Abstract
In collaborative learning, evaluating the process involves teamwork dynamics, and assessing the product focuses on the accuracy and quality of the final output. Assessment plays a crucial role, as it defines and measures the effectiveness of group activities to ensure that learning objectives are met. Mediation analysis is an important technique to better understand relationships between variables, specifically designed to test hypotheses about potential causal effects in various areas. However, many research initiatives have been discontinued prematurely due to the Baron-Kenny data restrictions. This research takes a case study of online learning from the Portuguese Open University to determine if and how group selection and interaction frequency affect individual assessment. The contribution lies in applying quantitative causal mediation analysis to collaborative learning assessment. The Lambda Mediation Ratio is proposed to enhance mediation analysis by enabling quick and flexible categorization into full, partial, or no mediation. Using Moodle platform logs and student outcomes, it was possible to find a significant influence of group dynamics on academic performance, highlighting the practical application of this improved methodology in an educational context. These findings reassure us of the relevance and applicability of this research in real-world educational settings. © 2024 Universidad de Murcia. All rights reserved.

Abstract
In this article we extend the Marketing Mix Diffusion (MMD) model to inhomogenous networks (i.e. complex networks of arbitrary topology). The (Homogenous) MMD model is an innovation diffusion model, similar to the Bass model, which includes four decision variables (the 4Ps of Marketing: Product, Price, Place, Promotion). We introduce the Inhomogenous MMD (IMMD) model and we conduct two separate experiments: one based on simulation and another one relying on empirical evidence. The simulation study compares the behavior of the IMMD model with the classic Bass diffusion model. Results suggest that the classic Bass model is able to represent the IMMD curves quite well in most cases. The IMMD is more general and capable of representing extreme scenarios. The empirical study focuses on the geographic diffusion of mobile broadband technology in Japan, combining adoption data with a spatial network of municipalities. The in-sample performance of the model is comparable to the existing methods, which suggests a good explanatory power of the IMMD model.

Abstract
Data maturity models are an important and current topic since they allow organizations to plan their medium and long-term goals. However, most maturity models do not follow what is done in digital technologies regarding experimentation. Data Science appears in the literature related to Business Intelligence (BI) and Business Analytics (BA). This work presents a new data science maturity model that combines previous ones with the emerging Business Experimentation (BE) and causality concepts. In this work, each level is identified with a specific function. For each level, the techniques are introduced and associated with meaningful wh-questions. We demonstrate the maturity model by presenting two case studies.

Abstract
Context: Mobile application testing has gained considerable attention in recent years since mobile devices have become increasingly present in our lives. Unlike traditional software, mobile application testing has to deal with peculiarities, such as screen size and densities, different operating systems, and multiple sensors that increase the complexity of testing. Objective: This paper summarizes and analyzes the current secondary studies on mobile application testing through a tertiary study. Method: We selected and analyzed 21 secondary studies related to mobile application testing. Results: We categorized 21 secondary studies according to their main and specific research topics, test objectives, and testing platforms. Furthermore, we analyze 87 gaps and challenges identified by the secondary studies to understand which gaps have already been addressed and which gaps are still uncovered. Conclusion: Based on the results, we propose a research agenda with 15 open challenges related to mobile application testing to help future research.

Abstract

Abstract
AbstractTest Driven Development (TDD) is one of the major practices of Extreme Programming for which incremental testing and refactoring trigger the code development. TDD has limited adoption in the industry, as it requires more code to be developed and experienced developers. Generative AI (GenAI) may reduce the extra effort imposed by TDD. In this work, we introduce an approach to automatize TDD by embracing GenAI either in a collaborative interaction pattern in which developers create tests and supervise the AI generation during each iteration or a fully-automated pattern in which developers only supervise the AI generation at the end of the iterations. We run an exploratory experiment with ChatGPT in which the interaction patterns are compared with the non-AI TDD regarding test and code quality and development speed. Overall, we found that, for our experiment and settings, GenAI can be efficiently used in TDD, but it requires supervision of the quality of the produced code. In some cases, it can even mislead non-expert developers and propose solutions just for the sake of the query.