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- W3130216199 abstract "This paper examines IRB-approved student surveys across five cybersecurity and digital forensics courses during Spring 2020 for the benefit of continual course improvement for regulatory requirements such as the Accreditation Board for Engineering and Technology (ABET) (re)accreditation. There is very little research on qualitative machine-learning based methods for the analysis of student feedback in cybersecurity courses, if any courses. This research fills the literature gap by analyzing the feedback from 114 open-ended surveys across five cybersecurity and digital forensic courses to categorize areas of course improvement. To gain insights into the qualitative survey feedback, we employed term frequency-inverse document frequency (TF-IDF) with a K-Means clustering algorithm on cleaned and pre-processed survey data. This methodology provides more useful insights for curriculum developers than a standard sentiment analysis. The methodology can be further extended to be directly integrated into continual course improvement (e.g. ABET, NSA, DoD, etc.) indicators." @default.
- W3130216199 created "2021-03-01" @default.
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- W3130216199 date "2020-12-01" @default.
- W3130216199 modified "2023-09-26" @default.
- W3130216199 title "What are they Reporting? Examining Student Cybersecurity Course Surveys through the Lens of Machine Learning" @default.
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- W3130216199 doi "https://doi.org/10.1109/icmla51294.2020.00156" @default.
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