Abstract
This study contributes to learning improvement in practical classes in Computer Network technology courses, using the Physical Technological Laboratory (PTL) as a tool. Multimodal narration content analysis was used, which aggregates and organises the data collected in the PTL environment. Based on the results, we infer that both the student and the teacher use the physical laboratory as a tool since the detected physical interactions prove its use and reuse. Evidence of causality between teacher epistemic movements and learning in terms of physical interactions, epistemic practices, and student autonomy was also noted. Contributions were: (1) In the context of work in networks PTL, the variety and quality of epistemic practices of students are enhanced if there is autonomous work concomitant with the physical interaction of students with the respective artifacts. (2) Teacher action can better promote epistemic practices, stretching beyond direct action if there is an 'orchestration' of teacher mediation patterns.

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?