Currently, in Scrum, there are different methods to estimate user stories in terms of effort or complexity. Most of the existing techniques consider factors in a fine grain level; these techniques are not always accurate. Although Planning Poker is the most used method in Scrum to estimate user stories, it is primarily effective in experienced teams since the estimation mostly depends on the observation of experts, but it is difficult when is used by inexperienced teams. In this paper, we present a proposal for complexity decomposition in a coarse grain level, in order to consider important factors for complexity estimation. We use a Bayesian network to represent those factors and their relations. The edges of the network are weighted with the judge of professional practitioners about the importance of the factors. The nodes of the network represent the factors. During the user estimation phase, the Scrum team members introduce the values for each factor; in this way, the network generates a value for the complexity of a User story, which is transformed in a Planning Poker card number, which represents the story points. The purpose of this research is to provide to development teams without experience or without historical data, a method to estimate the complexity of user stories through a model focused on the human aspects of developers.

The need to compete for users’ attention and provide them with the best user experience has increased the use of persuasion strategies in modern systems. Currently, Persuasive Systems (PSs) promote healthy behavior for well-being, energy consumption, and learning. Although the educational domain has limited investigation compared to other areas, PSs in education have been shown to be effective in motivating students. This paper summarizes the existing evidence on Persuasive Educational Systems (PESs), emphasizing research on the current design methods, evaluation methods, and characteristics. We follow Kitchenham’s method to perform a systematic literature review about PESs published between 2014 and 2020, with 19 relevant studies selected. We highlight some results from the analysis of selected papers such as persuasion strategies, use of a personalized persuasion technique, study of students’ susceptibility to strategies, integration of gamification mechanisms, and proposed tools to design PES. Moreover, we discuss interesting facts such as the common practice of using more than one tool to design PES, aspects of interaction, persuasion, learning, and the challenges in evaluating persuasive impact. Finally, as the main contribution of the paper we identify the seven necessary characteristics to build a persuasive educational system.

The coronavirus COVID-19 swept the world in early 2020, working from home was a necessity. In the software industry, thousands of software developers began working from home, many did so on short notice, under difficult and stressful conditions. The emotions of developers can be affected by this situation. On the other hand, some well-known soft skills have been emphasized as required for working remotely. Software engineering research lacks theory and methodologies for addressing human aspects in software development. In this paper we present an exploratory study with a survey applied to practitioners in the west border area of Mexico-USA. It is focused on the developers’ wellbeing during pandemic, expressed as emotions, and the perceptions of the level in which soft skills are practiced/required in the working from home mode. The results show that high percent expressed to experience positive emotions, however, a portion of respondents expressed to feel negative emotions. In general terms, 91.0% of respondents expressed to be satisfied working from home, but some conditions should be improved. In the case of soft skills, some of them are revealed as practiced in high level in working from home, but still there is not consensus.

Motivation is an essential element in the learning process. In computer-based tutoring environments, motivational components are as significant as cognitive ones. Previous work established that automatic affective feedback improves student motivation when he/she uses Tutoring Systems. Also, prior work examines the relationship between student’s motivation to learn and personality traits, but only from a partial point of view. The present study analyzes whole personality traits on motivation to learn by students using a Tutoring System. The work involved 30 undergraduate students in a qualitative experiment. The authors examined the results using Chi² to determine the relationship between motivation to use the system and personality; a Naïve Bayes classifier was applied. The findings suggested that Neuroticism is a factor that influences student’s motivation to use the tutoring system. Also, the Naïve Bayes algorithm reaches an accuracy of 90% for the classification.