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Learning from student browsing data on e-learning platforms: case study

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
Języki publikacji
EN
Abstrakty
EN
Interpretation of the behaviors of students in e-learning platforms with machine learning models has become an emerging need in recent years. Increase in the number of registered students on e-learning platforms is one of the reasons for choosing machine learning models. Tracking, modeling and understanding student activities gets more complex when the number of students is increased. This study is focusing modeling student activities on e-learning platforms with Complex Event Processing (CEP), Association Rule Mining (ARM) and Clustering methods based on distributed software architecture. Within the scope of this study, different modules that work real-time have been developed. An admin panel has been also developed in order to control all modules and track the student actions. Performance results of modules also obtained and evaluated on distributed system architecture.
Rocznik
Tom
Strony
37--44
Opis fizyczny
Bibliogr. 41 poz., rys., tab., wykr.
Twórcy
  • Morpa R&D Center, Istanbul, Turkey
autor
  • Morpa R&D Center, Istanbul, Turkey
  • Morpa R&D Center, Istanbul, Turkey
  • YTU Computer Engineering Dept, Istanbul, Turkey
Bibliografia
  • 1. S. Liaw, H. Huang, and G. Chen, “Surveying instructor and learner attitudes toward e-learning,” Computers & Education, vol. 49, no. 4, pp. 1066-1080, 2007.
  • 2. F. Hussain, “E-learning 3.0 = E-learning 2.0 + Web 3.0?”, IADIS International Conference on Cognition and Exploratory Learning in Digital Age, CELDA (Report Paper) , 2012.
  • 3. S. B. Aher, L.M.R.J. Lobo, “Best Combination of Machine Learning Algorithms for Course Recommendation System in E-learning”, International Journal of Computer Applications, 41(6) , 2012.
  • 4. D. C. Luckham, “The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems”, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 2001.
  • 5. G. Cugola and A. Margara, “Processing flows of information,” ACM Computing Surveys, vol. 44, no. 3, pp. 1-62, 2012.
  • 6. N. Mehdiyev, J. Krumeich, D. Enke, D. Werth, and P. Loos, “Determination of Rule Patterns in Complex Event Processing Using Machine Learning Techniques,” Procedia Computer Science, vol. 61, pp. 395-401, 2015.
  • 7. S. Chen, J. Jeng, and H. Chang, “Complex Event Processing using Simple Rule-based Event Correlation Engines for Business Performance Management,” The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (CEC/EEE'06), 2006.
  • 8. F. Esposito, O. Licchelli, G. Semeraro, “Discovering Student Models in e-learning Systems”, Journal of Universal Computer Science, 10 (1), pp. 47-57, 2004.
  • 9. R. Sison, M. Shimura, “Student Modeling and Machine Learning”, International Journal of Artificial Intelligence in Education (IJAIED), 1998 , 9, pp.128-158, 2008.
  • 10. N. Thai-Nghe, T. Horvath, L. Schmidt-Thieme, “Factorization models for forecasting student performance”, Proceedings of the 4th international conference on educational data mining, Eindhoven, The Netherlands, July 62011
  • 11. K. Pliakos, S.-H. Joo, J. Y. Park, F. Cornillie, C. Vens, and W. V. D. Noortgate, “Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems,” Computers & Education, vol. 137, pp. 91-103, 2019.
  • 12. M. Hussain, W. Zhu, W. Zhang, S. M. R. Abidi, and S. Ali, “Using machine learning to predict student difficulties from learning session data,” Artificial Intelligence Review, vol. 52, no. 1, pp. 381-407, 2018.
  • 13. J. Roldán, J. Boubeta-Puig, J. L. Martínez, and G. Ortiz, “Integrating complex event processing and machine learning: An intelligent architecture for detecting IoT security attacks,” Expert Systems with Applications, vol. 149, p. 113251, 2020.
  • 14. M. Cesarini, M. Monga, and R. Tedesco, “Carrying on the e-learning process with a workflow management engine,” Proceedings of the 2004 ACM symposium on Applied computing - SAC '04, 2004.
  • 15. O. Zaiane, “Building a recommender agent for e-learning systems,” International Conference on Computers in Education, 2002. Proceedings., 2002.
  • 16. D. Jin, S. Shi, Y. Zhang, H. Abbas, and T.-T. Goh, “A complex event processing framework for an adaptive language learning system,” Future Generation Computer Systems, vol. 92, pp. 857-867, 2019.
