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From ideal to reality: segmentation, annotation, and recommendation, the vital trajectory of intelligent micro learning

  • Jiayin LinEmail author
  • Geng Sun
  • Tingru Cui
  • Jun Shen
  • Dongming Xu
  • Ghassan Beydoun
  • Ping Yu
  • David Pritchard
  • Li Li
  • Shiping Chen
Article
  • 102 Downloads
Part of the following topical collections:
  1. Computational Social Science as the Ultimate Web Intelligence

Abstract

The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation and annotation strategies in the e-learning domain. As the core part of the micro learning service, we further investigate several the state-of-the-art recommendation strategies, such as soft computing, transfer learning, reinforcement learning, and context-aware techniques. From a research contribution perspective, this paper serves as a basis to depict and understand the challenges in the data sources and data mining for the research of micro learning.

Keywords

Micro learning Video segmentation Automatic annotation Recommender system Machine learning Data mining 

Notes

Acknowledgements

This research has been carried out with the support of the Australian Research Council Discovery Project, DP180101051, and Natural Science Foundation of China, no. 61877051, and UGPN RCF 2018-2019 project between University of Wollongong and University of Surrey.

References

  1. 1.
    Achour, H., Zouari, M.: Multilingual learning objects indexing and retrieving based on ontologies, in Computer and Information Technology (WCCIT), 2013 World Congress on, pp. 1–6, (2013)Google Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender systems handbook, pp. 217–253. Springer, Berlin (2011)CrossRefGoogle Scholar
  3. 3.
    Al-Hmouz, A., Shen, J., Al-Hmouz, R., Yan, J.: Modeling and simulation of an adaptive neuro-fuzzy inference system (ANFIS) for mobile learning. IEEE Trans. Learn. Technol. 5, 226–237 (2012)CrossRefGoogle Scholar
  4. 4.
    Al-Shamri, M.Y.H., Bharadwaj, K.K.: Fuzzy-genetic approach to recommender systems based on a novel hybrid user model. Expert Syst. Appl. 35, 1386–1399 (2008)CrossRefGoogle Scholar
  5. 5.
    Anand, D., Bharadwaj, K.K.: Enhancing accuracy of recommender system through adaptive similarity measures based on hybrid features, in Asian Conference on Intelligent Information and Database Systems, pp. 1–10, (2010)Google Scholar
  6. 6.
    Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J.: Engaging with massive online courses," in Proceedings of the 23rd international conference on World Wide Web, pp. 687–698, (2014)Google Scholar
  7. 7.
    Aubert, O., Prié, Y., Canellas, C.: Leveraging video annotations in video-based e-learning, arXiv preprint arXiv:1404.4607, (2014)Google Scholar
  8. 8.
    Baidya E., Goel, S.: LectureKhoj: automatic tagging and semantic segmentation of online lecture videos," in 2014 Seventh international conference on contemporary computing (IC3), pp. 37–43, (2014)Google Scholar
  9. 9.
    Bobadilla, J., Ortega, F., Hernando, A., Alcalá, J.: Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl.-Based Syst. 24, 1310–1316 (2011)CrossRefGoogle Scholar
  10. 10.
    Bolettieri, P., Falchi, F., Gennaro, C., Rabitti, F.: Automatic metadata extraction and indexing for reusing e-learning multimedia objects, in Workshop on multimedia information retrieval on The many faces of multimedia semantics, pp. 21–28, (2007)Google Scholar
  11. 11.
    Campanella, P., Impedovo, S.: Innovative methods for the E-learning recommendation, in Digital Information Processing and Communications (ICDIPC), 2015 Fifth International Conference on, pp. 312–317, (2015)Google Scholar
  12. 12.
    Cernea, D., Del Moral, E., Gayo, J.: SOAF: semantic indexing system based on collaborative tagging. Interdis. J. E-Learn. Learn. Objects. 4, 137–149 (2008)Google Scholar
  13. 13.
    Chen, W.-Y., Zhang, D., Chang, E.Y.: Combinational collaborative filtering for personalized community recommendation," in Proceedings of the 14th ACM SIGKDD International conference on Knowledge discovery and data mining, pp. 115–123, (2008)Google Scholar
  14. 14.
    Chen, W., Niu, Z., Zhao, X., Li, Y.: A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web. 17, 271–284 (2014)CrossRefGoogle Scholar
