Skip to main content
SpringerLink
Log in

Analyzing Engagement in an On-Line Session

  • Conference paper
  • First Online:
Artificial Intelligence in Education (AIED 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11626))

Included in the following conference series:

Abstract

It is well known that if the learning strategies align with learning outcomes, learner well engaged in the session is likely to make progress in acquiring knowledge. However, it is challenging to ascertain learner’s engagement in an online environment and to guess their grasp on particular topics. The objective of this work is to check for relations between the engagement and the performance. Firstly, log traces for each learner in a session depending on their interaction will be labeled. These features are analyzed to calculate engagement indicators that represent the level of learner’s involvement and engagement levels per activity and session. This will help to identify the less engaged learners as well as to inform about the low engaging sessions or a particular activity in the sessions. It could be used in an adaptive learning environment to update the learning process by providing more engaging activities. Using the quantified traces, the prediction of the performance based on the interactions of the learner will be attempted. The training dataset from completed courses with labeled performance will be used to develop a model that can effectively predict the performance well in advance. This can help to prescribe techniques like extra help through more exercises, reference material for whom the predicted performance is below the threshold level. Supervised machine learning algorithms like neural networks, random forest and support vector machines will be explored to understand the prominent indicators of performance and to compare and find the most efficient algorithm for the purpose.

This publication is an outcome of the R&D work undertaken under the Visvesvaraya PhD Scheme of Ministry of Electronics & Information Technology, Government of India, being implemented by Digital India Corporation.

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 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
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

Institutional subscriptions

References

  1. Appleton, J.J., Christenson, S.L., Kim, D., Reschly, A.L.: Measuring cognitive and psychological engagement: validation of the student engagement instrument. J. Sch. Psychol. 44(5), 427–445 (2006)

    Article  Google Scholar 

  2. Carini, R.M., Kuh, G.D., Klein, S.P.: Student engagement and student learning: testing the linkages*. Res. High. Educ. 47(1), 1–32 (2006)

    Article  Google Scholar 

  3. Chaouachi, M., Chalfoun, P., Jraidi, I., Frasson, C.: Affect and mental engagement: towards adaptability for intelligent. In: FLAIRS Conference (2010)

    Google Scholar 

  4. Dascalu, M., Popescu, E., Becheru, A., Crossley, S., Trausan-Matu, S.: Predicting academic performance based on students’ blog and microblog posts. In: Verbert, K., Sharples, M., Klobučar, T. (eds.) EC-TEL 2016. LNCS, vol. 9891, pp. 370–376. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45153-4_29

    Chapter  Google Scholar 

  5. Fredricks, J.A., Blumenfeld, P.C., Paris, A.H.: School engagement: potential of the concept, state of the evidence. Rev. Educ. Res. 74(1), 59–109 (2004)

    Article  Google Scholar 

  6. Fredricks, J.A., McColskey, W.: The measurement of student engagement: a comparative analysis of various methods and student self-report instruments. In: Christenson, S.L., Reschly, A.L., Wylie, C. (eds.) Handbook of Research on Student Engagement, pp. 763–782. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-2018-7_37

    Chapter  Google Scholar 

  7. Gobert, J.D., Baker, R.S., Wixon, M.B.: Operationalizing and detecting disengagement within online science microworlds. Educ. Psychol. 50(1), 43–57 (2015)

    Article  Google Scholar 

  8. Grafsgaard, J., Wiggins, J.B., Boyer, K.E., Wiebe, E.N., Lester, J.: Automatically recognizing facial expression: predicting engagement and frustration. In: Educational Data Mining 2013 (2013)

    Google Scholar 

  9. Henrick, P.B.G.: Moodle analytics plans: project inspire. Moodle Moot (2018)

    Google Scholar 

  10. Joseph, E.: Engagement tracing: using response times to model student disengagement. In: Artificial Intelligence in Education: Supporting Learning Through Intelligent and Socially Informed Technology, vol. 125, p. 88 (2005)

    Google Scholar 

  11. Lee, J.S.: The relationship between student engagement and academic performance: is it a myth or reality? J. Educ. Res. 107(3), 177–185 (2014)

