An Affective Smart Environment for Personalized Learning and Teaching
Royal Society International Exchanges

Royal Society International Exchanges
Funding Source: The Royal Society, UK
Reference Number: IE160609
Value: £11,841
2017 - 2019

About the Project

  • Researched facial analysis with deep learning, facilitating autonomous student learning support in developing countries.
  • Supported a collaborative research project with Dr Matangini Chattopadhyay from Jadavpur University, India.

People



Organizations


    YouTube


    Publications

    CCESK: A Chinese Character Educational System Based on Kinect
    CCESK: A Chinese Character Educational System Based on Kinect Impact Factor: 2.9Top 25% Journal in Education & Educational Research
    IEEE Transactions on Learning Technologies (TLT), 2018
    Yang Yang, Howard Leung, Hubert P. H. Shum, Jiao Li, Lanling Zeng, Nauman Aslam and Zhigeng Pan
    Webpage Cite This Plain Text
    Yang Yang, Howard Leung, Hubert P. H. Shum, Jiao Li, Lanling Zeng, Nauman Aslam and Zhigeng Pan, "CCESK: A Chinese Character Educational System Based on Kinect," IEEE Transactions on Learning Technologies, vol. 11, no. 3, pp. 342-347, IEEE, 2018.
    Bibtex
    @article{yang18ccesk,
     author={Yang, Yang and Leung, Howard and Shum, Hubert P. H. and Li, Jiao and Zeng, Lanling and Aslam, Nauman and Pan, Zhigeng},
     journal={IEEE Transactions on Learning Technologies},
     title={CCESK: A Chinese Character Educational System Based on Kinect},
     year={2018},
     volume={11},
     number={3},
     pages={342--347},
     numpages={6},
     doi={10.1109/TLT.2017.2723888},
     issn={1939-1382},
     publisher={IEEE},
    }
    RIS
    TY  - JOUR
    AU  - Yang, Yang
    AU  - Leung, Howard
    AU  - Shum, Hubert P. H.
    AU  - Li, Jiao
    AU  - Zeng, Lanling
    AU  - Aslam, Nauman
    AU  - Pan, Zhigeng
    T2  - IEEE Transactions on Learning Technologies
    TI  - CCESK: A Chinese Character Educational System Based on Kinect
    PY  - 2018
    VL  - 11
    IS  - 3
    SP  - 342
    EP  - 347
    DO  - 10.1109/TLT.2017.2723888
    SN  - 1939-1382
    PB  - IEEE
    ER  - 
    Paper Supplementary Material
    A Dual-Stream Recurrent Neural Network for Student Feedback Prediction using Kinect
    A Dual-Stream Recurrent Neural Network for Student Feedback Prediction using Kinect
    Proceedings of the 2018 International Conference on Software, Knowledge, Information Management and Applications (SKIMA), 2018
    Shanfeng Hu, Hindol Bhattacharya, Matangini Chattopadhyay, Nauman Aslam and Hubert P. H. Shum
    Webpage Cite This Plain Text
    Shanfeng Hu, Hindol Bhattacharya, Matangini Chattopadhyay, Nauman Aslam and Hubert P. H. Shum, "A Dual-Stream Recurrent Neural Network for Student Feedback Prediction using Kinect," in SKIMA '18: Proceedings of the 2018 International Conference on Software, Knowledge, Information Management and Applications, pp. 1-8, Phnom Penh, Cambodia, IEEE, Dec 2018.
    Bibtex
    @inproceedings{hu18dualstream,
     author={Hu, Shanfeng and Bhattacharya, Hindol and Chattopadhyay, Matangini and Aslam, Nauman and Shum, Hubert P. H.},
     booktitle={Proceedings of the 2018 International Conference on Software, Knowledge, Information Management and Applications},
     series={SKIMA '18},
     title={A Dual-Stream Recurrent Neural Network for Student Feedback Prediction using Kinect},
     year={2018},
     month={12},
     pages={1--8},
     numpages={8},
     doi={10.1109/SKIMA.2018.8631537},
     issn={2573-3214},
     publisher={IEEE},
     location={Phnom Penh, Cambodia},
    }
    RIS
    TY  - CONF
    AU  - Hu, Shanfeng
    AU  - Bhattacharya, Hindol
    AU  - Chattopadhyay, Matangini
    AU  - Aslam, Nauman
    AU  - Shum, Hubert P. H.
    T2  - Proceedings of the 2018 International Conference on Software, Knowledge, Information Management and Applications
    TI  - A Dual-Stream Recurrent Neural Network for Student Feedback Prediction using Kinect
    PY  - 2018
    Y1  - 12 2018
    SP  - 1
    EP  - 8
    DO  - 10.1109/SKIMA.2018.8631537
    SN  - 2573-3214
    PB  - IEEE
    ER  - 
    Paper

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