Pub Date : 2018-10-01DOI: 10.1109/VLHCC.2018.8506481
Kathryn Cunningham
Algorithms and automation run social worlds, support scientific discovery, and even arbitrate economic opportunity. Job opportunities in computer science match this outsized influence: projected job growth in computing dwarfs that of other STEM fields [1]. In recognition of this reality, the movement to expand computing education to all students, including low-income, underrepresented minority, and female students, has grown by leaps and bounds. This has led to computing instruction in K-12, more computing in colleges, and a more diverse set of students to teach.
{"title":"The novice programmer needs a plan","authors":"Kathryn Cunningham","doi":"10.1109/VLHCC.2018.8506481","DOIUrl":"https://doi.org/10.1109/VLHCC.2018.8506481","url":null,"abstract":"Algorithms and automation run social worlds, support scientific discovery, and even arbitrate economic opportunity. Job opportunities in computer science match this outsized influence: projected job growth in computing dwarfs that of other STEM fields [1]. In recognition of this reality, the movement to expand computing education to all students, including low-income, underrepresented minority, and female students, has grown by leaps and bounds. This has led to computing instruction in K-12, more computing in colleges, and a more diverse set of students to teach.","PeriodicalId":444336,"journal":{"name":"2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130345003","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}
Pub Date : 2018-10-01DOI: 10.1109/VLHCC.2018.8506511
D. Long, Kun Wang, Jason Carter, P. Dewan
This work addresses difficulty in web-supported programming. We conducted a lab study in which participants completed a programming task involving the use of the Java Swing/AWT API. We found that information about participant web accesses offered additional insight into the types of difficulties faced and how they could be detected. Difficulties that were not completely solved through web searches involved finding information on AWT/Swing tutorials, 2-D Graphics, Components, and Events, with 2-D Graphics causing the most problems. An existing algorithm to predict difficulty that mined various aspects of programming-environment actions detected more difficulties when it used an additional feature derived from the times when web pages were visited. This result is consistent with our observation that during certain difficulties, subjects had little interaction with the programming environment, they made more web visits during difficulty periods, and the new feature added information not available from features of the modified existing algorithm. The vast majority of difficulties, however, involved no web interaction and the new feature resulted in higher number of false positives, which is consistent with the high variance in web accesses during both non-difficulty and difficulty periods.
{"title":"Exploring the Relationship Between Programming Difficulty and Web Accesses","authors":"D. Long, Kun Wang, Jason Carter, P. Dewan","doi":"10.1109/VLHCC.2018.8506511","DOIUrl":"https://doi.org/10.1109/VLHCC.2018.8506511","url":null,"abstract":"This work addresses difficulty in web-supported programming. We conducted a lab study in which participants completed a programming task involving the use of the Java Swing/AWT API. We found that information about participant web accesses offered additional insight into the types of difficulties faced and how they could be detected. Difficulties that were not completely solved through web searches involved finding information on AWT/Swing tutorials, 2-D Graphics, Components, and Events, with 2-D Graphics causing the most problems. An existing algorithm to predict difficulty that mined various aspects of programming-environment actions detected more difficulties when it used an additional feature derived from the times when web pages were visited. This result is consistent with our observation that during certain difficulties, subjects had little interaction with the programming environment, they made more web visits during difficulty periods, and the new feature added information not available from features of the modified existing algorithm. The vast majority of difficulties, however, involved no web interaction and the new feature resulted in higher number of false positives, which is consistent with the high variance in web accesses during both non-difficulty and difficulty periods.","PeriodicalId":444336,"journal":{"name":"2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126399605","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}
Pub Date : 2018-10-01DOI: 10.1109/VLHCC.2018.8506504
A. Rao, Ayush Bihani, Mydhili K. Nair
Most courses on Data Science offered at universities or online require students to have familiarity with at least one programming language. In this paper, we present, “Milo”, a web-based visual programming environment for Data Science Education, designed as a pedagogical tool that can be used by students without prior-programming experience. To that end, Milo uses graphical blocks as abstractions of language specific implementations of Data Science and Machine Learning(ML) concepts along with creation of interactive visualizations. Using block definitions created by a user, Milo generates equivalent source code in JavaScript to run entirely in the browser. Based on a preliminary user study with a focus group of undergraduate computer science students, Milo succeeds as an effective tool for novice learners in the field of Data Science.
