Towards to an Bio-inspired Orchestration of Mobile Learning Activities

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Author(s)

Nassim DENNOUNI 1,* Yvan PETER 1 Luigi LANCIERI 1 Zohra SLAMA 2

1. NOCE team, LIFL Laboratory, Lille1 University, Lille, France

2. ISIBA team, EEDIS Laboratory, Djilali Liabes University, Sidi Bel Abbes, Algeria

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2015.04.01

Received: 12 Jan. 2015 / Revised: 7 Feb. 2015 / Accepted: 2 Mar. 2015 / Published: 8 Apr. 2015

Index Terms

Mobile learning, field trip scenario, POI, orchestration of mobile learning activities, recommendation system, passive collaborative filtering, ACO algorithm

Abstract

This paper presents a new approach to a recommendation of learning activities adapted to the spatial and temporal context of each mobile learner. Indeed, the path traveled by the user during a field trip can be guided using the technique of passive collaborative filtering. This recommendation is based on the ACO (Ant Colony Optimization) algorithm, which represents a good model for swarm intelligence. For this reason, the structure of our mobile scenario is described as a graph where POIs (Point Of Interest) are represented by nodes and the arcs indicate the probability of moving between them. This recommendation system allows the orchestration of mobile learning according to the geographical location of learners and the historical of their activities. Our contribution is devised in three parts: (1) the creation of a mobile learning scenario based on POIs, (2) the adaptation of the ACO algorithm for the orchestration of paths taken by learners, and (3) the development of a recommender system that helps learners to better choose their paths during the field trip.

Cite This Paper

Nassim Dennouni, Yvan Peter, Luigi Lancieri, Zohra Slama, "Towards to an Bio-inspired Orchestration of Mobile Learning Activities", International Journal of Modern Education and Computer Science (IJMECS), vol.7, no.4, pp.1-11, 2015. DOI:10.5815/ijmecs.2015.04.01

Reference

[1]Hung, P.H., Hwang, G.J., Lin, Y.F., Wu, T.H. and Su, I.H.: Seamless Connection between Learning and Assessment-Applying Progressive Learning Tasks in Mobile Ecology Inquiry, Educational Technology & Society. ISSN 1436-4522 194–205.
[2]Dennouni, N., Peter, Y., Lancieri, L. and Slama, Z.: To a Geographical Orchestration of Mobile Learning Activities. iJIM International Journal of Interactive Mobile Technologies. ISSN: 1865-7923, Volume 8, Numéro 2, (2014) 35-41
[3]Sharples, M.: The design of personal mobile technologies for lifelong learning, Computers & Education Volume 34 (2000) 177-193
[4]Lancieri, L., : Collective intelligence in a computer mediated environment, in Handbook of Research on Democratic Strategies and Citizen-Centered E-Government Services, Ejub Kajan editor, IGI global (2014)
[5]Lancieri, L., Manguin, M. and Mangon; S.,: Evaluation of a recommendation system for musical contents, IEEE International Conference on Multimedia & Expo, Hannover (ICME 2008)
[6]Candillier, L., Jack, K., Fessant, F. and Meyer, F.: State-of-the-art recommender systems, Collaborative and Social Information Retrieval and Access, (2009).
[7]Valigiani, G., Jamont, Y. and Bourgeois Republique, C.: Experimenting with a Real-Size Man-Hill to Optimize Pedagogical Paths, ACM Symposium on Applied Computing. (2005).
[8]Wang, T., Wang, K. and Huang, Y.: Using a style-based ant colony system for adaptive learning, Available online at www.sciencedirect.com, Expert Systems with Applications pp 2449–2464 (2008).
[9]Kurilovas, E., Zilinskiene, I., and Dagiene, V.: Recommending suitable learning scenarios according to learners preferences: An improved swarm based approach, Elsevier Computers in Human Behavior (2013)
[10]De Spindler, R., Spindler, D., Norrie, M.C., Grossniklaus, M. and Signer, B.: Spatio-Temporal Proximity as a Basis for Collaborative Filtering in Mobile Environments (2006).
[11]Zheng, W.V., Cao, B., Zheng, Y., Xie, X. and Yang, Q.: Towards mobile intelligence: Learning from GPS history data for collaborative recommendation. (2012) 17–37
[12]Phichaya-anutarat, P. and Mungsing, S. : Hybrid recommendation technique for automated personalized POI selection, International journal of information technology (2014)
[13]Picot-Clémente, R. and Bothorel, C. “Un système de recommandation de lieux basé sur la mesure de Katz dans les réseaux sociaux géographiques” MARAMI2013 4ième conférence sur les modèles et l’analyse des réseaux:
[14]Approches mathématiques et informatiques papier publié dans hal-00960139, version 1(2014).
[15]Biancalana, C., Gasparetti, F., Micarelli, A. and Sansonetti, G., :An Approach to Social Recommendation for Context-Aware MobileServices, ACM Transactions on Intelligent Systems and Technology, Vol. 4, No. 1, Article 10, Publication date: January 2013, Copyright 2011 ACM 978-1-4503-0757-4/11/07 pp 325-334(2011).
[16]Sang, J., Mei, T., Tao Sun, J., Xu, C. and Li, S.: Probabilistic Sequential POIs Recommendation via Check-In Data, ACM SIGSPATIAL GIS ’12, Nov. 6-9, 2012. Redondo Beach, CA, USA Copyright 2012 ACM ISBN 978-1-4503-1691-0/12/11(2012).
[17]Cheng, C., Yang, H., Lyu, M. R. and King, I.: Where You Like to Go Next: Successive Point-of-Interest Recommendation, Proceedings of the 23 International Joint Conference on Artificial Intelligence, August 3-9-2013, Beijing, china pp 2605-2611(2013).
[18]Krepel, W.J. and DuVall, C.R.: Field trips, “A guide for planning and conducting educational experiences”, National Education Association, Washington, DC, (1981).
[19]Giemza, A., Bollen, L., Seydel, P., Overhagen, A. and Ulrich Hoppe, H.: LEMONADE: “A Flexible Authoring Tool for Integrated Mobile Learning Scenarios” The 6th IEEE International Conference on Wireless, Mobile, and Ubiquitous Technologies in Education, IEEE Computer Society. pp 73-80 (2010).
[20]Dillenbourg, P. :Modéliser l'orchestration, Conférence sur éducation et sciences de l’apprendre Session 4, Ecole Normale Supérieure de Lyon, automne, (2012).
[21]Glahn, C. and Specht, M. “Embedding moodle intoubiquitous computing environments”. In Publications and Preprints. LMedia, (2010).
[22]Dorigo, M., Maniezzo, V. and Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics-Part B, volume 26, numéro , pp. 29-41(1996).
[23]Stutzle, T., Lopez-Ibanez, M., Pellegrini, P., Maur, M., Montes de Oca, M., Birattari, M. and Dorigo, M.: Parameter Adaptation in Ant Colony Optimization, Technical Report No.TR/IRIDIA/2010-002 ISSN 1781-3794 (2010).
[24]Roy, S. and Chaudhuri, S.: Bio-inspired Ant Algorithms: A review, I.J.Modern Education and Computer Science, ISSN: 2075-0161, volume 4, pp 25-35 (2013)
[25]Ye, M., Yin, P., Lee, W.C. and Lee, D.L.: Exploiting geographical influence for collaborative point-of-interest recommendation, New York, USA, ACM, pp. 325-334 (2011).
[26]Zafar, A. and Hasan, S.: Towards Contextual Mobile Learning, I.J. Modern Education and Computer Science, ISSN: 2075-0161, volume 12, pp 20-25 (2014).