Sequential Pattern Mining Model of Performing Video Learning History Data to Extract the Most Difficult Learning Subjects
Klíčová slova:
Sequential Pattern Mining (SPM), Video, Learning, Keyword Topic (KT)Abstrakt
The paper aim is to define a method for performing video learning data history of learner’s video watching logs, video segments or time series data in consistency with learning processes. To achieve this aim, a theoretical method is introduced. Sequential pattern mining with learning histories are used to extract the most difficult learning subjects. Based on this method, it is designed a model for understanding and learning the most difficult topics of students. The performed video learning history data of learner’s video watching logs makeup of stop/replay/backward data activities functions. They correspond as output of sequence of the learning histories, extraction of significant patterns by a set of sequences, and findings of learner’s most difficult/important topic from the extracted patterns. The paper mostly aim to devise the model for understanding and learning the most difficult topics through method of mining sequential pattern.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Reference
Al-Shabandar, R., Hussain, A., Laws, A., Keight, R., Lunn, J., Radi, N. (2017), “Machine learning approaches to predict learning outcomes in massive open online courses”, in proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), 14-19 May 2017, Anchorage, AK, USA, IEEE, pp. 713-720.
Bell, D. A., Beck, A., Miller, P., Wu, Q. X., Herrera, A. (2007), “Video Mining-Learning Patterns of Behaviour via an Intelligent Image Analysis System”, in proceedings of the 7th International Conference on Intelligent Systems Design and Applications (ISDA 2007), 20-24 October 2007, Rio de Janiero, Brazil, IEEE, pp. 460-464.
Lodhi, S. S. (2014), “Development of Sequential ID3:“An advance Sequential mining Algorithm””. American Journal of Software Engineering, Vol. 2, No. 2, pp. 16-21.
Mabroukeh, N. R., Ezeife, C. I. (2010), “A taxonomy of sequential pattern mining algorithms”, ACM Computing Surveys (CSUR), Vol. 43, No. 1, p. 3.
Nakamura, S., Nozaki, K., Morimoto, Y., Miyadera, Y. (2014), “Sequential pattern mining method for analysis of programming learning history based on the learning processs”, in proceedings of the 2014 International Conference on Education Technologies and Computers (ICETC), 22-24 September 2014, Lodz, Poland, IEEE, pp. 55-60.
Nakamura, S., Nozaki, K., Nakayama, H., Morimoto, Y., Miyadera, Y. (2015), “Sequential Pattern Mining System for Analysis of Programming Learning History”, in proceedings of 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS), 11-13 December 2015, Sydney, NSW, Australia, IEEE, pp. 69-74.
Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaïane, O. R. (2009), “Clustering and sequential pattern mining of online collaborative learning data”, IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 6, pp. 759-772.
Prakash, B. R., Hanumanthappa, M., Kavitha, V. (2014), “Big data in educational data mining and learning analytics”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 2, No. 12, pp. 7515-7520.
Ratnapala, I. P., Ragel, R. G., Deegalla, S. (2014), “Students behavioural analysis in an online learning environment using data mining”, in 7th International Conference on Information and Automation for Sustainability, 22-24 December 2014, Colombo, Sri Lanka, IEEE, pp. 1-7.
Srikant R., Agrawal R. (1996), “Mining sequential patterns: Generalizations and performance improvements”, in Apers P., Bouzeghoub M., Gardarin G. (Eds.), Advances in Database Technology — EDBT '96, EDBT 1996, Lecture Notes in Computer Science, Vol. 1057, Springer, Berlin, Heidelberg.
Shanabrook, D. H., Cooper, D. G., Woolf, B. P., Arroyo, I. (2010), “Identifying high-level student behavior using sequence-based motif discovery”, in proceedings of the 3rd International Conference on Educational Data Mining, 11-13 June 2010, Pittsburgh, PA, USA, pp. 191-200.
Yadav, A., Jain, S. (2011), “Analyses of web usage mining techniques to enhance the capabilities of E-learning environment”, in proceedings of the 2011 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), 22-24 April 2011, Udaipur, India, IEEE, pp. 223-225.
