This article presents a double evaluation carried out in the subject Complements for disciplinary training II: Computing, corresponding to the Master's degree teacher training in secondary education, baccalaureate, vocational training and languages taught by the Universidad Rey Juan Carlos. The students of the subject had to learn how to prepare simple web pages, using HTML, CSS and JavaScript programming languages. To this end, the flipped classroom technique was used to present the necessary contents, combined with the adaptation of Aronson's cooperative learning puzzle technique, used to carry out a group practice that reflected the knowledge acquired. It is worth mentioning, as a complement to the two techniques used, the use of an adapted assessment rubric, which was provided to the students at the beginning of the teaching block. The evaluation was carried out during two consecutive academic years, 2018/2019 and 2019/2020. There were important differences between the two studies: in the first study, the students' previous self-assigned level was much higher (2.8 points as opposed to 1.4 points on a scale of 1 to 5). The other difference, even more relevant, was that in the second year all teaching was done at home, in a non-attendance format, on a mandatory basis, due to the period of confinement decreed by the state of alarm at that moment, because of the pandemic caused by the SARS-CoV-2 virus, popularly known as coronavirus. At the end of the experience, the students expressed their satisfaction with the learning acquired and with the tasks performed, in both cases. The techniques used were well-appreciated, in the first year more than in the second, and especially flipped classroom. The scores obtained were, in addition, always very relevant.

Patients suffering from Parkinson's Disease (PD) manifest relevant changes in their speech, consisting of specific landmarks in articulation, phonation, fluency, and prosody. Usually, phonation and articulation changes are estimated and evaluated using different methods and statistical frameworks. Speech is especially relevant as a vehicular mechanism to monitor neurological evolution using well-known features extracted from sustained phonations (mainly vowels), diadochokinetic exercises, or running speech. Recent studies have shown that acoustic neurostimulation using binaural beats influences the cognitive and neuromotor conditions of patients with PD, at least temporarily after stimulation. The aim of this study is to describe an added-value solution considering the cooperation of both previously mentioned methods: speech analysis-based monitoring called within the project, Monitoring Parkinson using Locution (MonParLoc), and acoustical neurostimulation, called within project neuro-Acoustic-stimulation Parkinson (AcousticPar). The applications designed in both projects are embedded into a global solution denominated Teca-Park which consists of four main activities: speech evaluation, neurostimulation, motor symptom longitudinally, and questionnaires. This framework is conceived to be a powerful tool for treating and monitoring longitudinally remotely and contact-free. MonParLoc was tested and validated in real scenarios involving patient associations. Validation results produced in these associations demonstrating the utility of this approach are given in the study, particularly in reference to protocol vulnerability and robustness. This paper proposes a complete framework (a mobile app and a scorecard solution) including different services for Parkinson's clinical monitoring and patient management using speech, movement, and acoustic stimulation.

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Parkinson's Disease (PD), contrary to other neurodegenerative diseases, supports certain treatments which can improve patients' conditions or at least mitigate disease effects. Treatments, either pharmacological, surgical or rehabilitative need longitudinal monitoring of patients to assess the progression or regression of thier condition, to optimize resources and benefits. As it is well known, PD leaves important marks in phonation, thus correlates obtained from spoken recordings taken at periodic intervals may be used in longitudinal monitoring of PD. The most preferred correlates are mel-cepstral coefficients, distortion features (jitter, shimmer, HNR, PPE, etc.), tremor indicators, or biomechanical coefficients. Feature templates estimated from each periodic evaluation have to be compared to establish potential progression or regression. The present work is devoted to propose a comparison framework based on Log Likelihood Ratios. This methodology shows to be very sensitive and allows a three-band based comparison: pre-treatment status vs post-treatment status in reference to a control subject or to a control population. Results from a database of eight male patients recorded weekly during a month are shown with comments regarding their severity condition. The conclusions derived show that several distortion, biomechanical and tremor features are quite relevant in monitoring PD phonation.