Abstract
Adaptivity, personalization and recommendation techniques are classic solutions recommended by many specialists for providing successful learning experiences by offering suitable adaptation that satisfy the learning preferences and meet heterogeneous characteristics of users. In the present paper, we propose a video recommender system across a Small Private Online Course (SPOC). We adopt a hybrid recommendation technique which consists on analyzing users’ video behavior while enrolling into a SPOC, estimating their interest in videos, finding learners with similar profile and finally recommending target user the same videos in which similar users are interested in. The proposed approach consist first on capturing and analyzing user’s video clickstream in order to construct a user profile with an implicit way to infer user’s interest in videos. Second, the unsupervised K-Means clustering algorithm is used to group users with similar video behavior into clusters. Finally, videos from similar profiles that could meet user’s interest can be recommended.
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Kaplan, A.M., Haenlein, M.: Higher education and the digital revolution: about MOOCs, SPOCs, social media, and the Cookie Monster. Bus. Horiz. 59(4), 441–450 (2016). https://doi.org/10.1016/j.bushor.2016.03.008
Onah, D.F.O., Sinclair, J.: Massive open online courses: an adaptive learning framework. In: The 9th International Technology, Education and Development Conference, Madrid, Spain, pp. 1258–1266. INTED2015 Proceedings, March 2015
Filius, R.M., De Kleijn, R.A.M., Uijl, S.G., Prins, F.J., Van Rijen, H.V.M., Grobbee, D.E.: Challenges concerning deep learning in SPOCs. Int. J. Technol. Enhanced Learn. 10(1/2), 111 (2018). https://doi.org/10.1504/ijtel.2018.10008600
Brusilovsky, P., Peylo, C.: Adaptive and Intelligent Web-based Educational Systems. Int. J. Artif. Intell. Educ. (IJAIED) 13, 159–172 (2003)
Alshammari, M., Anane, R., Hendley, R. J.: Adaptivity in E-learning systems. In: Eighth International Conference on Complex, Intelligent and Software Intensive Systems (2014). https://doi.org/10.1109/cisis.2014.12
Muruganandam, S., Srininvasan, N.: Personalised e-learning system using learner profile ontology and sequential pattern mining-based recommendation. Int. J. Bus. Intell. Data Mining 12(1), 78–93 (2017). https://doi.org/10.1504/ijbidm.2017.082704
Chrysafiadi, K., Virvou, M.: Advances in Personalized Web-Based Education. Intelligent Systems Reference Library (2015). https://doi.org/10.1007/978-3-319-12895-5
Shute, V., Towle, B.: Adaptive E-learning. Educ. Psychol. 38(2), 105–114 (2003). https://doi.org/10.1207/s15326985ep3802_5
Henning, P., Heberle, F., Streicher, A., Zielinski, A., Swertz, C., Bock, J., Zander, S.: Personalized web learning: merging open educational resources into adaptive courses for higher education. In: 22nd International Conference User Modeling, Adaptation, and Personalization, vol. 1181, pp. 55–62. Aalborg, Denmark (2014)
Daniel, S.J., Vázquez Cano, E., Gisbert, M.: The future of MOOCs: adaptive learning or business model? RUSC. Universities and Knowl. Soc. J. 12(1), 64–73 (2015). https://doi.org/10.7238/rusc.v12i1.2475
Sonwalkar, N.: The first adaptive MOOC: a case study on pedagogy framework and scalable cloud architecture—Part I. MOOCs FORUM 1(P), pp. 22–29 (2013). https://doi.org/10.1089/mooc.2013.0007
Lerís, D., Sein-Echaluce, M.L., Hernández, M., Bueno, C.: Validation of indicators for implementing an adaptive platform for MOOCs. Comput. Hum. Behav. 72, 783–795 (2017). https://doi.org/10.1016/j.chb.2016.07.054
Clerc, F., Lefevre, M., Guin, N., Marty, J. C.: Mise en place de la personnalisation dans le cadre des MOOCs. In: 7ème Conférence sur les Environnements Informatiques pour l’Apprentissage Humain EIAH, pp. 144–155, June 2015
Sein-Echaluce, M.L., Fidalgo-Blanco, Á., García-Peñalvo, F.J., Conde, M.Á.: iMOOC platform: adaptive MOOCs. In: Learning and Collaboration Technologies, pp. 380–390 (2016). https://doi.org/10.1007/978-3-319-39483-1_35
Yan-hong, L., Bo, Z., Jian-hou, G.: Make adaptive learning of the MOOC: The CML model. Paper presented at the 10th International Conference on Computer Science & Education (ICCSE) (2015). https://doi.org/10.1109/iccse.2015.7250398
