Abstract—¬Supporting diverse and rapidly changing learning styles of new digital age generations is one of the major hurdles to higher education in the age of massification of education markets. Higher education institutions must now utilize unprecedented network speed and mobile technology to create stimulating learning environments for new digital age generations. This paper presents a new learning and teaching model that combines dynamic learning space (DLS) and mobile collaborative experimental learning (MCEL) for supporting diverse learning styles of students. DLS assists students with state-of-art modern wireless network technologies in order to support fast-paced, multi-tasking, data and content intensive collaborative learning in class. The model further extends student learning activities beyond classroom by allowing students to continue their learning anywhere and anytime conveniently using their mobile devices. MCEL provides automated continuous personalized formative-feedback 24/7. The main objectives of the model are to improve student engagement and to provide ownership of their learning journey, experiential learning, contextualized learning, and formative assessment at low cost. The model employs three factors that influence collaborative experiential learning and formative assessment. The three factors are: (1) the use of learning space within the classroom, (2) wireless learning technology, and (3) mobile learning system (m-Learning). Pilot studies of the model are conducted and evaluated on two groups of postgraduate students. Their participation is observed, and a survey is conducted. The results show that (1) DLS encourages high-level learning and diverse learning styles to move away from passive low-level knowledge intensive learning activities; (2) MCEL supports Bigg's constructive alignment in curriculum design, contextualized experimental learning, and personalized formative learning.
A health social network is an online information service which facilitates information sharing between closely related members of a community with the same or a similar health condition. Over the years, many automated recommender systems have been developed for social networking in order to help users find their communities of interest. For health social networking, the ideal source of information for measuring similarities of patients is the medical information of the patients. However, it is not desirable that such sensitive and private information be shared over the Internet. This is also true for many other security sensitive domains. A new information-sharing scheme is developed where each patient is represented as a small number of (possibly disjoint) d-words (discriminant words) and the d-words are used to measure similarities between patients without revealing sensitive personal information. The d-words are simple words like "food,'' and thus do not contain identifiable personal information. This makes our method an effective one-way hashing of patient assessments for a similarity measure. The d-words can be easily shared on the Internet to find peers who might have similar health conditions.
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