Measuring Peer Mentoring Effectiveness in Computing Courses: A Case study in Data Analytics for Cybersecurity

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dc.contributor.authorFaridee, Abu Zaher Md
dc.contributor.authorJaneja, Vandana P.
dc.date.accessioned2020-03-26T16:32:26Z
dc.date.available2020-03-26T16:32:26Z
dc.date.issued2020-02-20
dc.description2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW); 17-20 Dec. 2019; Hyderabad, India;en_US
dc.description.abstractComputing courses often suffer from lack of diversity. In this paper we evaluate an intervention method of peer mentoring to help increase interest in data analytics in cybersecurity. We present a text mining approach to analyze student assignments while they undergo a peer mentoring exercise. In our prior work, we have shown that the peer mentoring approach is effective at improving the students' interest in cybersecurity careers and contributes to an overall better knowledge gain throughout the semester. This was also reflected by an improvement in grades with two years of anonymous survey results. Across the years we also observed that peer mentoring is particularly effective in diverse groups. In this paper, we perform text mining of the written assignments for analyzing the group behavior of the control and experiment sections of a class while also documenting the effectiveness of intervention methods such as peer mentoring. We employ a few text mining techniques, namely Text Frequency Analysis, Lexical Diversity, Readability Analysis, Word Cloud Visualization, Hyperlink usage and Objectivity Analysis on the text assignments submitted by the students and show that students who receive peer mentoring are able to express more complex ideas with fewer words and thereby receive higher grades by the end of the semester. Based on these results, we also discuss how our methodology would be applicable in increasing reachability and diversity in other specialized computing courses such as Big Data and distributed systems.en_US
dc.description.sponsorshipThis work is funded by the National Science Foundation Grant#1515358 titled ‘Integrating Cybersecurity with Undergraduate IT Programs’. The authors would like to thank Dr. Aryya Gangopadhyay, Dr. Carolyn Seaman and Amy Everhart for their insights during the peer mentoring study.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/9001705en_US
dc.format.extent7 pagesen_US
dc.genreconference papers and proceedings postprintsen_US
dc.identifierdoi:10.13016/m2t0hu-iqpd
dc.identifier.citationFaridee, Abu Zaher Md; Janeja, Vandana P.; Measuring Peer Mentoring Effectiveness in Computing Courses: A Case study in Data Analytics for Cybersecurity; 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW); https://ieeexplore.ieee.org/document/9001705;en_US
dc.identifier.urihttps://doi.org/10.1109/HiPCW.2019.00024
dc.identifier.urihttp://hdl.handle.net/11603/17649
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rights© 2020 IEEE
dc.titleMeasuring Peer Mentoring Effectiveness in Computing Courses: A Case study in Data Analytics for Cybersecurityen_US
dc.typeTexten_US

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