Advertisement

Online Learning Activity Index (OLAI) and Its Application for Adaptive Learning

  • Jiyou JiaEmail author
  • Yueyang Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10309)

Abstract

We propose a model, Learning Activity Index (LAI) to describe learners’ learning activities comprising three components, i.e., speed, quality and quantity. We then concretize this model into online learning and propose the Online Learning Activity Index (OLAI). With the example of a web-based mathematics learning system, OLAI can be calculated by the summarization of standardized and dimensionless speed, quality and quantity. So OLAI is also a dimensionless value and can comprehensively describe the learner’s online learning behavior. We use OLAIMAA (OLAI Mean of All Activities) to measure one learner’s online learning activities during a certain period on average and OLAISAA (OLAI Sum of All Activities) to measure those from a summative perspective. Similarly, the performance of a group, such as a class or a school, can also be described by those indexes. Furthermore, we design the personalized instructional strategies adaptive to the learner’s OLAI and the OLAI of the learning group. The instructional strategies include the feedback corresponding to numerical intervals of three components of OLAI, bonus based on longitudinal comparison with the learner’s own activity profile, bonus based on locally horizontal comparison with learner’s classmates or partners in the same online group, and bonus based on globally horizontal comparison with all learners in the same system. We suggest that OLAI should be taken into consideration for the design of online learning platforms or course management system, and be referred as a basic approach to individualized instruction by both teachers and parents.

Keywords

Learning analytics Learning activity index Online learning activity index Mathematics Adaptive learning 

Notes

Acknowledgement

This research is supported by the project, “Lexue 100, Smart Education”, of Beijing Lexue 100 Online Education Co., Ltd. The authors thank also all the teachers and students who have participated in the project.

References

  1. 1.
    Elbadrawy, A., Polyzou, A., Ren, Z., Sweeney, M., Karypis, G., Rangwala, H.: Predicting student performance using personalized analytics. IEEE Comput. 49(4), 61–69 (2016)CrossRefGoogle Scholar
  2. 2.
    Moissa, B., Gasparini, I., Kemczinski, A.: A systematic mapping on the learning analytics field and its analysis in the massive open online courses context. Int. J. Distance Educ. Technol. 13(3), 1–24 (2015)CrossRefGoogle Scholar
  3. 3.
    Hughes, G., Dobbins, C.: The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs). Res. Pract. Technol. Enhanced Learn. 10(1), 10–15 (2015)CrossRefGoogle Scholar
  4. 4.
    Mukala, P., Buijs, J.C.A.M., van der Aalst, W.M.P.: Exploring students’ learning behaviour in moocs using process mining techniques. Technical report, Eindhoven University of Technology, BPM Center Report BPM-15-10 (2015). BPMcenter.org
  5. 5.
    Arnold, K.E., Pistilli, M.D.: Course signals at Purdue: using learning analytics to increase student success. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 267–270. ACM, April 2012Google Scholar
  6. 6.
    Ren, Z., Rangwala, H., Johri, A.: Predicting performance on MOOC assessments using multi-regression models. arXiv preprint arXiv:1605.02269 (2016)
  7. 7.
    VanLehn, K.: The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educ. Psychol. 46(4), 197–221 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Graduate School of EducationPeking UniversityBeijingChina
  2. 2.Department of Computer ScienceBeijing Foreign Studies UniversityBeijingChina

Personalised recommendations