Abstract
This paper presents for the first time a detailed analysis of fine-grained navigation style identification in MOOCs backed by a large number of active learners. The result shows 1) whilst the sequential style is clearly in evidence, the global style is less prominent; 2) the majority of the learners do not belong to either category; 3) navigation styles are not as stable as believed in the literature; and 4) learners can, and do, swap between navigation styles with detrimental effects. The approach is promising, as it provides insight into online learners’ temporal engagement, as well as a tool to identify vulnerable learners, which potentially benefit personalised interventions (from teachers or automatic help) in Intelligent Tutoring Systems (ITS).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
1st International Conference on Learning Analytics and Knowledge 2011 | Connecting the Technical, Pedagogical, and Social Dimensions of Learning Analytics. https://tekri.athabascau.ca/analytics/. Accessed 01 Mar 2020
Shi, L., Cristea, A.I.: In-depth exploration of engagement patterns in MOOCs. In: Hacid, H., Cellary, W., Wang, H., Paik, H.-Y., Zhou, R. (eds.) WISE 2018. LNCS, vol. 11234, pp. 395–409. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02925-8_28
Papamitsiou, Z., Economides, A.A.: Learning analytics and educational data mining in practice: a systematic literature review of empirical evidence. J. Educ. Technol. Soc. 17, 49–64 (2014)
Ferguson, R., Clow, D.: Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs). In: Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, LAK 2015, pp. 51–58. ACM Press, Poughkeepsie, New York (2015). https://doi.org/10.1145/2723576.2723606
Alexander, C.: A Pattern Language: Towns, Buildings, Construction. OUP, New York (1978)
Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33, 135–146 (2007). https://doi.org/10.1016/j.eswa.2006.04.005
Alamri, A., et al.: Predicting MOOCs dropout using only two easily obtainable features from the first week’s activities. In: Coy, A., Hayashi, Y., Chang, M. (eds.) ITS 2019. LNCS, vol. 11528, pp. 163–173. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-22244-4_20
Pardo, A., Jovanovic, J., Dawson, S., Gašević, D., Mirriahi, N.: Using learning analytics to scale the provision of personalised feedback. Br. J. Educ. Technol. 50, 128–138 (2019). https://doi.org/10.1111/bjet.12592
Zhang, X., Meng, Y., Ordóñez de Pablos, P., Sun, Y.: Learning analytics in collaborative learning supported by Slack: from the perspective of engagement. Comput. Hum. Behav. 92, 625–633 (2019). https://doi.org/10.1016/j.chb.2017.08.012
Shoufan, A.: Estimating the cognitive value of YouTube’s educational videos: a learning analytics approach. Comput. Hum. Behav. 92, 450–458 (2019). https://doi.org/10.1016/j.chb.2018.03.036
Cristea, A.I., Alamri, A., Kayama, M., Stewart, C., Alshehri, M., Shi, L.: Earliest predictor of dropout in MOOCs: a longitudinal study of futurelearn courses. Presented at the 27th International Conference on Information Systems Development (ISD2018), Lund, Sweden, 22 August (2018)
Shi, L., Cristea, A., Toda, A., Oliveira, W.: Revealing the hidden patterns: a comparative study on profiling subpopulations of MOOC students. In: The 28th International Conference on Information Systems Development (ISD2019). Association for Information Systems, Toulon, France (2019)
Zhu, M., Bergner, Y., Zhang, Y., Baker, R., Wang, Y., Paquette, L.: Longitudinal engagement, performance, and social connectivity: a MOOC case study using exponential random graph models. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, LAK 2016, pp. 223–230. ACM Press, Edinburgh (2016). https://doi.org/10.1145/2883851.2883934
Yang, B., Shi, L., Toda, A.: Demographical changes of student subgroups in MOOCs: towards predicting at-risk students. Presented at the 28th International Conference on Information Systems Development (ISD2019), Toulon, France, August (2019)
Van Laer, S., Elen, J.: The effect of cues for calibration on learners’ self-regulated learning through changes in learners’ learning behaviour and outcomes. Comput. Educ. 135, 30–48 (2019). https://doi.org/10.1016/j.compedu.2019.02.016
Felder, R.M., Silverman, L.K.: Learning and teaching styles in engineering education. Eng. Educ. 78, 674–681 (1988)
Kolb, A.Y., Kolb, D.A.: Learning styles and learning spaces: enhancing experiential learning in higher education. Acad. Manag. Learn. Educ. 4, 193–212 (2005)
Kirschner, P.A.: Stop propagating the learning styles myth. Comput. Educ. 106, 166–171 (2017). https://doi.org/10.1016/j.compedu.2016.12.006
Hassan, M.A., Habiba, U., Majeed, F., Shoaib, M.: Adaptive gamification in e-learning based on students’ learning styles. Interact. Learn. Environ. 1–21 (2019). https://doi.org/10.1080/10494820.2019.1588745
O’Grady, N.: Are Learners Learning? (and How do We Know?). https://about.futurelearn.com/research-insights/learners-learning-know. Accessed 23 Feb 2019
Clow, D.: MOOCs and the funnel of participation. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge, LAK 2013, p. 185. ACM Press, Leuven (2013). https://doi.org/10.1145/2460296.2460332
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Shi, L., Cristea, A.I., Toda, A.M., Oliveira, W. (2020). Exploring Navigation Styles in a FutureLearn MOOC. In: Kumar, V., Troussas, C. (eds) Intelligent Tutoring Systems. ITS 2020. Lecture Notes in Computer Science(), vol 12149. Springer, Cham. https://doi.org/10.1007/978-3-030-49663-0_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-49663-0_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49662-3
Online ISBN: 978-3-030-49663-0
eBook Packages: Computer ScienceComputer Science (R0)