2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)最新文献_第3页

Measuring Peer Mentoring Effectiveness in Computing Courses: A Case Study in Data Analytics for Cybersecurity 测量计算机课程中同伴指导的有效性:网络安全数据分析的案例研究

2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Pub Date : 2019-12-01 DOI: 10.1109/HiPCW.2019.00024

A. Faridee, V. Janeja

{"title":"Measuring Peer Mentoring Effectiveness in Computing Courses: A Case Study in Data Analytics for Cybersecurity","authors":"A. Faridee, V. Janeja","doi":"10.1109/HiPCW.2019.00024","DOIUrl":"https://doi.org/10.1109/HiPCW.2019.00024","url":null,"abstract":"Computing 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.","PeriodicalId":223719,"journal":{"name":"2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128762533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Invited Talk 3: Reskilling to Match the Needs of Exascale Architectures 特邀演讲3:重新培训以满足百亿亿级架构的需求

2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Pub Date : 2019-12-01 DOI: 10.1109/hipcw.2019.00019

Sharma Sharma

引用次数: 0

Anomaly Detection in Surveillance Videos 监控视频中的异常检测

2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Pub Date : 2019-01-03 DOI: 10.1109/HiPCW.2019.00031

Sukalyan Bhakat, Ganesh Ramakrishnan

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引用次数: 20

Message from the Workshops Co-chairs 讲习班联合主席致辞

2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW) Pub Date : 2008-10-12 DOI: 10.1109/WiMob.2008.126

Peter Mueller, M. Denko

引用次数: 0