Skip to main content
SpringerLink
Log in

Non-distracting Feedback in Artificial Intelligence Supported Learning

  • Chapter
  • First Online:
Book cover Artificial Intelligence Supported Educational Technologies

Part of the book series: Advances in Analytics for Learning and Teaching ((AALT))

  • 946 Accesses

Abstract

The introduction of sensory information into computer-supported learning results in a manifold of available data to be collected. In this publication we investigate how this “more in data” does not inevitably lead to a “more in distraction” at the interface. This becomes possible by analyzing intentional and non-intentional actions of the user or the environmental conditions to detect – using artificial intelligence – for instance, if the learner is unconcentrated, tired, or over-challenged. Relying on this analysis within a learning session, a user model can therefore be dynamically updated. This offers the possibility for a direct feedback loop to reflect the learner’s status, progress, etc. but also raises novel challenges that need to be addressed, namely, when to intervene, how much feedback needs to be provided, and how to represent the required information in order to reduce the distraction and thus to improve learning success.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

References

  • Ackerman, D. S., & Gross, B. L. (2010). Instructor feedback: How much do students really want? Journal of Marketing Education, 32(2), 172–181.

    Article  Google Scholar 

  • Addas, S., & Pinsonneault, A. (2015). The many faces of information technology interruptions: A taxonomy and preliminary investigation of their performance effects. Information Systems Journal, 25(3), 231–273.

    Article  Google Scholar 

  • Baharudin, A. F., Sahabudin, N. A., & Kamaludin, A. (2017). Behavioral tracking in E-learning by using learning styles approach. Indonesian Journal of Electrical Engineering and Computer Science, 8(1), 17–26.

    Article  Google Scholar 

  • Bouchet, F., Harley, J. M., & Azevedo, R. (2016). Can adaptive pedagogical agents’ prompting strategies improve students’ learning and self-regulation? In A. Micarelli, J. Stamper, & K. Panourgia (Eds.), Lecture notes in computer science (Intelligent tutoring systems) (Vol. 9684, pp. 368–374). Cham: Springer.

    Google Scholar 

  • Brünken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38, 53–61.

    Article  Google Scholar 

  • Csikszentmihalyi, M. (1997). Flow and the psychology of discovery and invention (Vol. 39). New York: Harper Perennial.

    Google Scholar 

  • D’Mello, S., Olney, A., Williams, C., & Hays, P. (2012). Gaze tutor: A gaze-reactive intelligent tutoring system. International Journal of Human-Computer Studies, 70(5), 377–398.

    Article  Google Scholar 

  • De Pessemier, T., De Moor, K., Joseph, W., De Marez, L., & Martens, L. (2012). Quantifying the influence of rebuffering interruptions on the user’s quality of experience during mobile video watching. IEEE Transactions on Broadcasting, 59(1), 47–61.

    Article  Google Scholar 

  • Dihoff, R. E., Brosvic, G. M., Epstein, M. L., & Cook, M. J. (2004). Provision of feedback during preparation for academic testing: Learning is enhanced by immediate but not delayed feedback. The Psychological Record, 54(2), 207–231.

    Article  Google Scholar 

  • Han, X., Ugail, H., & Palmer, I. (2009). Gender classification based on 3D face geometry features using SVM. In 2009 international conference on cyberworlds (pp. 114–118). New York: IEEE.

    Google Scholar 

  • Harley, J. M., Lajoie, S. P., Frasson, C., & Hall, N. C. (2017). Developing emotion-aware, advanced learning technologies: A taxonomy of approaches and features. International Journal of Artificial Intelligence in Education, 27(2), 268–297.

    Article  Google Scholar 

  • Hays, M. J., Kornell, N., & Bjork, R. A. (2010). Psychonomic Bulletin & Review, 17, 797. https://doi.org/10.3758/PBR.17.6.797

    Article  Google Scholar 

  • Hsiao, H. S., & Chen, J. C. (2016). Using a gesture interactive game-based learning approach to improve preschool children’s learning performance and motor skills. Computers & Education, 95, 151–162. Csikszentmihalyi, M. (1997).

    Article  Google Scholar 

  • Hwang, G. J. (2014). Definition, framework and research issues of smart learning environments-a context-aware ubiquitous learning perspective. Smart Learning Environments, 1(1), 4.

