Proceedings of the 2021 ACM Southeast Conference最新文献

{"title":"Analysis of students' database design skills in capstone projects","authors":"Shuting Xu, Lissa F. Pollacia, Shuhua Lai","doi":"10.1145/3409334.3452081","DOIUrl":"https://doi.org/10.1145/3409334.3452081","url":null,"abstract":"In this paper, we use Association rule learning to analyze the relationship between students' performance in database courses and their performance of database design in the capstone projects. Students need to design and implement databases to store related data in their capstone projects. Some of these databases are well designed, however, some of the designs have common problems such as redundant information, incorrect relationships between tables, etc. Therefore, we want to use Association rules to determine if database related courses affect the quality of students' database design performance in the capstone projects. The interesting strong rules found will be shared with instructors for these courses as teaching references.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129147819","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}

{"title":"Language agnostic model: detecting islamophobic content on social media","authors":"Heena Khan, Joshua L. Phillips","doi":"10.1145/3409334.3452077","DOIUrl":"https://doi.org/10.1145/3409334.3452077","url":null,"abstract":"Social media platforms can struggle to enforce rules preventing online abuse and hate speech due to the large amount of content that must be manually reviewed. Machine learning approaches have been proposed in the literature as a way to automate much of these labors, but social content in multiple languages further complicates this issue. Past work has focused on first building word embeddings in the target language which limits the application of such embeddings to other languages. We use the Google Neural Machine Translator (NMT) to identify and translate Non-English text to English to make the system language agnostic. We can therefore use already available pre-trained word embeddings, instead of training our models and word embeddings in different languages. We have experimented with different word-embedding and classifier pairs as we aimed to assess whether translated English data gives us accuracy comparable to an untranslated English dataset. Our best performing model, SVM with TF-IDF, gave us a 10-fold accuracy of 95.56 percent followed by the BERT model with a 10-fold accuracy of 94.66 percent on the translated data. This accuracy is close to the accuracy of the untranslated English dataset and far better than the accuracy of the untranslated Hindi dataset.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"105 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131913839","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}

{"title":"Virtual app development for adolescents during COVID-19","authors":"Kaylah Mackroy, Kinnis Gosha","doi":"10.1145/3409334.3452089","DOIUrl":"https://doi.org/10.1145/3409334.3452089","url":null,"abstract":"Minority students are not entering computing fields due to inadequate exposure in K-12 curricula. Online computing environments are effective at exposing more minority students to computing concepts before college. An HBCU hosted a virtual camp during COVID-19 to teach minority adolescent students the fundamentals of app development using MIT App Inventor, an app-development platform that allows its users to build fully functional apps for smartphones and tablets. The camp aimed to foster youth innovation and creativity through empowering students to create rather than simply use technology in their lives. Participants in the program showed an increase in wanting to pursue ongoing computing education.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131624022","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}

{"title":"Implementing a network intrusion detection system using semi-supervised support vector machine and random forest","authors":"Sandeep Shah, Pramita Sree Muhuri, Xiaohong Yuan, K. Roy, Prosenjit Chatterjee","doi":"10.1145/3409334.3452073","DOIUrl":"https://doi.org/10.1145/3409334.3452073","url":null,"abstract":"Network security is an important aspect for any organization to keep their information systems secure. A Network Intrusion Detection System (NIDS) is an aid to secure the network by detecting abnormal or malicious traffic. In this paper, we applied a Semi-supervised machine learning approach to design a NIDS. We implemented semi-supervised Support Vector Machine (SVM) and semi-supervised Random Forest (RF) classifiers to classify the NSL-KDD dataset. We have classified the dataset in both binary and multiclass. We have also implemented a Genetic Algorithm (GA) approach to select the optimal features from the original features set. Results show that the random forest algorithm produces a better result than SVM using semi-supervised learning method. Also, the results show that applying the GA in SVM produces a better result than without using GA, and so does using GA in Semi-supervised Random Forest.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115863844","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}

