Skip to main content

Towards the Use of Personal Robots to Improve the Online Learning Experience

  • 643 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 158)


All changes are difficult and moving from face-to-face to online learning is not an exception. Nowadays, online students have many supports to ease their learning process due to the evolution of Virtual Learning Environments (VLE), the maturity of the pedagogical models used, and the vast experience of online teachers who design, create and deploy successful learning activities and accompany students through these activities. However, these supports are mainly centralized within the contexts of the VLE or the virtual classrooms. Therefore, new online learners should get the necessary habits to enter the VLE and the classrooms frequently. In this research we present an ongoing study in which robots are used as personalized companions of new students. Robots provide personal feedback to each student with the aim of promoting behavioral changes that facilitate the learning experience of new students and potentially reduce their dropout.


  • Assistive robot
  • Persuasive technology
  • Motivation
  • Learning experience

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. 1.


  1. Spolaôr, N., Benitti, F.B.V.: Robotics applications grounded in learning theories on tertiary education: a systematic review. Comput. Educ. 112, 97–107 (2017)

    CrossRef  Google Scholar 

  2. Sangrà, A.: A new learning model for the information and knowledge society: the case of the UOC. Int. Rev. Res. Open Distance Learn. 2(2), 152–167 (2002)

    CrossRef  Google Scholar 

  3. Hiemstra, R.: Self-Directed Learning. IACE Hall of Fame Repository (1994)

    Google Scholar 

  4. Grau-Valldosera, J., Minguillón, J.: Redefining dropping out in online higher education: a case study from the UOC. In: Proceedings of the 1st International Conference on Learning Analytics and Knowledge, pp. 75–80 (2011)

    Google Scholar 

  5. Clark, F., Sanders, K., Carlson, M., Blanche, E., Jackson, J.: Synthesis of habit theory. OTJR Occup. Particip. Heal. 27(1_suppl), 7S-23S (2007)

    CrossRef  Google Scholar 

  6. Andrews, B.R.: Habit. Am. J. Psychol. 14(2), 121–149 (1903)

    CrossRef  Google Scholar 

  7. Ryan, R.M., Deci, E.L.: Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55(1), 68 (2000)

    CrossRef  Google Scholar 

  8. Birnbaum, G.E., Mizrahi, M., Hoffman, G., Reis, H.T., Finkel, E.J., Sass, O.: What robots can teach us about intimacy: the reassuring effects of robot responsiveness to human disclosure. Comput. Human Behav. 63, 416–423 (2016)

    CrossRef  Google Scholar 

  9. Oinas-Kukkonen, H., Harjumaa, M.: Persuasive systems design: key issues, process model, and system features. Commun. Assoc. Inf. Syst. 24(1), 28 (2009)

    Google Scholar 

  10. Fogg, B.: A behavior model for persuasive design. In: ACM International Conference Proceeding Series, vol. 350 (2009)

    Google Scholar 

  11. Ham, J., Cuijpers, R.H., Cabibihan, J.-J.: Combining robotic persuasive strategies: the persuasive power of a storytelling robot that uses gazing and gestures. Int. J. Soc. Robot. 7(4), 479–487 (2015)

    CrossRef  Google Scholar 

  12. Minguillón, J., Conesa, J., Rodríguez, M.E., Santanach, F.: Learning analytics in practice: providing E-learning researchers and practitioners with activity data. In: Frontiers of Cyberlearning, pp. 145–167. Springer, Singapore (2018)

    Google Scholar 

  13. Chidambaram, V., Chiang, Y.-H., Mutlu, B.: Designing persuasive robots: how robots might persuade people using vocal and nonverbal cues. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, pp. 293–300 (2012)

    Google Scholar 

Download references


This work has been partially supported by the eLearn Center of the UOC through the project titled “Botter: a personal robot for novel UOC students” and by European Commission through the project “colMOOC: Integrating Conversational Agents and Learning Analytics in MOOCs” (588438-EPP-1-2017-1-EL-EPPKA2-KA). This research has also been supported by Seidor Labs department of Seidor company, who, as a UOC’s technology provider, implemented the robots and the interaction between them and the UOC campus.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Jordi Conesa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Conesa, J. et al. (2021). Towards the Use of Personal Robots to Improve the Online Learning Experience. In: Barolli, L., Takizawa, M., Yoshihisa, T., Amato, F., Ikeda, M. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2020. Lecture Notes in Networks and Systems, vol 158. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61104-0

  • Online ISBN: 978-3-030-61105-7

  • eBook Packages: EngineeringEngineering (R0)