Abstract
The actual data availability, readiness and publicity has slowed down the research of making use of computational intelligence to improve the knowledge delivery in an emerging learning mode, namely adaptive micro open learning, which naturally has high demand in quality and quantity of data to be fed. In this study, we contribute a novel approach to tackle the current scarcity of both data and rules in micro open learning, by adopting evolutionary algorithm to produce association rules with both rare and negative associations taken into account. These rules further drive the generation and optimization of learner profiles through refinement and augmentation, in order to maintain them in a low-dimensional, descriptive and interpretable form.
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
Sun, G., Cui, T., Guo, W., Beydoun, G., Xu, D., Shen, J.: Micro learning adaptation in MOOC: a software as a service and a personalized learner model. In: Li, F.W.B., Klamma, R., Laanpere, M., Zhang, J., Manjón, B.F., Lau, R.W.H. (eds.) ICWL 2015. LNCS, vol. 9412, pp. 174–184. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25515-6_16
Souza, M.I., Amaral, S.F.D.: Educational micro content for mobile learning virtual environments. Creative Educ. 5, 672–681 (2014)
Lin, J., et al.: From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of the intelligent micro learning. World Wide Web J. https://doi.org/10.1007/s11280-019-00730-9
Lin, J., et al.: Towards the readiness of learning analytics data for micro learning. In: Ferreira, J.E., Musaev, A., Zhang, L.-J. (eds.) SCC 2019. LNCS, vol. 11515, pp. 66–76. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-23554-3_5
Sun, G., Cui, T., Yong, J., Shen, J., Chen, S.: MLaaS: a cloud-based system for delivering adaptive micro learning in mobile MOOC learning. IEEE Trans. Serv. Comput. 11(2), 292–305 (2018)
Sikka, R., Dhankhar, A., Rana, C.: A survey paper on e-learning recommender system. Int. J. Comput. Appl. 47(9), 27–30 (2012)
Rienties, B., Toetenel, L.: The impact of learning design on student behaviour, satisfaction and performance: a cross-institutional comparison across 151 modules. Comput. Hum. Behav. 60, 333–341 (2016)
Kumar, B.S., Rukmani, K.V.: Implementation of web usage mining using apriori and FP growth algorithms. Int. J. Adv. Netw. Appl. 1(6), 400–404 (2010)
Bernardini, A., Conati, C.: Discovering and recognizing student interaction patterns in exploratory learning environments. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010. LNCS, vol. 6094, pp. 125–134. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13388-6_17
Bousbahi, F., Chorfi, H.: MOOC-Rec: a case based recommender system for MOOCs. Soc. Behav. Sci. 195, 1813–1922 (2015)
Merceron, A., Yacef, K.: Interestingness measures for association rules in educational data. In: The 1st International Conference on Educational Data Mining (EDM), Montreal, Canada, June 2008
Tsang, S., Koh, Y.S., Dobbie, G.: Finding interesting rare association rules using rare pattern tree. In: Hameurlain, A., Küng, J., Wagner, R., Cuzzocrea, A., Dayal, U. (eds.) Transactions on Large-Scale Data- and Knowledge-Centered Systems VIII. LNCS, vol. 7790, pp. 157–173. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37574-3_7
Borah, A., Nath, B.: Rare pattern mining: challenges and future perspectives. Complex Intell. Syst. 5(1), 1–23 (2018). https://doi.org/10.1007/s40747-018-0085-9
Romero, C., Ventura, S.: Educational data mining: a review of the state-of-the-art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 40(6), 601–618 (2010)
Das, A.K.: Mining rare association rule. Int. J. Comput. Sci. Inf. Technol. 6(6), 552–5557 (2015)
Zhang, H., Zhao, Y., Cao, L., Zhang, C.: Class association rule mining with multiple imbalanced attributes. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 827–831. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-76928-6_100
Graf, S., Kinshuk, D., Liu, T.-C.: Supporting teachers in identifying students learning styles in learning management systems: an automatic students modelling approach. Educ. Technol. Soc. 12(4), 3–14 (2009)
Lan, A.S., Brinton, C.G., Yang, T., Chiang, M.: Behavior-based latent variable model for learner engagement. In: International Conference on Educational Data Mining, Wuhan, China, pp. 64–71, June 2017
Luna, J.M., Romero, C., Romero, J.R., Ventura, S.: An evolutionary algorithm for the discovery of rare class association rules in learning management systems. Appl. Intell. 42(3), 501–513 (2014). https://doi.org/10.1007/s10489-014-0603-4
Antonelli, M., Ducange, P., Lazzerini, B., Marcelloni, F.: Multi-objective evolutionary design of granular rule-based classifiers. Granul. Comput. 1(1), 37–58 (2015). https://doi.org/10.1007/s41066-015-0004-z
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
Sun, G., Lin, J., Shen, J., Cui, T., Xu, D., Chen, H. (2020). Evolutionary Learner Profile Optimization Using Rare and Negative Association Rules for Micro Open Learning. 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_54
Download citation
DOI: https://doi.org/10.1007/978-3-030-49663-0_54
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)