Purpose This study aimed to develop and evaluate the feasibility and preliminary efficiency of a methodology to measure the mindfulness state using a wearable device (“Cap”) capable of monitoring students’ levels of full attention by means of real-time measured heart rate variability (HRV). Methods The device was developed to export the data to the user’s smartphone via Bluetooth, which in turn stores the securely accessible data in the cloud. The autonomous wearable device consists of electronic boards of the Arduino platform that detect the period in milliseconds between two subsequent referential R peaks of the QRS complex wave through infrared oxygenation sensor. Results In a population of 13 subjects (8 female, 5 male, age 16.1 years ), the Z-test ( ) using rMSSD (root mean squared successive differences) and the Toronto Mindfulness (Curiosity) Scale within two 50 min windows, shows that increased HRV values converge to high values for the mindfulness state when the time difference between and samples is greater than 88 ms. Conclusion The device proved to be viable and potentially effective for measuring the state of mindfulness. Thus, further studies should be conducted to test it on a large scale as well as in real classroom situations.

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Abstract. With the growth of online courses and, usage of mobile access allowing students execute educational activities in multiple locales, with variety of data and media content, new perspectives of educational support using different computing models can be observed. Some of most recent evolved computing models stand out in areas like Social Networks Analysis, Artificial Intelligence, Mobile Computing and Context-Aware Computing. Understanding the combination of these computing areas as complementary researches, this work investigates the applications of these computing technics to modeling an intelligent computational engine with educational personalization purposes. In this resume of a research in progress, a reduced implementation prepared as proof of concept simulating aspects of the target model, operates as centralized adaptive engine. The implemented engine, applied Artificial Neural Networks on classification tasks and routing recommendation. A group of 27 students participated in an experiment interacting with the adaptive engine using a mobile application provided. The mobile application allowed tracking of interface during usage flow by students, and provided to students the adaptive engine recommendation results. Around 59% of students confirmed the recommendation effectiveness of adaptive engine. In this experiment, at the end of each participation, students sent feedbacks about application features. The current results indicate the viability of computational model related to automation of classification tasks to environment identification and activity routing recommendation. In brief, the initial experiment presented encouraging results, indicating that the continuity of research could result in a useful tool to online educational platforms.

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