{"title":"Milo: A visual programming environment for Data Science Education","authors":"A. Rao, Ayush Bihani, Mydhili K. Nair","doi":"10.1109/VLHCC.2018.8506504","DOIUrl":"https://doi.org/10.1109/VLHCC.2018.8506504","url":null,"abstract":"Most courses on Data Science offered at universities or online require students to have familiarity with at least one programming language. In this paper, we present, “Milo”, a web-based visual programming environment for Data Science Education, designed as a pedagogical tool that can be used by students without prior-programming experience. To that end, Milo uses graphical blocks as abstractions of language specific implementations of Data Science and Machine Learning(ML) concepts along with creation of interactive visualizations. Using block definitions created by a user, Milo generates equivalent source code in JavaScript to run entirely in the browser. Based on a preliminary user study with a focus group of undergraduate computer science students, Milo succeeds as an effective tool for novice learners in the field of Data Science.","PeriodicalId":444336,"journal":{"name":"2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115320922","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}
Pub Date : 2018-08-27DOI: 10.1109/VLHCC.2018.8506508
Nischal Shrestha, Titus Barik, Chris Parnin
Expertise in programming traditionally assumes a binary novice-expert divide. Learning resources typically target programmers who are learning programming for the first time, or expert programmers for that language. An underrepresented, yet important group of programmers are those that are experienced in one programming language, but desire to author code in a different language. For this scenario, we postulate that an effective form of feedback is presented as a transfer from concepts in the first language to the second. Current programming environments do not support this form of feedback. In this study, we apply the theory of learning transfer to teach a language that programmers are less familiar with-such as R-in terms of a programming language they already know-such as Python. We investigate learning transfer using a new tool called Transfer Tutor that presents explanations for R code in terms of the equivalent Python code. Our study found that participants leveraged learning transfer as a cognitive strategy, even when unprompted. Participants found Transfer Tutor to be useful across a number of affordances like stepping through and highlighting facts that may have been missed or misunderstood. However, participants were reluctant to accept facts without code execution or sometimes had difficulty reading explanations that are verbose or complex. These results provide guidance for future designs and research directions that can support learning transfer when learning new programming languages.
{"title":"It's Like Python But: Towards Supporting Transfer of Programming Language Knowledge","authors":"Nischal Shrestha, Titus Barik, Chris Parnin","doi":"10.1109/VLHCC.2018.8506508","DOIUrl":"https://doi.org/10.1109/VLHCC.2018.8506508","url":null,"abstract":"Expertise in programming traditionally assumes a binary novice-expert divide. Learning resources typically target programmers who are learning programming for the first time, or expert programmers for that language. An underrepresented, yet important group of programmers are those that are experienced in one programming language, but desire to author code in a different language. For this scenario, we postulate that an effective form of feedback is presented as a transfer from concepts in the first language to the second. Current programming environments do not support this form of feedback. In this study, we apply the theory of learning transfer to teach a language that programmers are less familiar with-such as R-in terms of a programming language they already know-such as Python. We investigate learning transfer using a new tool called Transfer Tutor that presents explanations for R code in terms of the equivalent Python code. Our study found that participants leveraged learning transfer as a cognitive strategy, even when unprompted. Participants found Transfer Tutor to be useful across a number of affordances like stepping through and highlighting facts that may have been missed or misunderstood. However, participants were reluctant to accept facts without code execution or sometimes had difficulty reading explanations that are verbose or complex. These results provide guidance for future designs and research directions that can support learning transfer when learning new programming languages.","PeriodicalId":444336,"journal":{"name":"2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116546750","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}
Learning from API documentation and tutorials is challenging for many programmers. Improving the learnability of APIs can reduce this barrier, especially for new programmers. We will use the tools of program analysis to extract key concepts and learning dependencies from API source code, API documentation, open source code, and other online sources of information on APIs. With this information we will generate learning maps for any user-provided code snippet, and will take users through each concept used in the code snippet. Users may also navigate through the most commonly used features of an API without providing a code snippet. We also hope to extend this work to help users find the features of an API they need and also help them integrate that into their code.
{"title":"Using Program Analysis to Improve API Learnability","authors":"Kyle Thayer","doi":"10.1145/3230977.3231009","DOIUrl":"https://doi.org/10.1145/3230977.3231009","url":null,"abstract":"Learning from API documentation and tutorials is challenging for many programmers. Improving the learnability of APIs can reduce this barrier, especially for new programmers. We will use the tools of program analysis to extract key concepts and learning dependencies from API source code, API documentation, open source code, and other online sources of information on APIs. With this information we will generate learning maps for any user-provided code snippet, and will take users through each concept used in the code snippet. Users may also navigate through the most commonly used features of an API without providing a code snippet. We also hope to extend this work to help users find the features of an API they need and also help them integrate that into their code.","PeriodicalId":444336,"journal":{"name":"2018 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127253694","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}
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