An empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free$\backslash$libre open source software (FLOSS) communities

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TitleAn empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free$\backslash$libre open source software (FLOSS) communities
Publication TypeJournal Article
Year of Publication2017
AuthorsMukala, P, Cerone, A, Turini, F
JournalEducation and Information Technologies
Pagination1–23
AbstractFree\Libre Open Source Software (FLOSS) environments are increasingly dubbed as learning environments where practical software engineering skills can be acquired. Numerous studies have extensively investigated how knowledge is acquired in these environments through a collaborative learning model that define a learning process. Such a learning process, identified either as a result of surveys or by means of questionnaires, can be depicted through a series of graphical representations indicating the steps FLOSS community members go through as they acquire and exchange skills. These representations are referred to as a-priori learning models. They are Petri net-like workflow nets (WF-net) that provide a visual representation of the learning process as it is expected to occur. These models are representations of a learning framework or paradigm in FLOSS communities. As such, the credibility of any models is estimated through a process of model verification and validation. Therefore in this paper, we analyze these models in comparison with the real behavior captured in FLOSS repositories by means of conformance verification in process mining. The purpose of our study is twofold. Firstly, the results of our analysis provide insights on the possible discrepancies that are observed between the initial theoretical representations of learning processes and the real behavior captured in FLOSS event logs, constructed from mailing archives. Secondly, this comparison helps foster the understanding on how learning actually takes place in FLOSS environments based on empirical evidence directly from the data.
DOI10.1007/s10639-017-9573-6