Context-aware support for computer-supported ubiquitous learning

By H. Ogata, Yoneo Yano. The 2nd IEEE International Workshop on Wireless and Mobile Technologies in Education, 2004. Proceedings., 2004

540 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/88c6029e3494e12ea08baa8a4062156a608beef4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gwo-Jen Hwang
2. Stephen J. H. Yang
3. Hendrik Drachsler
4. Nikos Manouselis
5. Chin-Chung Tsai
6. Tzu-Chi Yang
7. Katrien Verbert

In Computer Vision and Pattern Recognition

1. Gwo-Jen Hwang
2. Stephen J. H. Yang
3. Chin-Chung Tsai
4. Katrien Verbert
5. Tosti Hsu-Cheng Chiang

In Control and Systems Engineering

1. Gwo-Jen Hwang
2. Stephen J. H. Yang

Citations in the corpus

• An Augmented Reality-based Mobile Learning System to Improve Students' Learning Achievements and Motivations in Natural Science Inquiry Activities (2014, background): “Most of mobile learning studies emphasize the adoption of digital learning aids in real-life scenarios (Sharples, Milrad, Arnedillo-Sánchez, & Vavoula, 2009; Ogata & Yano, 2004; Wong & Looi, 2011).”
• Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective (2014, background): “For example, Ogata and Yano (2004) developed a context-aware ubiquitous learning system with GPS for guiding students to practice Japanese in the real world, while Hwang et al. (2012) developed a learning system with RFID for guiding students to operate scientific devices in a science park via…”
• Mobile Learning projects - a critical analysis of the state of the art (2009, unknown): “Name Context Tools Control Communication Subject Objective attaQ (Takenaka et al. 2006) Independent 2 1 3 1 1 BBC Bytesize (Thornton and Houser 2004) Independent 2 1 1 1 1 Code It (Goldman et al. 2004) Independent 3 2 3 1 3 DEEP (Traxler and Leach 2006) Independent 1 1 1 1 1 Eijiro (Morita 2003) Ind”
• Context-Aware Recommender Systems for Learning: A Survey and Future Challenges (2012, methodology): “For example, CALS [123] requests such information from the learner, whereas TANGO [76] relies on teachers to set knowledge levels indicators after exam-”
• A context-aware ubiquitous learning environment for conducting complex science experiments (2009, background): “In recent years, several studies have reported the benefits of applying the context-aware u-learning approach, including the promotion of learning motivation (Chu et al., 2008; Ogata and Yano, 2004) and the improvement of learning effectiveness (El-Bishouty et al.”