  • 17. Jung, J., Park H., 2020. SAX-ARM: Deviant event pattern discovery from multivariate time series using symbolic aggregate approximation and association rule mining, Expect Systems with Applications, 141, https://doi.org/10.1016/j.eswa.2019.112950
  • 18. K. Vougas, T. Sakellaropoulos, A. Kotsinas, G.-R. P. Foukas, A. Ntargaras, F. Koinis, A. Polyzos, V. Myrianthopoulos, H. Zhou, S. Narang, V. Georgoulias, L. Alexopoulos, I. Aifantis, P. A. Townsend, P. Sfikakis, R. Fitzgerald, D. Thanos, J. Bartek, R. Petty, A. Tsirigos, and V. G. Gorgoulis, “Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining,” Pharmacology & Therapeutics, vol. 203, p. 107395, 2019.
  • 19. Baeth, M.J. et al. (2019). Detecting misinformation in social networks using provenance data, CONCURR COMP-PRACT E, 31(3).
  • 20. Baeth M. J. et al. (2018) An approach to custom privacy policy violation detection problems using big social provenance data, CONCURR COMP-PRACT E, 30(21).
  • 21. Baeth, M.J. et al. (2017). Detecting misinformation in social networks using provenance data, SKG-17.
  • 22. Baeth, M.J. et al. (2015). On the Detection of Information Pollution and Violation of Copyrights in the Social Web, SOCA-15.
  • 23. Dundar, B. et al. (2016) A Big Data Processing Framework for Self Healing Internet of Things Applications, SKG-16.
  • 24. Aktas, M.S. et al. (2019), Provenance aware run-time verification of things for selfhealing Internet of Things applications, CONCURR COMP-PRACT E, http://dx.doi.org/10.1002/cpe.4263.
  • 25. Aktaş M.S., (2018) Hybrid cloud computing monitoring software architecture, CONCURR COMP-PRACT E, 30(21).
  • 26. Aktas M.S. (2019) Detecting Complex Events With Real Time Monitoring Infrastructure On Event-Based Systems, Pamukkale Univ Muh Bilim Derg. 2019; 25(2): 199-207.
  • 27. Fox, G. et al. (2006). Real Time Streaming Data Grid Applications. Distributed Cooperative Laboratories: Networking, Instrumentation, and Measurements. Editors: Davoli, F. Plazzo, S, Zappatore, S., pp. 253-267.
  • 28. Riveni, M. et al. (2019). Application of provenance in social computing: A case study, CONCURR COMP-PRACT E, 31(3).
  • 29. Tas, Y. et al. (2016) An Approach to Standalone Provenance Systems for Big Provenance Data, SKG-16.
  • 30. Aktas M.S. et al. (2010). High performance hybrid information service architecture, CONCURR COMP-PRACT E, 22(15).
  • 31. Aktas M.S., et al. (2008) XML metadata services, CONCURR COMP-PRACT E, 20 (7).
  • 32. Aktas, M.S. et al. (2007). Fault tolerant high-performance Information Services for dynamic collections of Grid and Web services, FUTURE GENER COMP SY, 23(3).
  • 33. Pierce, M.E. et al. (2008). The QuakeSim project: Web services for managing geophysical data and applications, PURE APPL GEOPHYS, 165(3-4).
  • 34. Aydin, G. et al. (2005). SERVOGrid complexity computational environments (CCE) integrated performance analysis, GRID-05.
  • 35. Aktas, M. et al. (2006). iSERVO: Implementing the International Solid Earth Research Virtual Observatory by Integrating Computational Grid and Geographical Information Web Services, PURE APPL GEOPHYS, 163(11-12).
  • 36. Aydin, G. et al (2008). Building and applying geographical information system Grids, CONCURR COMP-PRACT E, 20 (14).
  • 37. Oh, S. et al. (2010) Mobile Web Service Architecture Using Context-store, KSII T Internet Info, Volume 4.
  • 38. Nacar, M.A. et al. (2007) VLab: collaborative Grid services and portals to support computational material science, CONCURR COMP-PRACT E, 19 (12).
  • 39. Aktas, M.S. et al. (2004). A web based conversational case-based recommender system for ontology aided metadata discovery, GRID-04, pp:69-75.
  • 40. Aktas, M, (2007) A Federated Approach to Information Management in Grids. INT J WEB SERV RES, 7(1).
  • 41. Fox, G. et al, (2006) Grids for real time data applications. Parallel Processing and Applied Mathematics, Vol:3911, Book Editors: Wyrzykowski, R and Dongarra, J and Meye, N and Wasniewski, J.
Uwagi
1. This study was supported by Tubitak Teydeb Project ID: 3189164 Grant.
2. Track 4: Information Systems and Technology
3. Technical Session: 26th Conference on Knowledge Acquisition and Management
4. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-861452a8-8e9e-470f-96df-1d4a75d74628
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