Comprehension of how students and developers head the development of software and what specific hurdles they face, have a strong potential to better support the coding workflow. In this paper, we present the CodingMiner environment to generate event logs from IDE usage enabling the adoption of fuzzy-based process mining techniques to model and to study the developers' coding process. The logs from the development sessions have been analyzed using the fuzzy miner to highlight emergent and interesting developers' and students' behaviors during coding. The mined processes show different IDE usage patterns for students with different skills and performances. To validate our approach, we describe the results of a study in which the CodingMiner environment is used to investigate the coding activities of twenty students of a CS2 course performing a given programming task during four assignments. Results also demonstrate that fuzzy-based process mining techniques can be effectively exploited to understand students and developers behavior during programming tasks providing useful insights to improve the way they code.

Learning analytics to improve coding abilities: A fuzzy-based process mining approach

Ardimento P.;Cimitile M.;
2019

Abstract

Comprehension of how students and developers head the development of software and what specific hurdles they face, have a strong potential to better support the coding workflow. In this paper, we present the CodingMiner environment to generate event logs from IDE usage enabling the adoption of fuzzy-based process mining techniques to model and to study the developers' coding process. The logs from the development sessions have been analyzed using the fuzzy miner to highlight emergent and interesting developers' and students' behaviors during coding. The mined processes show different IDE usage patterns for students with different skills and performances. To validate our approach, we describe the results of a study in which the CodingMiner environment is used to investigate the coding activities of twenty students of a CS2 course performing a given programming task during four assignments. Results also demonstrate that fuzzy-based process mining techniques can be effectively exploited to understand students and developers behavior during programming tasks providing useful insights to improve the way they code.
978-1-5386-1728-1
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11586/250986
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