Cozmo is a real-life robot designed to interact with people playing games, making sounds, expressing emotions on a LCD screen and many other pre-programmable functions. We present the development and implementation of an educational platform for Cozmo mobile robot, with several features, including web server for user interface, computer vision, voice recognition, robot trajectory tracking control, among others. Functions for educational purposes were implemented, including mathematical operations, spelling, directions, and questions functions that gives more flexibility for the teachers to create their own scripts. In this system, a cloud voice recognition tool was implemented to improve the interactive system between Cozmo and the users. Also, a cloud computing vision system was used to perform object recognition using Cozmo's camera, to be applied on educational games. Other functions were created with the purpose of controlling the emotions and the motors of Cozmo to create more sophisticated scripts. To apply the functions on Cozmo robot, an interpreter algorithm was developed to translate the functions into Cozmo's programming language. To validate this work, the proposed framework was presented to several elementary school teachers (classes with students between 4 and 12). Students and teacher's impressions are reported in this text, and indicate that the proposed system can be a useful educational tool.
BACKGROUND: The number of mobile apps that support smoking cessation is growing, indicating the potential of the mobile phone as a means to support cessation. Knowledge about the potential end users for cessation apps results in suggestions to target potential user groups in a dissemination strategy, leading to a possible increase in the satisfaction and adherence of cessation apps.OBJECTIVE: This study aimed to characterize potential end users for a specific mobile health (mHealth) smoking cessation app.METHODS: A quantitative study was conducted among 955 Dutch smokers and ex-smokers. The respondents were primarily recruited from addiction care facilities and hospitals through Web-based media via websites and forums. The respondents were surveyed on their demographics, smoking behavior, and personal innovativeness. The intention to use and the attitude toward a cessation app were determined on a 5-point Likert scale. To study the association between the characteristics and intention to use and attitude, univariate and multivariate ordinal logistic regression analyses were performed.RESULTS: The multivariate ordinal logistic regression showed that the number of previous quit attempts (odds ratio [OR] 4.1, 95% CI 2.4-7.0, and OR 3.5, 95% CI 2.0-5.9) and the score on the Fagerstrom Test of Nicotine Dependence (OR 0.8, 95% CI 0.8-0.9, and OR 0.8, 95% CI 0.8-0.9) positively correlates with the intention to use a cessation app and the attitude toward cessation apps, respectively. Personal innovativeness also positively correlates with the intention to use (OR 0.3, 95% CI 0.2-0.4) and the attitude towards (OR 0.2, 95% CI 0.1-0.4) a cessation app. No associations between demographics and the intention to use or the attitude toward using a cessation app were observed.CONCLUSIONS: This study is among the first to show that demographic characteristics such as age and level of education are not associated with the intention to use and the attitude toward using a cessation app when characteristics related specifically to the app, such as nicotine dependency and the number of quit attempts, are present in a multivariate regression model. This study shows that the use of mHealth apps depends on characteristics related to the content of the app rather than general user characteristics.
Als relatief nieuw begrip in de context van e-learning krijgt ‘mobile learning’ steeds meer aandacht, wat ten dele kan worden verklaard door de ontwikkeling en verspreiding van mobiele technologie. Als we de pleitbezorgers van ‘mobile learning’ moeten geloven, dan wordt deze vorm van leren belangrijker en is het denkbaar dat sommige leerprocessen in de toekomst volledig op die wijze vormgegeven zullen worden. Probleem is dat een eenduidige definitie van ‘mobile learning’ nog altijd ontbreekt, dat er meningsverschillen zijn over de technologie die tot het domein van ‘mobile learning’ behoort, en dat er betrekkelijk weinig resultaten zijn van succesvolle inzet van mobiele technologie in leerprocessen. Daarbij wordt onder succesvol verstaan dat het heeft bijgedragen aan de effectiviteit van het leren, en daarmee aan een beter leerresultaat en een efficiënter leerproces, waarbij onder het laatste verstaan wordt dat het maximale leereffect wordt bereikt met een beperkte inzet van mensen en middelen. Deze notitie beoogt enige duidelijkheid te scheppen in de definitiekwestie en in de visies op leren die een rol spelen bij ‘mobile learning’. Vanuit dat perspectief wordt vervolgens ingegaan op kenmerken van mobiele technologie en ontwikkelingen die daarin verwacht worden. Aansluitend wordt er dieper ingegaan op leerprocessen en de rol die mobiele technologie daarin zou kunnen vervullen, waarna de notitie wordt afgesloten met een kijkkader om de mogelijke inzet en betekenis van ‘mobile learning’ in onderwijssituaties te kunnen duiden en beoordelen.
