Psychological flow has been measured in several areas to analyse to what extent users are engaged in particular tasks, and is relevant in the design of products like software, videogames, and eLearning courses. Although there is an unknown number of questionnaires for evaluating different aspects of psychological flow, the research problem faced in this paper is the analysis of the validity of these questionnaires, since it has only been probed for some of them. Thus, our goal is to synthesize the current evidence regarding validated questionnaires in the English language for psychological flow measurement by conducting a systematic review according to the PRISMA framework. As a result, we found a total number of 34 validated questionnaires to assess flow. The number of their items ranged from 3 to 66, while 63 different dimensions of optimal experiences were taken into consideration. Moreover, the contexts of use differed, including methods to assess flow intensity, prevalence, variations, proneness, metacognitions, in crowds, observed, as dimensions of autotelic personalities, or to differentiate flow from clutch states. As a consequence, this paper facilitates the selection of the questionnaires for research or applied aims, far beyond the classic dichotomy of prevalence–proneness. Moreover, we present a reinterpretation of the nine-dimensional scheme of flow in stages, and recommend future research for engineering and computer science.

Flow Theory has been used to study motivation in educational activities. However, few studies use physiological data to uncover unknown aspects of said data in any context, and isolated individuals are involved as well. In this paper, we present some of the results obtained from two control groups corresponding to two full primary education classrooms, as well as their teacher, using a quasi-experimental design. They participated in two training activities with different instructional designs and three different STEAM subjects: graphic design, video game design using Roblox Studio, and educational robotics. In this sense, the heart rate, its variability, data from accelerometers, and the educational activities carried out by the teacher have been automatically recorded for each participant at every second. To achieve this, we used smartwatches connected to Polar H10 sensors as well as our own apps. At the end of each session, everyone answered the Flow FKS and EduFlow prevalence questionnaires, and the teacher kept a class journal. Through this, we aim to understand whether the Flow Theory models derived from the FKS and EduFlow scales are valid from a physiological standpoint, as well as to develop classification and predictive models based on artificial intelligence that will allow for educational performance improvement of students in future research.

In this study, we determine the liquid limit (𝑊𝑙), plasticity index (PI), and plastic limit (𝑊𝑝) of several natural fine-grained soil samples with the help of machine-learning and statistical methods. This enables us to locate each soil type analysed in the Casagrande plasticity chart with a single measure in pressure-membrane extractors. These machine-learning models showed adjustments in the determination of the liquid limit for design purposes when compared with standardised methods. Similar adjustments were achieved in the determination of the plasticity index, whereas the plastic limit determinations were applicable for control works. Because the best techniques were based in Multiple Linear Regression and Support Vector Machines Regression, they provide explainable plasticity models. In this sense, 𝑊𝑙=(9.94±4.2)+(2.25 ±0.3)∙𝑝F4.2, PI=(−20.47±5.6)+(1.48 ±0.3)∙𝑝F4.2+(0.21±0.1)∙𝐹 , and 𝑊𝑝=(23.32±3.5)+(0.60 ±0.2)∙𝑝F4.2−(0.13±0.04)∙𝐹 . So that, we propose an alternative, automatic, multi-sample, and static method to address current issues on Atterberg limits determination with standardised tests.