Improving a Mobile Learning Companion for Self-regulated Learning using Sensors

Haeseon Yun, Albrecht Fortenbacher, Niels Pinkwart

2017

Abstract

The ability to efficiently manage learning is linked to positive learning experience and outcome. However, attaining the ability of self-regulating is not a matter of course for students and it requires an external assistance. To support learners to be equipped with self-regulated learning skills, a mobile device can serve as an ideal learning companion which provides valuable feedback. A learning companion stemming from intelligent tutoring system (ITS) has non-authoritative, co-present effect as a learning support and with available sensor technology, self-regulated learning can be better promoted. Sensor enhanced learning companion can detect learning states, learning behaviours and context and provide valuable feedback to learners to increase their awareness of learning progress and to effectively manage their learning. Considering the mobility, autonomy of learners along together with current trend in open online learning resources and contents, available embedded sensors are suitable for realising the concept of learning companion for self-regulated learning. The paper reviews a self-regulated learning concept, a learning companion pedagogy and proposes that self-regulated learning skills can be promoted using sensor technology and a learning companion pedagogy.

References

  1. Azevedo, R. and Cromley, J. G. (2004). Does training on self-regulated learning facilitate students' learning with hypermedia? Journal of educational psychology, 96(3):523.
  2. Bandura, A. (1997). Self-efficacy: The exercise of control . Macmillan.
  3. Ben-Zeev, D., Scherer, E. A., Wang, R., Xie, H., and Campbell, A. T. (2015). Next-generation psychiatric assessment: Using smartphone sensors to monitor behavior and mental health. Psychiatric rehabilitation journal, 38(3):218.
  4. Chan, T.-W. and Baskin, A. B. (1988). Studying with the prince: The computer as a learning companion. In Proceedings of the International Conference on Intelligent Tutoring Systems, volume 194200.
  5. Chan, T.-W., Chung, I.-L., Ho, R.-G., Hou, W.-J., and Lin, G.-L. (1992). Distributed learning companion system: West revisited. In International Conference on Intelligent Tutoring Systems, pages 643-650. Springer.
  6. Chou, C.-Y., Chan, T.-W., and Lin, C.-J. (2003). Redefining the learning companion: the past, present, and future of educational agents. Computers & Education, 40(3):255-269.
  7. Dillenbourg, P. and Self, J. A. (1992). People power: A human-computer collaborative learning system. In International Conference on Intelligent Tutoring Systems, pages 651-660. Springer.
  8. Eurostate (2016). Digital economy and society statisticshouseholds and individuals.
  9. Fathi, A., Li, Y., and Rehg, J. M. (2012). Learning to recognize daily actions using gaze. In European Conference on Computer Vision, pages 314-327. Springer.
  10. Fernandez-Rio, J., Cecchini, J. A., Méndez-Gimenez, A., Mendez-Alonso, D., and Prieto, J. A. (2017). SelfRegulation, Cooperative Learning, and Academic Self-Efficacy: Interactions to Prevent School Failure. Frontiers in Psychology, 8.
  11. Goodman, B., Linton, F., and Gaimari, R. (2016). Encouraging student reflection and articulation using a learning companion: A commentary. International Journal of Artificial Intelligence in Education , 26(1):474-488.
  12. Greer, J. E., Mccalla, G., Collins, J. A., Kumar, V. S., Meagher, P., and Vassileva, J. (1998). Supporting peer help and collaboration in distributed workplace environments. International Journal of Artificial Intelligence in Education (IJAIED), 9:159-177.
  13. Guy, R., Byrne, B., and Dobos, M. (2017). Stop Think: a simple approach to encourage the self-assessment of learning. Advances in Physiology Education, 41(1):130-136.
  14. Haake, M. and Gulz, A. (2009). A look at the roles in embodied pedagogical agents-a user preference perspective. International Journal of Artificial Intelligence in Education, 19(1):39-71.
  15. Hase, S. and Kenyon, C. (2001). From andragogy to heutagogy. Ultibase articles, 5(3):1-10.
  16. Johnson, W. L., Rickel, J. W., Lester, J. C., et al. (2000). Animated pedagogical agents: Face-to-face interaction in interactive learning environments. International Journal of Artificial intelligence in education , 11(1):47- 78.
  17. Kern, N. and Schiele, B. (2003). Context-aware notification for wearable computing. In Proceedings of the 7th IEEE International Symposium on Wearable Computers (ISWC'03), pages 223-230.
