Investigating live streaming data for student behaviour modelling
Modelling technology integration in the teaching and learning environment is a complex, uncertain and dynamic practice. A large amount of student behaviour data has been gathered literately for different processing purposes. Yet, considerable questions are still remaining due to the huge data volume, diversification and uncertainty. In this work, we implement a big-data analytical framework for online behaviour modelling, particularly taking streaming data of students' online activity from their laptop usage as an illustrative example. The proposed framework covers details from accessing streaming records to storing heterogeneous data. Furthermore, the work also demonstrates the use of a TF-IDF based feature generation and fuzzy representation strategy to discover critical patterns via this behaviour data. The accuracy of the modelling work is evaluated using students' score on a national-wide test. Experimental results show that the employed TF-IDF feature is much stabler than other traditional features, thereby achieving a better modelling performance. In summary, the simulation result demonstrates the flexibility and applicability of the proposed framework for processing complex behaviour data, and revealing important patterns for decision making.
Yang, J., Ma, J. & Howard, S. K. (2017). Investigating live streaming data for student behaviour modelling. 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 434-439). United States: IEEE.