Abstract
Online microtask labor has increased its role in the last few years and has provided the possibility of people who were usually excluded from the labor market to work anytime and without geographical barriers. While this brings new opportunities for people to work remotely, it can also pose challenges regarding the difficulty of assigning tasks to workers according to their abilities. To this end, cognitive personalization can be used to assess the cognitive profile of each worker and subsequently match those workers to the most appropriate type of work that is available on the digital labor market. In this regard, we believe that the time is ripe for a review of the current state of research on cognitive personalization for digital labor. The present study was conducted by following the recommended guidelines for the software engineering domain through a systematic literature review that led to the analysis of 20 primary studies published from 2010 to 2020. The results report the application of several cognition theories derived from the field of psychology, which in turn revealed an apparent presence of studies indicating accurate levels of cognitive personalization in digital labor in addition to a potential increase in the worker's performance, most frequently investigated in crowdsourcing settings. In view of this, the present essay seeks to contribute to the identification of several gaps and opportunities for future research in order to enhance the personalization of online labor, which has the potential of increasing both worker motivation and the quality of digital work.

Abstract
The human population with disability is rapidly expanding, more than 15% of people worldwide suffer from a disability and, despite the availability of accessibility guidelines, the websites are still inaccessible. Moreover, professionals with knowledge of accessibility and design abilities are hard to come by. Therefore, the current paper addresses the introduction of accessibility to the Software Engineering students through AccessCademy, a gamification-based tool, in a fun way. The activity is delivered via a Web-based learning environment, that presents bad accessibility scenarios or failures based on the Web Content Accessibility Guidelines (WCAG), and then encourages the students to solve them. Furthermore, a case study will be presented that evaluated the learning effectiveness of the tool in the context of a university course. The results demonstrated the potential of AccessCademy which offers students a fun and engaging way to learn about accessibility, to understand the importance of accessible design with WCAG and gain accessible design skills as well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

Abstract
Accessibility in digital labor is a research line that has been trending over the last few years. The usage of crowdsourcing, especially in the form of microtasks, can become an inclusive solution to support accessible digital work. Integrating cognitive abilities tests and task fingerprinting has proven to be effective mechanisms for microtask personalization when considering neurotypical people. In this article, we report the elaboration of usability tests on microtask personalization with neurodivergent people. The preliminary study recruited six participants with autism, attention deficit hyperactivity disorder, and dyslexia. The results obtained indicate that this solution can be inclusive and increase the accessibility of crowdsourcing tasks and platforms. One limitation of this study is that it is essential to evaluate this solution on a large scale to ensure the identification of errors and/or features of cognitive personalization in microtask crowdsourcing. © 2024 IEEE.

Abstract
Microtasks have become increasingly popular in the digital labor market since they provide easy access to a crowd of people with varying skills and aptitudes to perform remote work tasks that even the most capable algorithmic systems are unable to complete in a timely and efficient fashion. However, despite the latest advancements in crowd-powered and contiguous interfaces, many crowd workers still face some accessibility issues, which ultimately deteriorate the quality of the work produced. To mitigate this problem, we restrict attention to the development of two different web-based mini-games with a focus on cognitive personalization. We have conducted a pilot gamified experience, with six participants with autism, dyslexia, and attention deficit hyperactivity. The results suggest that a web-based mini-game can be incorporated in preliminary microtask-based crowdsourcing execution stages to achieve enhanced cognitive personalization in crowdsourcing settings.

Abstract
The current surge in the deployment of web applications underscores the need to consider users' individual preferences in order to enhance their experience. In response to this, an innovative approach is emerging that focuses on the detailed analysis of interaction data captured by web browsers. These data, which includes metrics such as the number of mouse clicks, keystrokes, and navigation patterns, offer insights into user behavior and preferences. By leveraging this information, developers can achieve a higher degree of personalization in web applications, particularly in the context of interactive elements such as online games. This paper presents the WebTraceSense project, which aims to pioneer this approach by developing a framework that encompasses a backend and frontend, advanced visualization modules, a DevOps cycle, and the integration of AI and statistical methods. The backend of this framework will be responsible for securely collecting, storing, and processing vast amounts of interaction data from various websites. The frontend will provide a user-friendly interface that allows developers to easily access and utilize the platform's capabilities. One of the key components of this framework is the visualization modules, which will enable developers to monitor, analyze, and interpret user interactions in real time, facilitating more informed decisions about user interface design and functionality. Furthermore, the WebTraceSense framework incorporates a DevOps cycle to ensure continuous integration and delivery, thereby promoting agile development practices and enhancing the overall efficiency of the development process. Moreover, the integration of AI methods and statistical techniques will be a cornerstone of this framework. By applying machine learning algorithms and statistical analysis, the platform will not only personalize user experiences based on historical interaction data but also infer new user behaviors and predict future preferences. In order to validate the proposed components, a case study was conducted which demonstrated the usefulness of the WebTraceSense framework in the creation of visualizations based on an existing dataset.