  15. 15.
    Chen, M., Tong, M., Liu, C., Han, M., Xia, Y.: Recommendation of learning path using an improved ACO based on novel coordinate system," in Advanced Applied Informatics (IIAI-AAI), 2017 6th IIAI International Congress on, pp. 747–753, (2017)Google Scholar
  16. 16.
    Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., Suri, S.: Feedback effects between similarity and social influence in online communities," in Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 160–168, (2008)Google Scholar
  17. 17.
    Dessì, D., Fenu, G., Marras, M., Recupero, D.R.: Bridging learning analytics and cognitive computing for big data classification in micro-learning video collections. Comput. Hum. Behav. 92, 468–477 (2019)CrossRefGoogle Scholar
  18. 18.
    Doush, I.A., Alkhateeb, F., Maghayreh, E.A., Alsmadi, I., Samarah, S.: Annotations, collaborative tagging, and searching mathematics in e-learning, arXiv preprint arXiv:1211.1780, (2012)Google Scholar
  19. 19.
    Du, X., Zhang, F., Zhang, M., Xu, S., Liu, M.: Research on Result Integration Mechanism Based on Crowd Wisdom to Achieve the Correlation of Resources and Knowledge Points, in International Conference on Innovative Technologies and Learning, pp. 568–577, (2018)Google Scholar
  20. 20.
    Fenza, G., Orciuoli, F., Sampson, D.G.: Building adaptive tutoring model using artificial neural networks and reinforcement learning, in Advanced Learning Technologies (ICALT), 2017 IEEE 17th International Conference on, pp. 460–462, (2017)Google Scholar
  21. 21.
    Fong, S., Ho, Y., Hang, Y.: Using genetic algorithm for hybrid modes of collaborative filtering in online recommenders, in Eighth International Conference on Hybrid Intelligent Systems, pp. 174–179, (2008)Google Scholar
  22. 22.
    Grčar, M., Mladenič, D., Fortuna, B., Grobelnik, M.: Data sparsity issues in the collaborative filtering framework, in International Workshop on Knowledge Discovery on the Web, pp. 58–76, (2005)CrossRefGoogle Scholar
  23. 23.
    Guo, P.J., Kim, J., Rubin, R.: How video production affects student engagement: an empirical study of MOOC videos, in Proceedings of the first ACM conference on Learning@ scale conference, pp. 41–50, (2014)Google Scholar
  24. 24.
    Hendez, M., Achour, H.: Keywords extraction for automatic indexing of e-learning resources, in Computer Applications & Research (WSCAR), 2014 World Symposium on, pp. 1–5, (2014)Google Scholar
  25. 25.
    Kim, J., Guo, P.J., Seaton, D.T., Mitros, P., Gajos, K.Z., Miller, R.C.: Understanding in-video dropouts and interaction peaks inonline lecture videos, in Proceedings of the first ACM conference on Learning@ scale conference, pp. 31–40, (2014)Google Scholar
  26. 26.
    Kopeinik, S., Lex, E., Seitlinger, P., Albert, D., Ley, T.: Supporting collaborative learning with tag recommendations: a real-world study in an inquiry-based classroom project, in Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 409–418, (2017)Google Scholar
  27. 27.
    Kovachev, D., Cao, Y., Klamma, R., Jarke, M.: Learn-as-you-go: new ways of cloud-based micro-learning for the mobile Web, in International Conference on Web-Based Learning, pp. 51–61, (2011)CrossRefGoogle Scholar
  28. 28.
    Li, B., Yang, Q., Xue, X.: Transfer learning for collaborative filtering via a rating-matrix generative model, in Proceedings of the 26th annual international conference on machine learning, pp. 617–624, (2009)Google Scholar
  29. 29.
    Li, B., Yang, Q., Xue, X.: Can movies and books collaborate? Cross-domain collaborative filtering for sparsity reduction, in IJCAI, pp. 2052–2057, (2009)Google Scholar
  30. 30.
    Li, H., Wu, D., Tang, W., Mamoulis, N.: Overlapping community regularization for rating prediction in social recommender systems, in Proceedings of the 9th ACM Conference on Recommender Systems, pp. 27–34, (2015)Google Scholar
  31. 31.
    Lin, M., Nunamaker, Jr J.F., Chau, M., Chen, H.: Segmentation of lecture videos based on text: a method combining multiple linguistic features, in International Conference on System Sciences, p. 10003c, (2004)Google Scholar
  32. 32.
