Recommendation Techniques in Mobile Learning Context: A Review

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Author(s)

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

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

2. NOCE team, CRISTAL Laboratory, Lille1 University, Lille, France

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2017.10.05

Received: 7 Feb. 2017 / Revised: 20 May 2017 / Accepted: 28 Jun. 2017 / Published: 8 Oct. 2017

Index Terms

Mobile learning, field trip, mobile learning activities, collaborative filtering, recommendation system, Point of Interest, ACO algorithm

Abstract

The objective of this article is to make a bibliographic study on the recommendation of learning activities that can integrate user mobility. This type of recommendation makes it possible to exploit the history of previous visits in order to offer adaptive learning according to the instantaneous position of the learner and the pedagogy of the guide. To achieve this objective, we review the existing literature on the recommendation systems that integrate contexts such as geographic location and training profile. Next, we are interested in the social relationships that users can have between themselves. Finally, we focus on the work of recommending mobile learning activities in the context of scenarios of field trips.

Cite This Paper

Nassim DENNOUNI, Zohra SLAMA, Yvan PETER, Luigi LANCIERI, "Recommendation Techniques in Mobile Learning Context: A Review", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.10, pp. 37-46, 2017. DOI:10.5815/ijmecs.2017.10.05

Reference

[1]L. Lancieri, “Collective intelligence in a computer-mediated environment”, Chapter 8 in Handbook of Research on Democratic Strategies and Citizen-Centered E-Government Services, DOI: 10.4018/978-1-4666-7266-6, ISBN13: 9781466672666; IGI Global Editor, 2015.
[2]N. Béchet, « État de l’art sur les Systèmes de Recommandation », Projet AxIS de l’INRIA, dans le cadre du projet Addictrip, 2012.
[3]P. Resnick & H. Varian, “Recommender systems”, Communications of the ACM, 40(3), pp 56–58, 1997.
[4]S. Chelcea, G. Gallais & B. Trousse, « Recommandations personnalisées pour la recherche d’information facilitant les déplacements », Mobilité & Ubiquité, June 1-3, 2004, Nice, France, ACM 1-58113-915-2/04/0006, 2004.
[5]Y. Wei, Z. Jennings, R. Moreau and N. Hall, “User evaluation of a market-based recommender system. Journal of Autonomous Agents and Multi-Agent Systems”, pp 251-269, 2008.
[6]Z. Zaier, « Modèle multi-agent pour le filtrage collaboratif de l’information », Thèse de doctorat de l’université du Québec à Montréal, 2010.
[7]I. Boussebough, « Les système multi-agent adaptables », Thèse de doctorat en sciences, Université Mentouri Constantine, 2011.
[8]P. Bedi, R. Sharma & H. Kau, “Recommender system based on collaborative behavior of ants”, Journal of Artificial Intelligence. ISSN 1994-5450, pp 40-50, 2009.
[9]W. Gao, S. Wang and N. Cerrone, “A dynamic recommendation system based on log mining”, International journal of foundations of computer science. Vol. 13, N° 4, pp 521-530, 2002.
[10]L. Lancieri, M. Manguin & S. Mangon, “Evaluation of a recommendation system for musical contents”, IEEE International Conference on Multimedia & Expo, Hannover, (ICME), 2008.
[11]N. Béchet & M. Aufaure « Construction et peuplement de structures hiérarchiques de concepts dans le domaine du e-tourisme » IC’2011(Ingénierie des connaissances).
[12]M. Lopez, « Accès à l’information par un système de filtrage collaboratif contrôlé », Thèse de doctorat à l’Université Grenoble I, 2005.
[13]S. Chelcea, G. Gallais & B. Trousse, « Recommandations personnalisées pour la recherche d’information facilitant les déplacements », Mobilité & Ubiquité, June 1-3, 2004, Nice, France, ACM 1-58113-915-2/04/0006, 2004.
[14]Y. Wang, N. Stash, L. Aroyo, L. Hollink &G. Schreiber, “Using semantic relations for content-based recommender systems in cultural heritage”. In Proceedings of the Workshop on Ontology Patterns (WOP) at ISWC, 16–28, 2009.
[15]M. Hosseini-Pozveh, M. Nematbakhsh & N. Movahhedinia, “A multidimensional approach for context-aware recommendation in mobile commerce”, International Journal of Computer Science and Information Security, 2009.
