attitudes in social life. To investigate the development of spontaneity and sociality, several researchers have used Learning Stories, in which on-site observers elaborately describe behaviors of each child. However, Learning Stories would need too much labor cost for monitoring and qualitatively evaluating many children. This study proposed a new method of quantitative analysis and visualization to evaluate children’s group behavior related to both spontaneity and sociality during eurhythmics, in which children move in tune with music. It used position data of children for the analysis and visualization. The results showed that children in the 6-year-old class got to work closely with other children in a short time compared to those in the 5-year-old class. Such group behavior would include the actions to spontaneously make a good relationship with others. We hope that this study will be meaningful in creating practical and useful curricula and support methods for children at nurseries.

PBL Exchange is a new web-based, open source platform implemented in Python using the Django framework for sharing competencies among teaching staff at Aalborg University concerning the supervision of problem-based learning (PBL) projects. PBL Exchange uses a crowdsourcing strategy where users ask questions about PBL and answer questions posed by other users. We describe the structure of PBL Exchange and analyze the challenges of building an active user community for the system. A particular challenge has been that of dealing with the highly heterogeneous teaching environments that exist across departments and faculties.

The ColCoMa environment supports the training of workplace-oriented conflict management strategies through virtual role play. The role play relies on a web-based environment in which the participants interact through chat dialogues. Two of the participants (the parties in conflict) are human actors whereas the third role (“mediator”) is occupied by a chatbot. Our study aims at exploring the potential of eye-tracking analyses to assess the quality of cooperation in this situation. The standard assumption is that a certain “convergence” of the visual foci of attention between cooperation partners indicates better coordination and consideration of the other party. In our scenario, this assumption has to be refined by taking into account the different roles (including the role of the chatbot) and the distribution of utterances on the chat history. The eye-tracking parameters are compared to quality criteria such as successful completion of the game or richness/mutuality of the chat interactions. There are quite strong correlations on the aggregate level (taking overall eye-tracking convergence as a global parameter), yet not in terms of synchronicity between convergent eye-tracking and chat interaction. This is possibly due to the specific distribution of roles in our virtual training environment.

Group awareness correspond to an important concept on Groupware applications, allowing individual users to be kept aware of group’s activities and status. Similarly, on Pervasive Computing, context is defined as any relevant information that can be used to characterize the situation of an entity [7]. In this position paper, we advocate that group awareness information should be considered as context information and handled as such. Group awareness information is often employed for decision making, contributing to users’ activities and decisions. It represents also an important clue about user’s context, characterizing individual’s actions regarding the group. As such, group awareness may be used for adaptation purposes, adapting the system behavior, the supplied content or its services. To illustrate this point, we discuss the use of a context distribution system as a group awareness distribution mechanism.

Although Galleries, Libraries, Archives, and Museums (GLAMs) increasingly encourage users to assist in the curation of online collections through open collaboration systems, measuring users’ engagement in these systems is a dynamic and complex challenge. We analyzed 18 user’s actions over 20 days according to the User Engagement Scale (UES) and based on Maximal Repeating Patterns (MRPs) and correlations between user interaction elements and dimensions of user engagement (focused attention, perceived usability, aesthetics, and reward). Our results show differences in usage tactics for users with high, medium, and low scores from UES, and monotonically increasing moderate correlations between perceived usability scores and game design elements. Additionally, we found that the longer the mean time interval between two consecutive user actions during a usage period lasted, the higher the UES score was. These results help to understand what influences user engagement, isolating the effects of user interaction elements.