{"title":"Meta-analysis to study the impact of learning engagement strategies in introductory computer programming courses: a multi-institutional study","authors":"Vijayalakshmi Ramasamy, Mourya Reddy Narasareddygari, G. Walia, Andrew A. Allen, D. Duke, J. Kiper, D. Davis","doi":"10.1145/3409334.3452060","DOIUrl":"https://doi.org/10.1145/3409334.3452060","url":null,"abstract":"Various Learning Engagement Strategies (LESs) have been used in CS education to motivate students and facilitate learning. More recently, LESs are being used to support programming pedagogy. Therefore, investigating the influence that the multiple attributes of LESs used as instruction interventions have on students' academic performance is a fertile educational research area. For the past few years, a group of CSEd researchers across three different U.S. institutions have been using a cyberlearning environment (incorporating LESs) to promote student learning and engagement in introductory programming courses. While there have been researches on independent studies on particular LES, the current paper is a meta-analysis of the effectiveness of various combinations of Learning Engagement Strategies (LESs) across different student groups using a series of studies conducted across these three separate institutions over a period of time. Specifically, we investigate the impact of different combinations of LESs such as collaborative learning (CL), gamification (G), and social interaction (SI) embedded in a cyberlearning environment on student understanding of programming concepts in an introductory programming course. In terms of findings that can be generalized across institutions and students, we found that using LESs had a positive impact on student engagement and learning especially when using SI and G in combination.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"330 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123197803","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}

{"title":"Application of back-translation: a transfer learning approach to identify ambiguous software requirements","authors":"Isha Subedi, Maninder Singh, Vijayalakshmi Ramasamy, G. Walia","doi":"10.1145/3409334.3452068","DOIUrl":"https://doi.org/10.1145/3409334.3452068","url":null,"abstract":"Ambiguous requirements are problematic in requirement engineering as various stakeholders can debate on the interpretation of the requirements leading to a variety of issues in the development stages. Since requirement specifications are usually written in natural language, analyzing ambiguous requirements is currently a manual process as it has not been fully automated to meet the industry standards. In this paper, we used transfer learning by using ULMFiT where we pre-trained our model to a general-domain corpus and then fine-tuned it to classify ambiguous vs unambiguous requirements (target task). We then compared its accuracy with machine learning classifiers like SVM, Linear Regression, and Multinomial Naive Bayes. We also used back translation (BT) as a text augmentation technique to see if it improved the classification accuracy. Our results showed that ULMFiT achieved higher accuracy than SVM (Support Vector Machines), Logistic Regression and Multinomial Naive Bayes for our initial data set. Further by augmenting requirements using BT, ULMFiT got a higher accuracy than SVM, Logistic Regression, and Multinomial Naive Bayes classifier, improving the initial performance by 5.371%. Our proposed research provides some promising insights on how transfer learning and text augmentation can be applied to small data sets in requirements engineering.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114230884","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}

{"title":"Automated mapping of fault logs to SRS requirements using key-phrase extraction","authors":"Maninder Singh, G. Walia","doi":"10.1145/3409334.3452043","DOIUrl":"https://doi.org/10.1145/3409334.3452043","url":null,"abstract":"Software requirement specification (SRS) document contains faults due to the inherent ambiguous nature of natural language (NL). These faults are identified and reported (using fault logs) through inspections and are handed back to the requirements author for fixations. This process is very manual, time consuming and a lot of efforts is spent on re-inspection of the SRS document while fault fixations. An automated approach is needed that can map fault logs to faulty requirements and to other similar requirements. The automated approach could enable large fault coverage and can reduce significant manual re-inspection time and efforts. Our proposed approach extracts the key-phrases to identify key problems from fault-logs, and then maps them back to group of similar requirements in an SRS document to inspect requirements that may contain a similar types of faults. Our approach uses key-phrase extraction algorithms, semantic analysis models and clustering approaches to map faults to requirements. We evaluated the mapping of faults to requirements in our approach using two widely used semantic analysis models (i.e., Latent Semantic Analysis and Latent Dirichlet Allocation) with the evaluation performed by the domain expert. Our results have been promising and have showed a large potential to support additional decision making during fault fixations.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133011342","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}

