Analysis of source code snapshot granularity levels

Vihavainen, Arto; Luukkainen, Matti; Ihantola, Petri

doi:10.1145/2656450.2656473

Cited by 19 publications

(14 citation statements)

References 22 publications

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“…Though our results do not reach the accuracy levels of some other bestperforming classifiers used with freely typed text, we have showed that a reasonable accuracy can be achieved also in the context of programming. As the need for new identification methods increases with the amount of online courses, and storing data with finer granularity has become increasingly popular [32], these results are important and topical.…”

Section: Discussionmentioning

confidence: 99%

Identification of programmers from typing patterns

Longi

Leinonen

Nygren

et al. 2015

Proceedings of the 15th Koli Calling Conference on Computing Education Research

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Being able to identify the user of a computer solely based on their typing patterns can lead to improvements in plagiarism detection, provide new opportunities for authentication, and enable novel guidance methods in tutoring systems. However, at the same time, if such identification is possible, new privacy and ethical concerns arise. In our work, we explore methods for identifying individuals from typing data captured by a programming environment as these individuals are learning to program. We compare the identification accuracy of automatically generated user profiles, ranging from the average amount of time that a user needs between keystrokes to the amount of time that it takes for the user to press specific pairs of keys, digraphs. We also explore the effect of data quantity and different acceptance thresholds on the identification accuracy, and analyze how the accuracy changes when identifying individuals across courses. Our results show that, while the identification accuracy varies depending on data quantity and the method, identification of users based on their programming data is possible. These results indicate that there is potential in using this method, for example, in identification of students taking exams, and that such data has privacy concerns that should be addressed.

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Section: Discussionmentioning

confidence: 99%

Identification of programmers from typing patterns

Longi

Leinonen

Nygren

et al. 2015

Proceedings of the 15th Koli Calling Conference on Computing Education Research

Self Cite

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“…Vihavainen et al [13] have investigated the e ect of storing student data of di erent granularity: submission-level, snapshotlevel (e.g. compiling, saving), and keystroke-level (e.g.…”

Section: Threats To Validitymentioning

confidence: 99%

Code Quality Issues in Student Programs

Keuning

Heeren

Jeuring

2017

Proceedings of the 2017 ACM Conference on Innovation and Technology in Computer Science Education

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Because low quality code can cause serious problems in software systems, students learning to program should pay attention to code quality early. Although many studies have investigated mistakes that students make during programming, we do not know much about the quality of their code. This study examines the presence of quality issues related to program ow, choice of programming constructs and functions, clarity of expressions, decomposition and modularization in a large set of student Java programs. We investigated which issues occur most frequently, if students are able to solve these issues over time and if the use of code analysis tools has an e ect on issue occurrence. We found that students hardly x issues, in particular issues related to modularization, and that the use of tooling does not have much e ect on the occurrence of issues.

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“…Different researchers have taken different approaches to capturing or recording interactions between learners and programming environments [1-3, 11, 14, 18, 26, 31, 36, 38, 43, 48, 54, 57, 62, 63, 65, 72-74, 83, 86, 88, 93, 96, 100, 103, 113, 118]. First, we can roughly describe the differences in terms of granularity-approximately referring to the size or frequency of the events, or how frequently the state of the solution is captured [110]. Figure 3 depicts the most common points on the granularity spectrum from smallest (individual key strokes) to largest (complete assignment submissions to some form of assessment or feedback system).…”

Section: How Data Are Collectedmentioning

confidence: 99%

Educational Data Mining and Learning Analytics in Programming

Ihantola

Vihavainen

Ahadi

et al. 2015

Proceedings of the 2015 ITiCSE on Working Group Reports

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150

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Educational data mining and learning analytics promise better understanding of student behavior and knowledge, as well as new information on the tacit factors that contribute to student actions. This knowledge can be used to inform decisions related to course and tool design and pedagogy, and to further engage students and guide those at risk of failure. This working group report provides an overview of the body of knowledge regarding the use of educational data mining and learning analytics focused on the teaching and learning of programming. In a literature survey on mining students' programming processes for 2005-2015, we observe a significant increase in work related to the field. However, the majority of the studies focus on simplistic metric analysis and are conducted within a single institution and a single * Working group leaders Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Analysis of source code snapshot granularity levels

Cited by 19 publications

References 22 publications

Identification of programmers from typing patterns

Identification of programmers from typing patterns

Code Quality Issues in Student Programs

Educational Data Mining and Learning Analytics in Programming

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