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Exploring Arduino for Building Educational Context-Aware Recommender Systems that Deliver Affective Recommendations in Social Ubiquitous Networking Environments

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Web-Age Information Management (WAIM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8597))

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Abstract

One of the most challenging context features to detect when making recommendations in educational scenarios is the learner’s affective state. Usually, this feature is explicitly gathered from the learner herself through questionnaires or self-reports. In this paper, we analyze if affective recommendations can be produced with a low cost approach using the open source electronics prototyping platform Arduino together with corresponding sensors and actuators. TORMES methodology (which combines user centered design methods and data mining techniques) can support the recommendations elicitation process by identifying new recommendation opportunities in these emerging social ubiquitous networking scenarios.

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Notes

  1. 1.

    http://arduino.cc/

  2. 2.

    http://arduino.cc/en/Guide/ArduinoEsplora

  3. 3.

    http://www.seeedstudio.com/wiki/GROVE_System

  4. 4.

    http://medicarduino.net/

  5. 5.

    http://lilypadarduino.org

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Acknowledgements

Authors would like to thank Spanish Ministry of Economy and Competence for funding MAMIPEC project (TIN2011-29221-C03-01).

Author information

Authors and Affiliations

  1. aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, C/Juan del Rosal 16, 28040, Madrid, Spain

    Olga C. Santos & Jesus G. Boticario

Authors
  1. Olga C. Santos
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  2. Jesus G. Boticario
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Corresponding author

Correspondence to Olga C. Santos .

Editor information

Editors and Affiliations

  1. DEKE Lab., Renmin University of China, Beijing, China

    Yueguo Chen

  2. Institute for Information Systems, Technical University Braunschweig, Braunschweig, Germany

    Wolf-Tilo Balke

  3. Hong Kong Baptist University Dept. Computer Science, Kowloon Tong, Hong Kong SAR

    Jianliang Xu

  4. School of Information, Renmin University of China, Beijing, China

    Wei Xu

  5. School of Computer Science and Technology, Hefei, China

    Peiquan Jin

  6. Department of Computer Science, East China Normal University, Shanghai, China

    Xin Lin

  7. Department of Computer Science, Kean University, Wenzhou, China

    Tiffany Tang

  8. School of Electrical Engineering, Korea University, Seoul, Korea, Republic of (South Korea)

    Eenjun Hwang

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Santos, O.C., Boticario, J.G. (2014). Exploring Arduino for Building Educational Context-Aware Recommender Systems that Deliver Affective Recommendations in Social Ubiquitous Networking Environments. In: Chen, Y., et al. Web-Age Information Management. WAIM 2014. Lecture Notes in Computer Science(), vol 8597. Springer, Cham. https://doi.org/10.1007/978-3-319-11538-2_25

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  • DOI: https://doi.org/10.1007/978-3-319-11538-2_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11537-5

  • Online ISBN: 978-3-319-11538-2

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