[16]A. Niaraki & K. Kim, “Ontology based personalized route planning system using a multi-criteria decision making approach”, Expert Systems with Applications, pp 2250–2259, 2009.
[17]C. Yu & H. Chang, “Personalized Location-Based Recommendation Services for Tour Planning in Mobile Tourism Applications”. In: Di Noia, T., Buccafurri, F. (eds.) E-Commerce and Web Technologies. LNCS, vol. 5692, Springer, Heidelberg, pp 38–49, 2009.
[18]P. Vansteenwegen, W. Souffriau, G. Vanden Berghe & D. Van Oudheusden, “The city trip planner: an expert system for tourists”, Expert Systems with Applications 2010.
[19]A. Yahi, A. Chassang, L.Raynaud, H. Duthil, D. Horng & C. Aurigo, “An Interactive Tour Planner for Personalized Itineraries”, ACM 978-1-4503-3306-1/15/03, 2015.
[20]H. Drachsler, “Recommender systems for learning”, http://fr.slideshare.net/Drachsler/recsystel-lecture-at-advanced-siks-course-nl, 2014.
[21]N. Manouselis, H. Drachsler, K. Verbert, & E. Duval, “Recommender Systems for Learning” – An introduction – Handbook, Springer, 2012.
[22]P. Brusilovsky & N. Henze, “Open Corpus Adaptive Educational Hypermedia”, Brusilovsky P, Kobsa A, Nejdl W (eds), The Adaptive Web: Methods and Strategies of Web Personalization, LNCS 4321, Berlin Heidelberg NewYork: Springer, pp 671-696, 2007.
[23]M. Kocaleva, I. Stojanovic & Z. Zoran, “Model of e-Learning Acceptance and Use for Teaching Staff in Higher Education Institutions”, International Journal of Modern Education and Computer Science (IJMECS), vol. 7, N 3, pp 23-34, 2015.
[24]P. Brusilovsky & W. Nejdl, “Adaptive Hypermedia and Adaptive Web. Practical”, Handbook of Internet Computing, CRC Press LLC, 2004.
[25]R. Koper, E. Rusman & P. Sloep, “Effective Learning Networks”, Lifelong Learning in Europe, pp18-27, 2005.
[26]P. Brusilovsky & N. Henze, “Open Corpus Adaptive Educational Hypermedia”, Brusilovsky P, Kobsa A, Nejdl W (eds), The Adaptive Web: Methods and Strategies of Web Personalization, LNCS 4321, Berlin Heidelberg NewYork: Springer, 671-696, 2007.
[27]H. Drachsler, H. Hummel & R. Koper, “Personal recommender systems for learners in lifelong learning: requirements, techniques and model”, International Journal of Learning Technology, 3(4), pp 404-423, 2008.
[28]M. Recker & A. Walker, “Supporting “word of mouth” social networks through collaborative information filtering”, Journal of Interactive Learning Research, 14(1), pp 79-98, 2003.
[29]M. Anderson, M. Ball, H. Boley, S. Greene, N. Howse, D. Lemire & S. McGrath, “RACOFI: A Rule-Applying Collaborative Filtering System”, 2003.
[30]R. Sheizaf, B. Miri, D.-G. Yuval & T. Eran, “QSIA – a Web-based environment for learning, assessing and knowledge sharing in communities”, Computers & Education 43, pp 273–289, 2004.
[31]D. K. Le Tran, « Conception et développement de fonctionnalités innovantes liées à Facebook pour un système de recommandation ». Rapport bibliographique Dept. Logique des Usages, Sciences Sociales et de l'Information Telecom Bretange, 2011.
[32]Naziha Abderrahim & Sidi Mohamed Benslimane, “Towards Improving Recommender System: A Social Trust-Aware Approach”, International Journal of Modern Education and Computer Science (IJMECS), vol. 3, N 4, pp 8-15, 2015.
[33]F. Carmagnola, F. Vernero, & P. Grillo, “Sonars : A social networksbased algorithm for social recommender systems”, volume 5535 de Lecture Notes in Computer Science, Springer Berlin/ Heidelberg, 10.1007/978-3-642-02247-0-22, pp 223-234,2009. Zancanaro, editeurs: User Modeling, Adaptation, and Persona-lization, volume 5535 de Lecture Notes in Computer Science, Springer Berlin/ Heidelberg, 10.1007/978-3-642-02247-0-22, pp 223-234, 2009.
[34]J. O'Donovan & B. Smyth, “Trust in recommender systems”, Proceedings of the 10th international conference on intelligent user interfaces, IUI '05, New York, NY, USA, ACM, pp167-174, 2005.
[35]M. Szomszor, H. Alani, I.Cantador, K. O'Hara & N. Shadbolt, “Semantic modelling of user interests based on cross-folksonomy analysis”, 7th International Semantic Web Conference (ISWC), 2008.