Fasihuddin, H., Skinner, G., Athauda, R.: A framework to personalise open learning environments by adapting to learning styles. In: Proceedings of the 7th International Conference on Computer Supported Education (2015). https://doi.org/10.5220/0005443502960305
García-Peñalvo, F.J., Fidalgo-Blanco, Á., Sein-Echaluce, M.L.: An adaptive hybrid MOOC model: disrupting the MOOC concept in higher education. Telematics Inform. 35(4), 1018–1030 (2018). https://doi.org/10.1016/j.tele.2017.09.012
Dutt, A., Ismail, M.A., Herawan, T.: A systematic review on educational data mining. IEEE Access 5, 15991–16005 (2017). https://doi.org/10.1109/access.2017.2654247
Bakhshinategh, B., Zaiane, O.R., ElAtia, S., Ipperciel, D.: Educational data mining applications and tasks: a survey of the last 10 years. Educ. Inf. Technol. 23(1), 537–553 (2017). https://doi.org/10.1007/s10639-017-9616-z
Midgley, C.: Goals, Goal Structures, and Patterns of Adaptive Learning (2014). https://doi.org/10.4324/9781410602152
Brusilovsky, P.: Adaptive hypermedia for education and training. In: Adaptive Technologies for Training and Education, pp. 46–66 (2012). https://doi.org/10.1017/cbo9781139049580.006
Brusilovsky, P.: Adaptive hypermedia. User Model. User-Adap. Inter. 11(1/2), 87–110 (2001)
Kobsa, A., Koenemann, J., Pohl, W.: Personalised hypermedia presentation techniques for improving online customer relationships. Knowl. Eng. Rev. 16(02), 111–155 (2001). https://doi.org/10.1017/s0269888901000108
Boyle, C., Encarnacion, A.O.: Metadoc: an adaptive hypertext reading system. In: Adaptive Hypertext and Hypermedia, pp. 71–89 (1998). https://doi.org/10.1007/978-94-017-0617-9_3
Brusilovsky, P., Pesin, L.: Adaptive navigation support in educational hypermedia: an evaluation of the ISIS-Tutor. J. Comput. Inf. Technol. 6(1), 27–38 (1998)
Popescu, E., Badica, C., Moraret, L.: Accommodating learning styles in an adaptive educational system. Informatica 34(4), 451–462 (2010)
Baker, R.S.J.d., Goldstein, A.B., Heffernan, N.T.: Detecting the Moment of Learning. Lecture Notes in Computer Science, pp. 25–34 (2010). https://doi.org/10.1007/978-3-642-13388-6_7
Halimi, K., Seridi-Bouchelaghem, H., Faron-Zucker, C.: An enhanced personal learning environment using social semantic web technologies. Interact. Learn. Environ. 22(2), 165–187 (2013). https://doi.org/10.1080/10494820.2013.788032
Melesko, J., Kurilovas, E.: Personalised intelligent multi-agent learning system for engineering courses. In: IEEE 4th Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) (2016). https://doi.org/10.1109/aieee.2016.7821821
Trikha, N., Godbole, A.: Adaptive e-learning system using hybrid approach. International Paper presented at the Conference on Inventive Computation Technologies (ICICT) (2016). https://doi.org/10.1109/inventive.2016.7824844
Al-Omari, M., Carter, J., Chiclana, F.: A hybrid approach for supporting adaptivity in e-learning environments. Int. J. Inf. Learn. Technol. 33(5), 333–348 (2016). https://doi.org/10.1108/ijilt-04-2016-0014
Schiaffino, S., Garcia, P., Amandi, A.: eTeacher: Providing personalized assistance to e-learning students. Comput. Educ. 51(4), 1744–1754 (2008). https://doi.org/10.1016/j.compedu.2008.05.008
Dahbi, A., El kamoun, N., Berraissoul, A.: Conception d’un système hypermédia d’enseignement adaptatif centré sur les styles d’apprentissage: modèle et expérience. Revue Internationale Des Technologies En Pédagogie Universitaire 6(1), 55-71 (2009). https://doi.org/10.7202/039181ar
Kim, J., Lee, A., Ryu, H.: Personality and its effects on learning performance: design guidelines for an adaptive e-learning system based on a user model. Int. J. Ind. Ergon. 43(5), 450–461 (2013). https://doi.org/10.1016/j.ergon.2013.03.001
Baschera, G.M., Gross, M.: Poisson-based inference for perturbation models in adaptive spelling training. Int. J. Artif. Intell. Educ. 20(4), 333–360 (2010)
Surjono, H.D., Maltby, J.R.: Adaptive educational hypermedia based on multiple student characteristics. In: Advances in Web-Based Learning, pp. 442–449 (2003). https://doi.org/10.1007/978-3-540-45200-3_41
Goel, G., Lallé, S., Luengo, V.: Fuzzy Logic Representation for Student Modelling. Lecture Notes in Computer Science 428–433 (2012). https://doi.org/10.1007/978-3-642-30950-2_55
Ricci, F., Rokach, L., Shapira, B.: Recommender Systems: Introduction and Challenges. Recommender Systems Handbook, pp. 1–34 (2015). https://doi.org/10.1007/978-1-4899-7637-6_1