    Lin, J., Sun, G., Shen, J., Cui, T., Yu, P., Xu, D. et al.: Towards the readiness of learning analytics data for micro learning, in International Conference on Services Computing, pp. 66–76, (2019)Google Scholar
  33. 33.
    Liu, X., Aberer, K.: SoCo: a social network aided context-aware recommender system, in Proceedings of the 22nd international conference on World Wide Web, pp. 781–802, (2013)Google Scholar
  34. 34.
    Ma, D., Agam, G.: Lecture video segmentation and indexing, in Document Recognition and Retrieval XIX, p. 82970V, (2012)Google Scholar
  35. 35.
    Ma, D., Xie, B., Agam, G.: A machine learning based lecture video segmentation and indexing algorithm, in Document Recognition and Retrieval XXI, p. 90210V, (2014)Google Scholar
  36. 36.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering for improved recommendations. Aaai/iaai. 23, 187–192 (2002)Google Scholar
  37. 37.
    Mittal, A., Krishnan, P.V., Altman, E.: Content classification and context-based retrieval system for e-learning. J. Educ. Technol. Soc. 9, 349–358 (2006)Google Scholar
  38. 38.
    Murray, T., Arroyo, I.: Toward measuring and maintaining the zone of proximal development in adaptive instructional systems, in International Conference on Intelligent Tutoring Systems, pp. 749–758, (2002)CrossRefGoogle Scholar
  39. 39.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 1345–1359 (2010)CrossRefGoogle Scholar
  40. 40.
    Pan, W., Xiang, E.W., Liu, N.N., Yang, Q.: Transfer learning in collaborative filtering for sparsity reduction, in AAAI, pp. 230–235, (2010)Google Scholar
  41. 41.
    Pan, W., Liu, N.N., Xiang, E.W., Yang, Q.: Transfer learning to predict missing ratings via heterogeneous user feedbacks, in IJCAI Proceedings-International Joint Conference on Artificial Intelligence, pp. 2318–2323, (2011)Google Scholar
  42. 42.
    Risk, U.: Draft standard for learning object metadata, IEEE Standard, 1484, (2002)Google Scholar
  43. 43.
    Risko, E.F., Foulsham, T., Dawson, S., Kingstone, A.: The collaborative lecture annotation system (CLAS): a new TOOL for distributed learning. IEEE Trans. Learn. Technol. 6, 4–13 (2013)CrossRefGoogle Scholar
  44. 44.
    Roy, D., Sarkar, S., Ghose, S.: Automatic extraction of pedagogic metadata from learning content. Int. J. Artif. Intell. Educ. 18, 97–118 (2008)Google Scholar
  45. 45.
    Shah, R.R., Yu, Y., Shaikh, A.D., Tang, S., Zimmermann, R.: ATLAS: automatic temporal segmentation and annotation of lecture videos based on modelling transition time, in Proceedings of the 22nd ACM international conference on Multimedia, pp. 209–212, (2014)Google Scholar
  46. 46.
    Shah, R.R., Yu, Y., Shaikh, A.D., Zimmermann, R.: TRACE: linguistic-based approach for automatic lecture video segmentation leveraging Wikipedia texts," in Multimedia (ISM), 2015 IEEE International Symposium on, pp. 217–220, (2015)Google Scholar
  47. 47.
    Shen, L., Wang, M., Shen, R.: Affective e-learning: using" emotional" data to improve learning in pervasive learning environment, J. Educ. Technol. Soc., 12, (2009)Google Scholar
  48. 48.
    Shu, J., Shen, X., Liu, H., Yi, B., Zhang, Z.: A content-based recommendation algorithm for learning resources. Multimedia Systems. 24, 163–173 (2018)CrossRefGoogle Scholar
  49. 49.
    Sikka, R., Dhankhar, A., Rana, C.: A survey paper on e-learning recommender system. Int. J. Comput. Appl. 47, 27–30 (2012)Google Scholar
  50. 50.
    Sun, G., Cui, T., Beydoun, G., Chen, S., Dong, F., Xu, D., Shen, J.: Towards massive data and sparse data in adaptive micro open educational resource recommendation: a study on semantic knowledge base construction and cold start problem. Sustainability. 9, 898 (2017)CrossRefGoogle Scholar
  51. 51.
    Sun, G., Cui, T., Shen, J., Xu, D., Beydoun, G., Chen, S.: Ontological learner profile identification for cold start problem in micro learning resources delivery, in Advanced Learning Technologies (ICALT), 2017 IEEE 17th International Conference on, pp. 16–20, (2017)Google Scholar
  52. 52.
    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, 292–305 (2018)CrossRefGoogle Scholar
  53. 53.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning: an Introduction. MIT press, Cambridge (1998)zbMATHGoogle Scholar
  54. 54.