[36]Z.-K. Z. Liu, Y.-C. Zhang & T. Zhou, “Solving the cold-start problem inrecommender systems with social tags”, 2010.
[37]M. Bank & J. Franke, “Social networks as data source for recommendation systems”, Will Aalst, John Mylopoulos, Norman M. Sadeh, Michael J. Shaw, Clemens Szyperski, Francesco Buccafurri et Giovanni Semeraro, editeurs : E-Commerce and Web Technologies, volume 61 de Lecture Notes in Business Information Processing, Springer , 49-60, 2010.
[38]M. J. Pazzani, “A framework for collaborative, content-based and demographic filtering”. Artif. Intell. Rev., 13, pp 393-408, 1999.
[39]M. Vozalis & K. G. Margaritis, “On the enhancement of collaborative filtering by demographic data”. Web Intelli. and Agent Sys., 4, pp117-138, 2006.
[40]B. Yapriady & A. L. Uitdenbogerd, “Combining demographic data with collaborative ltering for automatic music recommendation”. In Rajiv Khosla, Robert J. Howlett et Lakhmi C. Jain, editeurs : Knowledge-Based Intelligent Information and Engineering Systems, volume 3684 de Lecture Notes in Computer Science, Springer Berlin /Heidelberg, 2005. 10.1007/11554028-29., pp 201-207, 2005.
[41]L. Candillier, K. Jack, F. Fessant, & F. Meyer, “State-of-the-art recommender systems”, Collaborative and Social Information Retrieval and Access, 2009.
[42]M. Dorigo, V. Maniezzo & A. Colorni, “Ant system: optimization by a colony of cooperating agents”, IEEE Transactions on Systems, Man, and Cybernetics--Part B, volume 26, numéro 1, pp 29-41, 1996.
[43]R. Sangita & C. Sheli Sinha, “Bio-inspired Ant Algorithms: A review”, International Journal of Modern Education and Computer Science (IJMECS), vol. 4, N 4, pp 25-35, 2013.
[44]G. Valigiani, Y. Jamont & C. Bourgeois Republique, “Experimenting with a Real-Size Man-Hill to Optimize Pedagogical Paths”, ACM Symposium on Applied Computing, 2005.
[45]T.-I. Wang, K.-T. Wang, Y.-M. Huang, “Using a style-based ant colony system for adaptive learning”, Expert Systems with Applications 34, pp 2449–2464, 2008.
[46]E. Kurilovas, I. Zilinskiene, and V. Dagiene, “Recommending suitable learning scenarios according to learners’preferences: An improved swarm based approach”, Computers in Human Behavior (2013), http://dx.doi.org/10.1016/j.chb.2013.06.036, 2013.
[47]A. De Spindler, R. D. Spindler, M. C. Norrie, M. Grossniklaus and B. Signer, “Spatio-Temporal Proximity as a Basis for Collaborative Filtering in Mobile Environments”, 2006.
[48]W.-V. Zheng, B. Cao, Y. Zheng, X. Xie & Q.Yang, “Towards mobile intelligence: Learning from GPS history data for collaborative recommendation”, Artificial Intelligence, pp17–37, 2012.
[49]P. Phichaya-anutarat & S. Mungsing, “Hybrid recommendation technique for automated personalized POI selection”, International journal of information technology (IJIT) Volume 1, Issue 1, January- June, 2014.
[50]M. Ye, P. Yin, W.-C. Lee & D.-L. Lee, “Exploiting geographical influence for collaborative point-of-interest recommendation”, New York, NY, USA, ACM, pp 325-334, 2011.
[51]C. Biancalana, F. Gasparetti, A. Micarelli & G. Sansonetti, “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, 2013.
[52]J. Sang, T. Mei, J.Tao., C.Xu & S. Li, “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.
[53]C. Cheng, H.Yang, M. Lyu & I. King, “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.
[54]N. Dennouni, Y. Peter, L. Lancieri & Z. Slama, “To a Geographical Orchestration of Mobile Learning Activities”, iJIM International Journal of Interactive Mobile Technologies. ISSN: 1865-7923, Volume 8, Number 2, 2014,pp 35-41
[55]N. Dennouni, Y. Peter, L. Lancieri & Z.Slama, “Towards to an Bio-inspired Orchestration of Mobile Learning Activities”, International Journal of Modern Education and Computer Science (IJMECS), vol. 4, N 7, pp 1-11, 2015.
[56]M. Ava Clare & O. Robles, “The Use of Educational Web Tools: An Innovative Technique in Teacher Education Courses”, International Journal of Modern Education and Computer Science (IJMECS), vol. 5, N 2, pp 1-11, 2013.
[57]A. Pourhosein Gilakjani, L. Lai-Mei & I. Hairul Nizam “Teachers:Use of Technology and Constructivism”, International Journal of Modern Education and Computer Science (IJMECS), vol. 7, N 4, pp 49-63, 2013.