{"title":"Cross courses pedagogy development in data analytics","authors":"Yi Ding, Hyesung Park, Shuhua Lai, Shuting Xu, Yaquan Xu","doi":"10.1145/3409334.3452088","DOIUrl":"https://doi.org/10.1145/3409334.3452088","url":null,"abstract":"Data and Analysis of Data (or Data Analytics, or DA) are becoming increasingly important parts of professional practices within IT (Information Technology) and across the larger domains of business and science. Many dedicated data analytics related programs have been developed to train the future data analyst workforce. Still, growing needs of DA skills and knowledge almost as literacy requirement for many job practices outpace what the dedicated DA programs can supply. DA training often goes beyond dedicated DA curricula. Even in IT field, many non-DA IT courses could involve some DA topics. Currently, there is a lack of study on how to coordinate the pedagogical methods in teaching those DA topics embedded in different courses to deliver needed DA training for our non-DA IT graduates. Here, we explore the possibility of developing a pedagogy framework that helps link the teaching and learning of those fragmented DA topics embedded across different non-DA courses in multidisciplinary study areas of IT field together. In doing so, we expect this interdisciplinary effort to provide a complimentary approach to promote DA education and prepare IT graduates with the DA skills needed in a variety of job requirements. Although our current effort is limited to the IT field this experience can inspire and be transferred to other fields where DA is a part of the prevalent job practices for their DA pedagogy development.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132040104","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}

{"title":"Comparison of algorithm learning tools to assist the education of alabama students","authors":"Brian E Willis, Eric Gamess, Gretchen M. Richards","doi":"10.1145/3409334.3452062","DOIUrl":"https://doi.org/10.1145/3409334.3452062","url":null,"abstract":"With the introduction of the Digital Literacy and Computer Science (DLCS) education standards, there is a need for Alabama teachers at all grade levels to be equipped with software tools that can be used to bolster students' learning of content standards as well as the knowledge and resources to quickly begin leveraging them in the classroom. The purpose of this paper is to examine software solutions that can be adapted for teaching within the scope of DLCS content standards based on, among other characteristics, cost, supplemental resources, verbal and coding language options, and accessibility. It was found that free and open-source tools with a wide range of additional resources exist in abundance. However, these tools generally lack accessibility, either for English as a second language (ESL) or blind/visually impaired users. Furthermore, integration with learning management systems (LMS) is very limited, but in some instances, this is a limitation of the LMS rather than the software, and file submission for assignments ensures these tools are still viable for instructors and schools that rely on an LMS in their classes.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127869394","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}

{"title":"Game development workshops designed and delivered by peer mentors to increase student curiosity and interest in an introductory programming course","authors":"Xin Xu, Wei Jin","doi":"10.1145/3409334.3452046","DOIUrl":"https://doi.org/10.1145/3409334.3452046","url":null,"abstract":"Student motivation in the Programming Fundamentals (CS1) course in our college has long been a problem. Our project utilized both peer modeling and game development to increase students' curiosity for programming and help them enjoy the learning process. Our project involved peer mentors in every aspect of the game development workshops. We guided peer mentors to design, develop and deliver game development workshops. The main goal is to motivate and retain early college IT students, especially traditionally underrepresented groups, such as females and underrepresented minorities (URM) in IT. The second goal is to improve peer mentors' professional development and career readiness. We conducted a pilot study in spring 2020. Shortly after the peer mentors conducted the first workshop in person in classrooms, the classes were moved online due to the COVID pandemic. The remaining workshops were forced to be converted to the online asynchronous format. This adaptation in fact gave us a unique opportunity to observe the difference of the two modalities. Survey data show that our approach was effective at increasing students' curiosity and enjoyment of programming. Female and URM students responded especially well to workshops conducted by peer mentors in the in-person sessions, better than their counterparts. However, the benefit seemed greatly reduced for them when the remaining workshops were forced to be converted to the online asynchronous format when classes were moved online. Our survey data also showed that 91% of students prefer the in-person workshop to the standard class format, while only 56% of students preferred the online asynchronous workshops to a standard programming assignment. The contrast between the two modalities shed light on the elements, such as peer modeling and personal interaction that are especially effective for increasing motivation and improving learning for underrepresented students in IT. A larger scale implementation is in planning and what we learned in the pilot study will help guide the project in the future for both the delivery and assessment of the workshops.","PeriodicalId":148741,"journal":{"name":"Proceedings of the 2021 ACM Southeast Conference","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123351272","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}