    Syeda-Mahmood, T., Ponceleon, D.: Learning video browsing behavior and its application in the generation of video previews, in Proceedings of the ninth ACM international conference on Multimedia, pp. 119–128, (2001)Google Scholar
  55. 55.
    Ujjin, S., Bentley, P.J.: Learning user preferences using evolution, in Proceedings of the 4th Asia-Pacific conference on simulated evolution and learning, Singapore, (2002)Google Scholar
  56. 56.
    Ujjin, S., Bentley, P.J.: Particle swarm optimization recommender system, in Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE, pp. 124–131, (2003)Google Scholar
  57. 57.
    Verbert, K., Manouselis, N., Ochoa, X., Wolpers, M., Drachsler, H., Bosnic, I., Duval, E.: Context-aware recommender systems for learning: a survey and future challenges. IEEE Trans. Learn. Technol. 5, 318–335 (2012)CrossRefGoogle Scholar
  58. 58.
    Walter, F.E., Battiston, S., Schweitzer, F.: A model of a trust-based recommendation system on a social network. Auton. Agent. Multi-Agent Syst. 16, 57–74 (2008)CrossRefGoogle Scholar
  59. 59.
    Wasid, M., Ali, R.: Use of soft computing techniques for recommender systems: an overview. In: Applications of Soft Computing for the Web, pp. 61–80. Springer, Berlin (2017)CrossRefGoogle Scholar
  60. 60.
    Wasid, M., Kant, V.: A particle swarm approach to collaborative filtering based recommender systems through fuzzy features. Proc. Comp. Sci. 54, 440–448 (2015)CrossRefGoogle Scholar
  61. 61.
    Wasserman, S., Faust, K.: Social network analysis: Methods and applications, vol. 8. Cambridge university press, Cambridge (1994)CrossRefGoogle Scholar
  62. 62.
    Weibel, S., Kunze, J., Lagoze, C., Wolf, M.: Dublin core metadata for resource discovery, 2070–1721, (1998)Google Scholar
  63. 63.
    Wu, D., Lu, J., Zhang, G.: A fuzzy tree matching-based personalized e-learning recommender system. IEEE Trans. Fuzzy Syst. 23, 2412–2426 (2015)CrossRefGoogle Scholar
  64. 64.
    Wu, J., Zhou, L., Cai, C., Shen, J., Lau, S.K., Yong, J.: Data Fusion for MaaS: Opportunities and Challenges, in 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 642–647, (2018)Google Scholar
  65. 65.
    Yang, H., Meinel, C.: Content based lecture video retrieval using speech and video text information. IEEE Trans. Learn. Technol. 7, 142–154 (2014)CrossRefGoogle Scholar
  66. 66.
    Yang, H., Siebert, M., Luhne, P., Sack, H., Meinel, C.: Automatic lecture video indexing using video OCR technology, in Multimedia (ISM), 2011 IEEE International Symposium on, pp. 111–116, (2011)Google Scholar
  67. 67.
    Zhang, X., Li, C., Li, S.-W., Zue, V.: Automated segmentation of MOOC lectures towards customized learning, in Advanced Learning Technologies (ICALT), 2016 IEEE 16th International Conference on, pp. 20–22, (2016)Google Scholar
  68. 68.
    Zhao, B., Xu, S., Lin, S., Luo, X., Duan, L.: A new visual navigation system for exploring biomedical open educational resource (OER) videos. J. Am. Med. Inform. Assoc. 23, e34–e41 (2015)CrossRefGoogle Scholar
  69. 69.
    Zhao, Q., Zhang, Y., Chen, J.: An improved ant colony optimization algorithm for recommendation of micro-learning path, in Computer and Information Technology (CIT), 2016 IEEE International Conference on, pp. 190–196, (2016)Google Scholar
  70. 70.
    Zhao, B., Lin, S., Luo, X., Xu, S., Wang, R.: A novel system for visual navigation of educational videos using multimodal cues," in Proceedings of the 2017 ACM on Multimedia Conference, pp. 1680–1688, (2017)Google Scholar
  71. 71.
    Zhou, J., Tang, M., Tian, Y., Al-Dhelaan, A., Al-Rodhaan, M., Lee, S.: Social network and tag sources based augmenting collaborative recommender system. IEICE Trans. Inf. Syst. 98, 902–910 (2015)Google Scholar

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Authors and Affiliations

  1. 1.School of Computing and Information TechnologyUniversity of WollongongWollongongAustralia
  2. 2.Research Lab of ElectronicsMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.UQ Business SchoolThe University of QueenslandBrisbaneAustralia
  4. 4.School of Information, System and ModellingThe University of Technology SydneySydneyAustralia
  5. 5.Faculty of Computer and Information ScienceSouthwest UniversityChongqingChina
  6. 6.Data 61, CSIROSydneyAustralia

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