Abstract
It has been a quarter of a century since the publication of the first edition of the IEEE International Conference on Computer Supported Cooperative Work in Design (CSCWD) held in 1996 in Beijing, China. Despite some attempts to empirically examine the evolution and identity of the field of CSCW and its related communities and disciplines, the scarcity of scientometric studies on the IEEE CSCWD research productivity is noteworthy. To fill this gap, this study reports on an exploratory quantitative analysis of the literature published in the IEEE CSCWD conference proceedings with the purpose of visualizing and understanding its structure and evolution for the 2001-2019 period. The findings offer valuable insights into the paper and author distribution, country and citation-level productivity indicators, degree of collaboration, and collaboration index. Through this analysis we also expect to get an initial overview of the IEEE CSCWD conference concerning the main topics being presented, most cited papers, and variances in the number of keywords, full-text views, and references.

Abstract
Despite decades of research and development in named entity resolution, dealing with name ambiguity is still a challenging issue for many bibliometric-enhanced information retrieval (IR) tasks. As new bibliographic datasets are created as a result of the upward growth of publication records worldwide, more problems arise when considering the effects of errors resulting from missing data fields, duplicate entities, misspellings, extra characters, etc. As these concerns tend to be of large-scale, both the general consistency and the quality of electronic data are largely affected. This paper presents an approach to handle these name ambiguity problems through the use of crowdsourcing as a complementary means to traditional unsupervised approaches. To this end, we present "AuthCrowd", a crowdsourcing system with the ability to decompose named entity disambiguation and entity matching tasks. Experimental results on a real-world dataset of publicly available papers published in peer-reviewed venues demonstrate the potential of our proposed approach for improving author name disambiguation. The findings further highlight the importance of adopting hybrid crowd-algorithm collaboration strategies, especially for handling complexity and quantifying bias when working with large amounts of data.

Abstract
In this paper, we present and discuss an innovative approach to solve Job Shop scheduling problems based on machine learning techniques. Traditionally, when choosing how to solve Job Shop scheduling problems, there are two main options: either use an efficient heuristic that provides a solution quickly, or use classic optimization approaches (e.g., metaheuristics) that take more time but will output better solutions, closer to their optimal value. In this work, we aim to create a novel architecture that incorporates reinforcement learning into scheduling systems in order to improve their overall performance and overcome the limitations that current approaches present. It is also intended to investigate the development of a learning environment for reinforcement learning agents to be able to solve the Job Shop scheduling problem. The reported experimental results and the conducted statistical analysis conclude about the benefits of using an intelligent agent created with reinforcement learning techniques. The main contribution of this work is proving that reinforcement learning has the potential to become the standard method whenever a solution is necessary quickly, since it solves any problem in very few seconds with high quality, approximate to the optimal methods.

Abstract
There is a growing presence of technology in the daily lives of elementary school students, with a recent exponential rise due to the constraints of remote teaching during the COVID-19 pandemic. It is important to understand how the education system can contribute to helping students develop the required skills for technological careers, without neglecting its obligation to create conditions that allow them to acquire transversal skills and to enable them to exercise full citizenship. The integration of Educational Robotics and block programming activities in collaborative learning environments promotes the development of computational thinking and other ICT skills, as well as critical thinking, social skills, and problem solving. This paper presents a theoretical proposal of a didactic sequence for the introduction to educational robotics and programming with Scratch Jr. It is composed of three learning scenarios, designed for elementary school teaching. Its main goal is to create conditions that favour the development of computational thinking in a collaborative learning environment. With increasing complexity and degree of difficulty, all the tasks root from a common problem: How can we create an algorithm that programs the robot/sprite to reach a predetermined position?

Abstract
With the increasing development of human-AI teaming structures within and across geographies, the time is ripe for a continuous and objective look at the predictors, barriers, and facilitators of human-AI scientific collaboration from a multidisciplinary point of view. This paper aims at contributing to this end by exploiting a set of factors affecting attitudes towards the adoption of human-AI interaction into scientific work settings. In particular, we are interested in identifying the determinants of trust and acceptability when considering the combination of hybrid human-AI approaches for improving research practices. This includes the way as researchers assume human-centered artificial intelligence (AI) and crowdsourcing as valid mechanisms for aiding their tasks. Through the lens of a unified theory of acceptance and use of technology (UTAUT) combined with an extended technology acceptance model (TAM), we pursue insights on the perceived usefulness, potential blockers, and adoption drivers that may be representative of the intention to use hybrid intelligence systems as a way of unveiling unknown patterns from large amounts of data and thus enabling novel scientific discoveries.

Abstract
Authoring 360 multisensory videos is a true challenge as the authoring tools available are scarce and restrictive. In this paper, we propose an authoring tool with three different authoring interfaces (desktop, immersive, and tangible interface) for creating multisensory 360 videos with the advantage of having a live preview of the multisensory content that is being produced. An evaluation of the three authoring tools having into account gender, system usability, presence, satisfaction, and effectiveness (time to accomplish tasks, number of errors, and number of help requests) is presented. The sample consisted of 48 participants (24 males and 24 females) evenly distributed between the different interfaces (8 males and 8 females for each interface). The results revealed that gender does not have any impact in the studied interfaces regarding all the dependent variables; immersive and tangible interfaces have higher levels of satisfaction than desktop interface as it allows more interaction freedom, and desktop interface have the lowest time to accomplish the tasks because people are more familiar with keyboard and mouse.