Abstract
The advances in MOOCs, Web learning communities, social media platforms and mobile learning apps have been witnessed in recent few years. With the development of these applications and systems, the significant growth of learning resources with multimodalities (e.g., web pages, e-books, lecture videos) has greatly changed the way people learn new knowledge and skills. However, this results in the problem of information overload as learners are overwhelmed by the rich learning resources that accompany the ever developing technologies. In other words, it is increasingly difficult for learners to find required learning materials efficiently and effectively when they confront such a large volume of data. To tackle this problem, it is essential to build a powerful framework to organize e-learning resources and capture learning preferences. In this paper, we therefore propose a graph-based framework to achieve these intended outcomes by integrating various hidden relationships among learners, users and resources. Throughout the case studies, we have verified that the proposed framework is very flexible and powerful to support various kinds of e-learning applications in different scales.
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References
Xie, H., Li, Q., Mao, X., Li, X., Cai, Y., Rao, Y.: Community-aware user profile enrichment in folksonomy. Neural Netw. 58, 111–121 (2014)
Zou, D., Xie, H., Li, Q., Wang, F.L., Chen, W.: The load-based learner profile for incidental word learning task generation. In: Popescu, E., Lau, R.W., Pata, K., Leung, H., Laanpere, M. (eds.) ICWL 2014. LNCS, vol. 8613, pp. 190–200. Springer, Heidelberg (2014)
Xie, H., Zou, D., Lau, Y.K.R., Wang, F., Wong, T.L.: Generating incidental word-learning tasks via topic-based and load-based profiles. IEEE Multimedia 23(1), 60–70 (2016)
Admiraal, W., Huizenga, J., Akkerman, S., Dam, G.T.: The concept of flow in collaborative game-based learning. Comput. Hum. Behav. 27(3), 1185–1194 (2011)
Chen, Z.H., Chen, S.Y.: A surrogate competition approach to enhancing game-based learning. ACM Trans. Comput. Hum. Interact. (TOCHI) 20(6), 35 (2013)
Yessad, A., Carron, T., Labat, J.-M.: An approach to model and validate scenarios of serious games in the design stage. In: Wang, J.-F., Lau, R. (eds.) ICWL 2013. LNCS, vol. 8167, pp. 264–273. Springer, Heidelberg (2013)
Fu, F.L., Su, R.C., Yu, S.C.: EGameFlow: a scale to measure learners’ enjoyment of e-learning games. Comput. Educ. 52(1), 101–112 (2009)
Sweetser, P., Wyeth, P.: GameFlow: a model for evaluating player enjoyment in games. Comput. Entertain. (CIE) 3(3), 3 (2005)
Johnson, W.L., Rickel, J.: Steve: an animated pedagogical agent for procedural training in virtual environments. ACM SIGART Bull. 8(1–4), 16–21 (1997)
Choi, S., Clark, R.E.: Cognitive and affective benefits of an animated pedagogical agent for learning English as a second language. J. Educ. Comput. Res. 34(4), 441–466 (2006)
Lane, H., Cahill, C., Foutz, S., Auerbach, D., Noren, D., Lussenhop, C., Swartout, W.: The effects of a pedagogical agent for informal science education on learner behaviors and self-efficacy. In: Lane, H., Yacef, K., Mostow, J., Pavlik, P. (eds.) AIED 2013. LNCS, vol. 7926, pp. 309–318. Springer, Heidelberg (2013)
Lu, J.: Personalized e-learning material recommender system. In: International Conference on Information Technology for Application, pp. 374–379 (2004)
Chen, C.M., Lee, H.M., Chen, Y.H.: Personalized e-learning system using item response theory. Comput. Educ. 44(3), 237–255 (2005)
Klašnja-Milićević, A., Vesin, B., Ivanović, M., Budimac, Z.: E-learning personalization based on hybrid recommendation strategy and learning style identification. Comput. Educ. 56(3), 885–899 (2011)
Romero, C., Ventura, S., Zafra, A., Bra, P.D.: Applying web usage mining for personalizing hyperlinks in web-based adaptive educational systems. Comput. Educ. 53(3), 828–840 (2009)
Zhao, J., Forouraghi, B.: An interactive and personalized cloud-based virtual learning system to teach computer science. In: Wang, J.-F., Lau, R. (eds.) ICWL 2013. LNCS, vol. 8167, pp. 101–110. Springer, Heidelberg (2013)
Xie, H., Zou, D., Wong, T.L., Wang, F.: A versatile learning context framework for heterogeneous e-learning applications. In: GCCCE 2016 (2016)
Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Effects of user similarity in social media. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 703–712. ACM (2012)
Ma, H., Zhu, J., Lyu, M.T., King, I.: Bridging the semantic gap between image contents and tags. IEEE Trans. Multimedia 12(5), 462–473 (2010)
Leung, E.W.C., Li, Q.: An experimental study of a personalized learning environment through open-source software tools. IEEE Trans. Educ. 50(4), 331–337 (2007)
Pearson, K.: The problem of the random walk. Nature 72(1865), 294 (1905)
Acknowledgement
The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14), the Internal Research Grant (RG 30/2014-2015) of the Hong Kong Institute of Education and a grant from the Soft Science Research Project of Guangdong Province (Grant No. 2014A030304013).
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Zou, D., Xie, H., Wong, TL., Wang, F.L., Wu, Q. (2016). The Augmented Hybrid Graph Framework for Multi-level E-Learning Applications. In: Cheung, S., Kwok, Lf., Shang, J., Wang, A., Kwan, R. (eds) Blended Learning: Aligning Theory with Practices . ICBL 2016. Lecture Notes in Computer Science(), vol 9757. Springer, Cham. https://doi.org/10.1007/978-3-319-41165-1_32
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