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Register a new userAnalyzing Student Strategies In Blended Courses Using Clickstream Data
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Added by
Nil-Jana Akpinar
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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No Arabic abstract
Educational software data promises unique insights into students study behaviors and drivers of success. While much work has been dedicated to performance prediction in massive open online courses, it is unclear if the same methods can be applied to blended courses and a deeper understanding of student strategies is often missing. We use pattern mining and models borrowed from Natural Language Processing (NLP) to understand student interactions and extract frequent strategies from a blended college course. Fine-grained clickstream data is collected through Diderot, a non-commercial educational support system that spans a wide range of functionalities. We find that interaction patterns differ considerably based on the assessment type students are preparing for, and many of the extracted features can be used for reliable performance prediction. Our results suggest that the proposed hybrid NLP methods can provide valuable insights even in the low-data setting of blended courses given enough data granularity.
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Data-driven decision making is serving and transforming education. We approached the problem of predicting students performance by using multiple data sources which came from online courses, including one we created. Experimental results show preliminary conclusions towards which data are to be considered for the task.
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