Student response systems (SRSs) are becoming popular among instructors in nearly all levels of learning. The benefits of using SRSs in terms of attendance, attention, participation, or motivation have been shown in many studies. Moreover, several studies demonstrate that the use of some kind of gaming techniques in education are useful to stimulate students to learn in wider, longer, and deeper ways. This paper analyzes whether the integration of both SRSs and gaming techniques leads to better results in motivation, attention, engagement, and learning performance than SRSs alone. For this purpose, a new tool has been developed for conducting an experimental study with students from different subjects and from different academic levels. A randomized post-test-only control group analysis was conducted to evaluate the impact of the gamified SRS. In addition, a multivariate three-way factorial design has been performed considering three relevant factors: 1) learning tool (non-gamified SRS or gamified SRS); 2) study type; and 3) gender. The main finding is that students who took lecture sessions with a gamified SRS had more positive perceptions with respect to motivation, attention, and learning performance than students who took lecture sessions with a non-gamified SRS.

Recognizing human activity is very useful for an investigator about a patient's behavior and can aid in prescribing activity in future recommendations. The use of body worn accelerometers has been demonstrated to be an accurate measure of human activity; however, research looking at the use of multiple body worn accelerometers in a free living environment to recognize a wide range of activities is not evident. This paper aimed to successfully recognize activity and sub-category activity types through the use of multiple body worn accelerometers in a free-living environment. Ten participants (Age = 23.1 ± 1.7 years, height =171.0 ± 4.7 cm, and mass = 78.2 ± 12.5 Kg) wore nine body-worn accelerometers for a day of free living. Activity type was identified through the use of a wearable camera, and subcategory activities were quantified through a combination of free-living and controlled testing. A variety of machine learning techniques consisting of preprocessing algorithms, feature, and classifier selections were tested, accuracy, and computing time were reported. A fine k-nearest neighbor classifier with mean and standard deviation features of unfiltered data reported a recognition accuracy of 97.6%. Controlled and free-living testing provided highly accurate recognition for sub-category activities (>95.0%). Decision tree classifiers and maximum features demonstrated to have the lowest computing time. Results show that recognition of activity and sub-category activity types is possible in a free-living environment through the use of multiple body worn accelerometers. This method can aid in prescribing recommendations for activity and sedentary periods for healthy living.

This paper presents an in-depth empirical analysis of a nine-week MOOC. This analysis provides novel results regarding participants' profiles and use of built-in and external social tools. The results served to detect seven participants' patterns and conclude that the forum was the social tool preferred to contribute to the MOOC.

Selenium is often considered the de-facto standard framework for end-to-end web testing nowadays. It allows practitioners to drive web browsers (such as Chrome, Firefox, Edge, or Opera) in an automated fashion using different language bindings (such as Java, Python, or JavaScript, among others). The term ecosystem, referring to the open-source software domain, includes various components, tools, and other interrelated elements sharing the same technological background. This article presents a descriptive survey aimed to understand how the community uses Selenium and its ecosystem. This survey is structured in seven categories: Selenium foundations, test development, system under test, test infrastructure, other frameworks, community, and personal experience. In light of the current state of Selenium, we analyze future challenges and opportunities around it.