    Article  Google Scholar 

  • Hwang, J.-P., Wu, T.-T., Lai, F.-J., & Huang, Y.-M. (2011). A sensor-assisted model for estimating the accuracy of learning retention in computer classroom. In 2011 fifth international conference on sensing technology (ICST) (pp. 650–654).

    Google Scholar 

  • Ifenthaler, D. (2015). Learning analytics. In J. M. Spector (Ed.), The sage encyclopedia of educational technology (Vol. 2, pp. 447–451). Los Angeles: Sage Publications.

    Google Scholar 

  • Khan, I., & Pardo, A. (2016, April). Data2U: Scalable real time student feedback in active learning environments. In Proceedings of the sixth international conference on learning analytics & knowledge (pp. 249–253). New York: ACM.

    Google Scholar 

  • Kolb, K. J., & Aiello, J. R. (1996). The effects of electronic performance monitoring on stress: Locus of control as a moderator variable. Computers in Human Behavior, 12(3), 407–423.

    Article  Google Scholar 

  • Lai, M. L., Tsai, M. J., Yang, F. Y., Hsu, C. Y., Liu, T. C., Lee, S. W. Y., et al. (2013). A review of using eye-tracking technology in exploring learning from 2000 to 2012. Educational Research Review, 10, 90–115.

    Article  Google Scholar 

  • Lallé, S., Toker, D., Conati, C., & Carenini, G. (2015). Prediction of users’ learning curves for adaptation while using an information visualization. In Proceedings of the 20th international conference on intelligent user interfaces (IUI’15) (pp. 357–368). New York: ACM. https://doi.org/10.1145/2678025.2701376

    Chapter  Google Scholar 

  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436.

    Article  Google Scholar 

  • Liu, M. C., Lai, C. H., Su, Y. N., Huang, S. H., Chien, Y. C., Huang, Y. M., et al. (2015). Learning with great care: The adoption of the multi-sensor technology in education. In Sensing technology: Current status and future trends III (pp. 223–242). Cham: Springer.

    Chapter  Google Scholar 

  • Lokaiczyk, R., Faatz, A., Beckhaus, A., & Goertz, M. (2007, August). Enhancing just-in-time e-learning through machine learning on desktop context sensors. In International and interdisciplinary conference on modeling and using context (pp. 330–341). Berlin, Heidelberg: Springer.

    Chapter  Google Scholar 

  • Luo, Y., Lee, B., Wohn, D., Rebar, A., Conroy, D., & Choe, E. (2018). Time for break: Understanding information workers’ sedentary behavior through a break prompting system. In CHI ’18 (p. 127).

    Google Scholar 

  • Martha, A. S. D., & Santoso, H. B. (2019). The design and impact of the pedagogical agent: A systematic literature review. Journal of Educators Online, 16(1), n1.

    Article  Google Scholar 

  • Metcalfe, J., Kornell, N., & Finn, B. (2009). Delayed versus immediate feedback in children’s and adults’ vocabulary learning. Memory & Cognition, 37(8), 1077–1087.

    Article  Google Scholar 

  • Monk, C. A., Trafton, J. G., & Boehm-Davis, D. A. (2008). The effect of interruption duration and demand on resuming suspended goals. Journal of Experimental Psychology: Applied, 14(4), 299.

    Google Scholar 

  • Nakajima, T., & Lehdonvirta, V. (2013). Designing motivation using persuasive ambient mirrors. Personal and Ubiquitous Computing, 17(1), 107–126.

    Article  Google Scholar 

  • Schlippe, T., Wölfel, M., & Stitz, A. (2018). U.S. Patent No. US2018/0068662A1. Washington, DC: U.S. Patent and Trademark Office.

    Google Scholar 

  • Schmidt, A. (2000). Implicit human computer interaction through context. Personal Technologies, 4(2–3), 191–199.

    Article  Google Scholar 

  • Schumacher, C., & Ifenthaler, D. (2018). Features students really expect from learning analytics. Computers in Human Behavior, 78, 397–407.

    Article  Google Scholar 

  • Schwendimann, B. A., Rodriguez-Triana, M. J., Vozniuk, A., Prieto, L. P., Boroujeni, M. S., Holzer, A., et al. (2016). Perceiving learning at a glance: A systematic literature review of learning dashboard research. IEEE Transactions on Learning Technologies, 10(1), 30–41.