  18. Kim, Y. (2007). Desirable characteristics of learning companions. International Journal of Artificial Intelligence in Education, 17(4):371-388.
  19. Kizilcec, R. F., Pérez-Sanagustín, M., and Maldonado, J. J. (2017). Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education, 104:18-33.
  20. Lee, J.-E. and Recker, M. (2017). Measuring Students' Use of Self-Regulated Learning Strategies from Learning Management System Data: An Evidence-Centered Design Approach About Analytics for Learning (A4L).
  21. Mandryk, R. L. and Atkins, M. S. (2007). A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. International journal of human-computer studies, 65(4):329- 347.
  22. Mega, C., Ronconi, L., and De Beni, R. (2014). What makes a good student? How emotions, self-regulated learning, and motivation contribute to academic achievement. Journal of Educational Psychology, 106(1):121-131.
  23. Miluzzo, E., Lane, N. D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S. B., Zheng, X., and Campbell, A. T. (2008). Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application. In Proceedings of the 6th ACM conference on Embedded network sensor systems, pages 337-350. ACM.
  24. PewResearchCenter (2017). Mobile fact sheet. Accessed: 2017-03-15.
  25. Pintrich, P. R. (2004). A conceptual framework for assessing motivation and self-regulated learning in college students. Educational psychlogy review, 16(4):385- 407.
  26. Prendinger, H. and Ishizuka, M. (2005). The empathic companion: A character-based interface that addresses users'affective states. Applied Artificial Intelligence , 19(3-4):267-285.
  27. Ryokai, K., Vaucelle, C., and Cassell, J. (2003). Virtual peers as partners in storytelling and literacy learning. Journal of computer assisted learning, 19(2):195- 208.
  28. Samsung (2016). Galaxy s7 edge and galaxy s7. Accessed: 2017-01-31.
  29. Schmidt, A., Aidoo, K. A., Takaluoma, A., Tuomela, U., Van Laerhoven, K., and Van de Velde, W. (1999). Advanced interaction in context. In International Symposium on Handheld and Ubiquitous Computing, pages 89-101. Springer.
  30. Sharma, K., Alavi, H. S., Jermann, P., and Dillenbourg, P. (2016). A gaze-based learning analytics model: invideo visual feedback to improve learner's attention in moocs. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pages 417-421. ACM.
  31. Siewiorek, D. P., Smailagic, A., Furukawa, J., Krause, A., Moraveji, N., Reiger, K., Shaffer, J., and Wong, F. L. (2003). Sensay: A context-aware mobile phone. In ISWC, volume 3, page 248.
  32. Traxler, J. and Kukulska-Julme, A. (2005). Mobile learning in developing countries.
  33. VanLehn, K., Zhang, L., Burlson, W., Girard, S., and Hidago-Pontet, Y. (2016). Can an non-cognitive learning companion increase the effectiveness of a metacognitive learning strategy? IEEE Transactions on Learning Technologies.
  34. Wellman, S. (2007). Google lays out its mobile user experience strategy. Information Week, April, 11.
  35. Wenger, E. C. (1987). Artificial intelligence and tutoring systems. Computational and Cognitive Approaches to the Communication of Knowledge. Morgan Kauffmann, Los Altos, San Francisco, CA USA.
  36. Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., and Picard, R. (2009). Affect-aware tutors: recognising and responding to student affect. International Journal of Learning Technology, 4(3-4):129-164.
  37. Zeidner, M. (1998). Test anxiety: The state of the art. Springer Science & Business Media.
  38. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory into practice, 41(2):64- 70.
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Paper Citation


in Harvard Style

Yun H., Fortenbacher A. and Pinkwart N. (2017). Improving a Mobile Learning Companion for Self-regulated Learning using Sensors . In Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU, ISBN 978-989-758-239-4, pages 531-536. DOI: 10.5220/0006375405310536


in Bibtex Style

@conference{csedu17,
author={Haeseon Yun and Albrecht Fortenbacher and Niels Pinkwart},
title={Improving a Mobile Learning Companion for Self-regulated Learning using Sensors},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,},
year={2017},
pages={531-536},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006375405310536},
isbn={978-989-758-239-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 1: CSEDU,
TI - Improving a Mobile Learning Companion for Self-regulated Learning using Sensors
SN - 978-989-758-239-4
AU - Yun H.
AU - Fortenbacher A.
AU - Pinkwart N.
PY - 2017
SP - 531
EP - 536
DO - 10.5220/0006375405310536