Early prediction of school dropout is a serious problem in education, but it is not an easy issue to resolve. On the one hand, there are many factors that can influence student retention. On the other hand, the traditional classification approach used to solve this problem normally has to be implemented at the end of the course to gather maximum information in order to achieve the highest accuracy. In this paper, we propose a methodology and a specific classification algorithm to discover comprehensible prediction models of student dropout as soon as possible. We used data gathered from 419 high schools students in Mexico. We carried out several experiments to predict dropout at different steps of the course, to select the best indicators of dropout and to compare our proposed algorithm versus some classical and imbalanced well‐known classification algorithms. Results show that our algorithm was capable of predicting student dropout within the first 4–6 weeks of the course and trustworthy enough to be used in an early warning system.

BackgroundThe methods used for the motor rehabilitation of patients with neurological disorders include a number of different rehabilitation exercises. For patients who have been diagnosed with multiple sclerosis (MS), the performance of motor rehabilitation exercises is essential. Nevertheless, this rehabilitation may be tedious, negatively influencing patients’ motivation and adherence to treatment.ObjectiveWe present RemoviEM, a system based on Kinect that uses virtual reality (VR) and natural user interfaces (NUI) to offer patients with MS an intuitive and motivating way to perform several motor rehabilitation exercises. It offers therapists a new motor rehabilitation tool for the rehabilitation process, providing feedback on the patient’s progress. Moreover, it is a low-cost system, a feature that can facilitate its integration in clinical rehabilitation centers.MethodsA randomized and controlled single blinded study was carried out to assess the influence of a Kinect-based virtual rehabilitation system on the balance rehabilitation of patients with MS. This study describes RemoviEM and evaluates its effectiveness compared to standard rehabilitation. To achieve this objective, a clinical trial was carried out. Eleven patients from a MS association participated in the clinical trial. The mean age was 44.82 (SD 10.44) and the mean time from diagnosis (years) was 9.77 (SD 10.40). Clinical effectiveness was evaluated using clinical balance scales.ResultsSignificant group-by-time interaction was detected in the scores of the Berg Balance Scale (P=.011) and the Anterior Reach Test in standing position (P=.011). Post-hoc analysis showed greater improvement in the experimental group for these variables than in the control group for these variables. The Suitability Evaluation Questionnaire (SEQ) showed good results in usability, acceptance, security, and safety for the evaluated system.ConclusionsThe results obtained suggest that RemoviEM represents a motivational and effective alternative to traditional motor rehabilitation for MS patients. These results have encouraged us to improve the system with new exercises, which are currently being developed.

El sistema educativo en tiempos de pandemia ha tenido que transformarse de forma urgente e imprevista a una modalidad virtual. En este trabajo se presenta un estudio exploratorio sobre las principales dificultades encontradas por las instituciones educativas en Iberoamérica y algunas estrategias utilizadas en los procesos de enseñanza aprendizaje. Asimismo, teniendo en cuenta el análisis previo, se propone un modelo de evaluación a considerar en los planes de contingencia debido a la emergencia sanitaria.

Our architecture showed correct performance and we realized that it could be introduced in other fields, apart from assistive technology. However, when being targeted to patients with dementia some usability problems appeared, such as difficulties to read information in a small screen or take a proper photo. These problems should be addressed in further research. Implications for Rehabilitation This article presents a prototypal assistive technology for Alzheimer's disease (AD) patients. It targets AD patients to recognize their familiars, especially in medium-advanced stages of the disease. Analysing pictures taken by a smart watch, which the patient carries, the person in front is recognized and information about him is sent to the watch. This technology enables patients to have all the information of any close person, as a remainder, easing their daily lives, improving their self-esteem and stimulating the patient with novel technology.

Due to the global pandemic (COVID-19) currently facing humanity, a new environment that promotes teaching–learning is now emerging. This environment that challenges traditional teaching practices created an opportunity for the technology industry to capitalize on by developing creative e-learning platforms that empowers the teaching–learning process, during this ‘emergency’ situation. E-learning scenario is an important element to be considered, as it offers a host of benefits, such as reducing costs, allowing for education on-demand, etc., to its adopters. However, the application of this could bring some challenges, as some of the existing online platforms are not conducive to support clear communication among academic staff. This paper describes a report identifying the main problems faced by teachers and students from different countries in Latin America, when using e-learning platforms in a lockdown scenario, reporting the importance to include aspects related with emotions and awareness.

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