    Article  Google Scholar 

  • Shan, C. (2012). Learning local binary patterns for gender classification on real-world face images. Pattern Recognition Letters, 33(4), 431–437.

    Article  Google Scholar 

  • Siegle, M., & Lames, M. (2012). Game interruptions in elite soccer. Journal of Sports Sciences, 30(7), 619–624.

    Article  Google Scholar 

  • Siemens, G., & Baker, R. S. J. D. (2012). Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252–254).

    Google Scholar 

  • Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1510–1529.

    Article  Google Scholar 

  • Streng, S., Stegmann, K., Hußmann, H., & Fischer, F. (2009). Metaphor or diagram? Comparing different representations for group mirrors. In Proc. OZCHI ’09 (pp. 249–256). New York: ACM.

    Google Scholar 

  • Udell, C. (2014). Employing mobile device sensors for enhanced learning experiences. Mastering Mobile Learning, 145–148, https://www.doi.org/10.1002/9781119036883.ch20

  • Van Beuningen, C. G., De Jong, N. H., & Kuiken, F. (2008). The effect of direct and indirect corrective feedback on L2 learners’ written accuracy. ITL International Journal of Applied Linguistics, 156, 279–296.

    Article  Google Scholar 

  • Van Gog, T., Kester, L., & Paas, F. (2011). Effects of concurrent monitoring on cognitive load and performance as a function of task complexity. Applied Cognitive Psychology, 25(4), 584–587.

    Article  Google Scholar 

  • Wölfel, M. (2012). Kinetic space: 3D-Gestenerkennung für dich und mich. Konturen, Ausgabe, 31, 58–63.

    Google Scholar 

  • Wölfel, M. (2013). Gesture-based learning. In Jahrbuch eLearning & Wissensmanagement 2014 Chapter: Gesture-based learning. Hagen: Siepmann Media.

    Google Scholar 

  • Wölfel, M. (2017). Acceptance of dynamic feedback to poor sitting habits by anthropomorphic objects. In Proceedings of the 11th EAI international conference on pervasive computing technologies for healthcare (pp. 307–314). New York: ACM.

    Google Scholar 

  • Wölfel, M., Schlippe, T., & Stitz, A. (2015). Voice driven type design. In 2015 international conference on speech technology and human-computer dialogue (SpeD) (pp. 1–9). New York: IEEE. https://doi.org/10.1109/SPED.2015.7343095

  • Wulf, G., Shea, C., & Lewthwaite, R. (2010). Motor skill learning and performance: A review of influential factors. Medical Education, 44(1), 75–84.

    Article  Google Scholar 

  • Yin, Z., Quanqi, C., & Yujin, Z. (2014). Deep learning and its new progress in object and behavior recognition. Journal of Image and Graphics, 19(2), 175–184.

    Google Scholar 

  • Yun, H., Fortenbacher, A., & Pinkwart, N. (2017). Improving a mobile learning companion for self-regulated learning using sensors. In CSEDU (1) (pp. 531–536).

    Google Scholar 

  • Yun, H., Fortenbacher, A., Pinkwart, N., & Bisson, T. (2017). A pilot study of emotion detection using sensors in a learning context: Towards an affective learning companion. In Proceedings of DeLFI and GMW workshops 2017 (pp. 15–25).

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer Science and Business Information Systems, Karlsruhe University of Applied Sciences, Karlsruhe, Germany

    Matthias Wölfel

Authors
  1. Matthias Wölfel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthias Wölfel .

Editor information

Editors and Affiliations

  1. Institut für Informatik, Humboldt-Univ. zu Berlin, Berlin, Germany

    Niels Pinkwart

  2. National Engineering Research Center for E-learning, Central China Normal University, Wuhan, Hubei, People’s Republic of China

    Sannyuya Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wölfel, M. (2020). Non-distracting Feedback in Artificial Intelligence Supported Learning. In: Pinkwart, N., Liu, S. (eds) Artificial Intelligence Supported Educational Technologies. Advances in Analytics for Learning and Teaching. Springer, Cham. https://doi.org/10.1007/978-3-030-41099-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-41099-5_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41098-8

  • Online ISBN: 978-3-030-41099-5

  • eBook Packages: EducationEducation (R0)

Publish with us

Policies and ethics

Search

Navigation