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.
Keywords
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
Chaouachi, M., Chalfoun, P., Jraidi, I., Frasson, C.: Affect and mental engagement: towards adaptability for intelligent. In: FLAIRS Conference (2010)
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
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)
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
Gobert, J.D., Baker, R.S., Wixon, M.B.: Operationalizing and detecting disengagement within online science microworlds. Educ. Psychol. 50(1), 43–57 (2015)
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)
Henrick, P.B.G.: Moodle analytics plans: project inspire. Moodle Moot (2018)
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)
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)
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
Martin, A.J.: Enhancing student motivation and engagement: the effects of a multidimensional intervention. Contemp. Educ. Psychol. 33(2), 239–269 (2008)
Miller, B.W.: Using reading times and eye-movements to measure cognitive engagement. Educ. Psychol. 50(1), 31–42 (2015)
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)
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)
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
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
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
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
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)
Shulman, L.S.: Making differences: a table of learning. Change Mag. High. Learn. 34(6), 36–44 (2002)
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
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)