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inf. J. 16(3), 261–273 (2015). https://doi.org/10.1016/j.eij.2015.06.005
Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey. Knowl. Based Syst. 46, 109–132 (2013). https://doi.org/10.1016/j.knosys.2013.03.012
Klašnja-Milićević, A., Ivanović, M., Nanopoulos, A.: Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions. Artif. Intell. Rev. 44(4), 571–604 (2015). https://doi.org/10.1007/s10462-015-9440-z
Drachsler, H., Verbert, K., Santos, O.C., Manouselis, N.: Panorama of recommender systems to support learning. In: Recommender Systems Handbook, pp. 421–451 (2015). https://doi.org/10.1007/978-1-4899-7637-6_12
Dascalu, M.-I., Bodea, C.-N., Mihailescu, M.N., Tanase, E.A., Ordoñez de Pablos, P.: Educational recommender systems and their application in lifelong learning. Behav. Inf. Technol. 35(4), 290–297 (2016). https://doi.org/10.1080/0144929x.2015.1128977
Zaiane, O. R.: Building a recommender agent for e-learning systems. International Paper presented at the Conference on Computers in Education (2002). https://doi.org/10.1109/cie.2002.1185862
Tseng, H.-C., Chiang, C.-F., Su, J.-M., Hung, J.-L., Shelton, B.E.: Building an online adaptive learning and recommendation platform. Lecture Notes in Computer Science, pp. 428–432 (2017). https://doi.org/10.1007/978-3-319-52836-6_45
Fraihat, S., Shambour, Q.: A framework of semantic recommender system for e-Learning. J. Softw. 10(3), 317–330 (2015). https://doi.org/10.17706/jsw.10.3.317-330
Alspector, J., Kolcz, A., Karunanithi, N.: Feature-based and clique-based user models for movie selection: a comparative study. User Model. User-Adap. Inter. 7(4), 279–304 (1997). https://doi.org/10.1023/A:1008286413827
Belarbi, N., Chafiq, N., Talbi, M., Namir, A., Benlahmar, E.: User profiling in a SPOC: a method based on user video clickstream analysis. Int. J. Emerg. Technol. Learn. (2018, in press)
Van der Sluis, F., Ginn, J., Van der Zee, T.: Explaining student behavior at scale: the influence of video complexity on student dwelling. In: Proceedings of the Third ACM Conference on Learning @ Scale - L@S’16 (2016). https://doi.org/10.1145/2876034.2876051
Chorianopoulos, K.: Collective intelligence within web video. Hum.-Centric Comput. Inf. Sci. 3(1), 10 (2013). https://doi.org/10.1186/2192-1962-3-10
Li, N., Kidzinski, L., Jermann, P., Dillenbourg, P.: How do in-video interactions reflect perceived video difficulty?. In: Proceedings of the European MOOCs Stakeholder Summit 2015, No. EPFL-CONF-207968, PAU Education (2015)
Guo, P. J., Kim, J., Rubin, R.: How video production affects student engagement. In: Proceedings of the First ACM Conference on Learning @ Scale Conference - L@S’14, pp. 41–50 (2014). https://doi.org/10.1145/2556325.2566239
Kizilcec, R.F., Piech, C., Schneider, E.: Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. In: Proceedings of the Third International Conference on Learning Analytics and Knowledge – LAK 2013, pp. 170–179 (2013). https://doi.org/10.1145/2460296.2460330
Sinha, T., Jermann, P., Li, N., Dillenbourg, P.: Your click decides your fate: inferring information processing and attrition behavior from MOOC video clickstream interactions. In: Proceedings of the EMNLP 2014 Workshop on Analysis of Large Scale Social Interaction in MOOCs (2014). https://doi.org/10.3115/v1/w14-4102
Kim, J., Guo, P.J., Seaton, D.T., Mitros, P., Gajos, K.Z., Miller, R.C.: Understanding in-video dropouts and interaction peaks in online lecture videos. In: Proceedings of the First ACM Conference on Learning @ Scale Conference - L@S’14 (2014). https://doi.org/10.1145/2556325.2566237
MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1: Statistics, pp. 281–297. University of California Press, Berkeley, Calif. (1967). https://projecteuclid.org/euclid.bsmsp/1200512992
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Belarbi, N., Chafiq, N., Talbi, M., Namir, A., Benlahmar, H. (2019). A Recommender System for Videos Suggestion in a SPOC: A Proposed Personalized Learning Method. In: Farhaoui, Y., Moussaid, L. (eds) Big Data and Smart Digital Environment. ICBDSDE 2018. Studies in Big Data, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-030-12048-1_12
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