ID3AS is a programme in the field of sensor technology to stimulate innovation and network creation in the Eems Dollard Region (EDR), the most northern region along the Dutch-German border. The ID3AS-programme provided an opportunity for over 80 students with different backgrounds to participate on a scala of real world challenges. Real world learning environments like these are becoming increasingly popular in education, so it is important that we know how to organise the participation of students and tutors effectively.However, in ID3AS it proved challenging to realise a fruitful learning experience for the students, while simultaneously adding real value to the projects. The difficulty stems from the fact that both students and tutors struggle with the inherent unclarity of innovation projects, while at the same time industry partners need actual results. We think that the currently prevailing approach of the student learning by discovery, with the tutor in the role of process supervisor, is suboptimal in these conditions. Based on our experiences we propose to have students join a consortium as an 'apprentice’ to a ‘master’. The master, being a tutor from either university or company, should be comfortable with leading by example in an uncertain environment where both learning outcomes and concrete results are expected. We present several examples where this approach worked and give the outline of an experiment we plan to conduct on this topic.
BACKGROUND: Approximately 5%-10% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem.OBJECTIVE: This study examines whether sensor-augmented toys can be used to assess children's fine motor skills. The objectives were to (1) predict the outcome of the fine motor skill part of the Movement Assessment Battery for Children Second Edition (fine MABC-2) and (2) study the influence of the classification model, game, type of data, and level of difficulty of the game on the prediction.METHODS: Children in elementary school (n=95, age 7.8 [SD 0.7] years) performed the fine MABC-2 and played 2 games with a sensor-augmented toy called "Futuro Cube." The game "roadrunner" focused on speed while the game "maze" focused on precision. Each game had several levels of difficulty. While playing, both sensor and game data were collected. Four supervised machine learning classifiers were trained with these data to predict the fine MABC-2 outcome: k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM). First, we compared the performances of the games and classifiers. Subsequently, we compared the levels of difficulty and types of data for the classifier and game that performed best on accuracy and F1 score. For all statistical tests, we used α=.05.RESULTS: The highest achieved mean accuracy (0.76) was achieved with the DT classifier that was trained on both sensor and game data obtained from playing the easiest and the hardest level of the roadrunner game. Significant differences in performance were found in the accuracy scores between data obtained from the roadrunner and maze games (DT, P=.03; KNN, P=.01; LR, P=.02; SVM, P=.04). No significant differences in performance were found in the accuracy scores between the best performing classifier and the other 3 classifiers for both the roadrunner game (DT vs KNN, P=.42; DT vs LR, P=.35; DT vs SVM, P=.08) and the maze game (DT vs KNN, P=.15; DT vs LR, P=.62; DT vs SVM, P=.26). The accuracy of only the best performing level of difficulty (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was significantly better than the combination of the easiest and middle level (P=.046).CONCLUSIONS: The results of our study show that sensor-augmented toys can efficiently predict the fine MABC-2 scores for children in elementary school. Selecting the game type (focusing on speed or precision) and data type (sensor or game data) is more important for determining the performance than selecting the machine learning classifier or level of difficulty.
Technology is becoming omnipresent in public spaces: from CCTV cameras to smart phones, and from large public displays to RFID enabled travel cards. Although such technology comes with great potential, it also comes with apparent (privacy) threats and acceptance issues. Our research focuses on realizing technologyenhanced public spaces in a way that is acceptable and useful for the public. This paper gives a brief overview of the research that is aimed to unlock the positive potential of public spaces. This paper’s main focus is on the acceptance of sensor technology in the realm of tourism. The ITour project which investigates the potential and acceptance of using (sensor) technology and ambient media to collect, uncover and interpret data regarding tourists’ movements, behavior and experiences in the city of Amsterdam is particularly discussed as an example.