    Article  Google Scholar 

  12. Liu, D.Y.T., Richards, D., Dawson, P., Froissard, J.-C., Atif, A.: Knowledge acquisition for learning analytics: comparing teacher-derived, algorithm-derived, and hybrid models in the moodle engagement analytics plugin. In: Ohwada, H., Yoshida, K. (eds.) PKAW 2016. LNCS (LNAI), vol. 9806, pp. 183–197. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42706-5_14

    Chapter  Google Scholar 

  13. Martin, A.J.: Enhancing student motivation and engagement: the effects of a multidimensional intervention. Contemp. Educ. Psychol. 33(2), 239–269 (2008)

    Article  Google Scholar 

  14. Miller, B.W.: Using reading times and eye-movements to measure cognitive engagement. Educ. Psychol. 50(1), 31–42 (2015)

    Article  Google Scholar 

  15. Morris, L.V., Finnegan, C., Wu, S.S.: Tracking student behavior, persistence, and achievement in online courses. Internet High. Educ. 8(3), 221–231 (2005)

    Article  Google Scholar 

  16. Mwalumbwe, I., Mtebe, J.S.: Using learning analytics to predict students’ performance in moodle learning management system: a case of Mbeya University of Science and Technology. Electron. J. Inf. Syst. Dev. Countries 79(1), 1–13 (2017)

    Article  Google Scholar 

  17. Naik, V., Kamat, V.: Predicting engagement using machine learning techniques. In: 26th International Conference on Computers in Education Doctoral Student Consortium Proceedings, pp. 17–20. Asia-Pacific Society for Computers in Education (APSCE), Taiwan, November 2018

    Google Scholar 

  18. Olivé, D.M., Huynh, D.Q., Reynolds, M., Dougiamas, M., Wiese, D.: A supervised learning framework for learning management systems. In: Proceedings of the First International Conference on Data Science, E-Learning and Information Systems, p. 18. ACM, October 2018

    Google Scholar 

  19. Ramesh, A., Goldwasser, D., Huang, B., Daume III, H., Getoor, L.: Uncovering hidden engagement patterns for predicting learner performance in MOOCs. In: Proceedings of the First ACM Conference on Learning @ Scale Conference, pp. 157–158. ACM, March 2014

    Google Scholar 

  20. Rodríguez, C.A.V., Lavalle, M.M., Elías, R.P.: Modeling student engagement by means of nonverbal behavior and decision trees. In: 2015 International Conference on Mechatronics, Electronics and Automotive Engineering (ICMEAE), pp. 81–85, November 2015

    Google Scholar 

  21. Romero, C., Espejo, P.G., Zafra, A., Romero, J.R., Ventura, S.: Web usage mining for predicting final marks of students that use moodle courses. Comput. Appl. Eng. Educ. 21(1), 135–146 (2013)

    Article  Google Scholar 

  22. Shulman, L.S.: Making differences: a table of learning. Change Mag. High. Learn. 34(6), 36–44 (2002)

    Article  Google Scholar 

  23. Skinner, E.A., Pitzer, J.R.: Developmental dynamics of student engagement, coping, and everyday resilience. In: Christenson, S.L., Reschly, A.L., Wylie, C. (eds.) Handbook of Research on Student Engagement, pp. 21–44. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-2018-7_2

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Goa University, Taleigao, Goa, India

    Vandana Naik & Venkatesh Kamat

Authors
  1. Vandana Naik
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Venkatesh Kamat
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vandana Naik .

Editor information

Editors and Affiliations

  1. University of Sao Paulo, Sao Paulo, Brazil

    Seiji Isotani

  2. University of Malaga, Málaga, Spain

    Eva Millán

  3. Carnegie Mellon University, Pittsburgh, PA, USA

    Amy Ogan

  4. DePaul University, Chicago, IL, USA

    Peter Hastings

  5. Carnegie Mellon University, Pittsburgh, PA, USA

    Bruce McLaren

  6. University College London, London, UK

    Rose Luckin

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Naik, V., Kamat, V. (2019). Analyzing Engagement in an On-Line Session. In: Isotani, S., Millán, E., Ogan, A., Hastings, P., McLaren, B., Luckin, R. (eds) Artificial Intelligence in Education. AIED 2019. Lecture Notes in Computer Science(), vol 11626. Springer, Cham. https://doi.org/10.1007/978-3-030-23207-8_66

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-23207-8_66

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-23206-1

  • Online ISBN: 978-3-030-23207-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation