Experiences with RFID-based interactive learning in museums

被引:0
作者
Huang Y.-P. [1 ]
Chang Y.-T. [2 ]
Sandnes F.E. [3 ]
机构
[1] Department of Electrical Engineering, National Taipei University of Technology, Taipei
[2] Department of Computer Science and Engineering, Tatung University, Taipei
[3] Faculty of Engineering, Oslo University College, 0130 Oslo, St. Olavs Plass
关键词
Collaborative filtering; Data mining; Digital learning; Guide system; Learning assistant service; RFID;
D O I
10.1504/IJAACS.2010.030312
中图分类号
学科分类号
摘要
Tourism plays an important role in the economies of many countries. Tourism can secure employment, foreign exchange earnings, investment and regional development. To attract more tourists and local visitors, many stakeholders such as natural parks, museums, art galleries, hotels and restaurants provide personalised services to meet individual needs. With the increasing number of tourists comes an increased demand for guides at education-oriented leisure centers. Each provided needs unique way to present their services. In this study, these educational leisure centres are coarsely divided into art and science. This paper introduces the architecture of the proposed guide system including a PDA-based recommendation guide for art museums and an Radiofrequency identification-based interactive learning system using collaborative filtering technology for science and engineering education. Evaluations of the two systems reveal that the system inspires and nurtures visitors' interest in science and arts. Copyright © 2010 Inderscience Enterprises Ltd.
引用
收藏
页码:59 / 74
页数:15
相关论文
共 25 条
  • [1] Bahl Paramvir, Padmanabhan Venkata N., RADAR: An in-building RF-based user location and tracking system, Proceedings - IEEE INFOCOM, 2, pp. 775-784, (2000)
  • [2] Bellotti F., Berta R., De Gloria A., Margarone M., User testing a hypermedia tour guide, IEEE Pervasive Computing, 1, 2, pp. 33-41, (2002)
  • [3] Billsus D., Pazzani M.J., Learning collaborative information filters, The Proceedings of the International Conference on Machine Learning, pp. 46-54, (1998)
  • [4] Brown P.J., Bovey J.D., Chen X., Context-aware applications: From the laboratory to the marketplace, IEEE Personal Communications, 4, 5, pp. 58-64, (1997)
  • [5] Chen T., Han W.-L., Wang H.-D., Zhou Y.-X., Xu B., Zang B.-Y., Content recommendation system based on private dynamic user profile, The Proceedings of the International Conference on Machine Learning and Cybernetics, 4, pp. 2112-2118, (2007)
  • [6] Claypool M., Gokhale A., Miranda T., Murnikov P., Netes D., Sartin M., Combining content-based and collaborative filters in an online newspaper, The Proceedings of the ACM SIGIR Workshop on Recommender Systems, (1999)
  • [7] Davies N., Cheverst K., Mitchell K., Efrat A., Using and determining location in a context-sensitive tour guide, Computer, 34, 8, pp. 35-41, (2001)
  • [8] Derntl M., Hummel K.A., Modeling context-aware e-learning scenarios, The Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 337-342, (2005)
  • [9] Facer K., Joiner R., Stanton D., Reid J., Hull R., Kirk D., Savannah: Mobile 75 game and learning, Journal of Computer Assisted Learning, 20, pp. 399-409, (2004)
  • [10] Furukawa M., Watanabe M., Kinoshita M., Kakazu Y., A mathematical model for learning agents on a multi-agent system, The Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, 3, pp. 1369-1374, (2003)