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

Ardimento, Pasquale; Bernardi, Mario Luca; Cimitile, Marta; Ruvo, Giuseppe De

doi:10.1109/fuzz-ieee.2019.8859009

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…The majority of papers cover RPA [2-5, 7-9, 20, 22, 24-27, 35]. Four publications [6,10,12,28] focus on software process mining [33]. The remaining two are general approaches to analyzing low-level user interactions with broader applications [11,29].…”

Section: Resultsmentioning

confidence: 99%

“…Examples include clicking a button, entering a string into a text field, ticking a checkbox, or selecting an item from a dropdown [26]. Multiple recent research streams use this type of data, for example to analyze usage patterns in software applications [6,12,28], to identify candidate routines for robotic process automation (RPA) [7,26,35], or to derive RPA automation and test scripts [3,8]. In addition, companies like Celonis and UiPath offer tools that record and process UI data for inspecting and automating task executions [5].…”

Section: Introductionmentioning

confidence: 99%

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A Reference Data Model for Process-Related User Interaction Logs

Abb¹,

Rehse²

2022

Preprint

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User interaction (UI) logs are high-resolution event logs that record low-level activities performed by a user during the execution of a task in an information system. Each event in a UI log corresponds to a single interaction between the user and the interface, such as clicking a button or entering a string into a text field. UI logs are used for purposes like task mining or robotic process automation (RPA), but each study and tool relies on a different conceptualization and implementation of the elements and attributes that constitute user interactions. This lack of standardization makes it difficult to integrate UI logs from different sources and to combine tools for UI data collection with downstream analytics or automation solutions. To address this, we propose a universally applicable reference data model for process-related UI logs. Based on a review of scientific literature and industry solutions, this model includes the core attributes of UI logs, but remains flexible with regard to the scope, level of abstraction, and case notion. We provide an implementation of the model as an extension to the XES interchange standard for event logs and demonstrate its practical applicability in a real-life RPA scenario.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Reference Data Model for Process-Related User Interaction Logs

Abb¹,

Rehse²

2022

Preprint

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“…In another study [10] demonstrated that fuzzy-based process mining techniques can effectively understand students and developer's behaviour during programming sessions and have useful insights to improve the coding process.…”

Section: Review Of Literaturementioning

confidence: 99%

Online Courses Performance Prediction Using Machine Learning Techniques

Dongardive

Pawar

Mishra

2021

Advances in Intelligent Systems and Computing

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The impact of the pandemic has been felt through all sectors of the global society including the field of education. The pandemic is a strong indication of the fact that things will not be the same as they were before. This impact is also seen in the education community. Several institutions across the globe have vouched for a blended learning approach and have adopted VLEs to disembark education. This has given rise to a number of professionally developed as well as amateur types of courses on these platforms. Through this study, the researchers have applied machine learning algorithms to identify the learners with lower engagement and predicting their reasons. A comparison is made between several such algorithms to fine tune the output and understand the interdependency. This study has emphasised on the gender and predictability of course completion on MOOC and there is a significant difference seen in it.

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“…Process models were discovered and used to classify the developers as low-performing and high-performing profiles. With a similar goal, in [64], a different miner algorithm was assessed to obtain complementary results and in [65], developers' profiling was achieved by mining event logs from a web-based cloud IDE.…”

Section: Related Workmentioning

confidence: 99%

Unveiling process insights from refactoring practices

Caldeira,

Abreu,

Cardoso

et al. 2020

Preprint

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Context: Software comprehension and maintenance activities, such as refactoring, are said to be negatively impacted by software complexity. The methods used to measure software product and processes complexity have been thoroughly debated in the literature. However, the discernment about the possible links between these two dimensions, particularly on the benefits of using the process perspective, has a long journey ahead. Objective: To improve the understanding of the liaison of developers' activities and software complexity within a refactoring task, namely by evaluating if process metrics gathered from the IDE, using process mining methods and tools, are suitable to accurately classify different refactoring practices and the resulting software complexity. Method : We mined source code metrics from a software product after a quality improvement task was given in parallel to (117) software developers, organized in (71) teams. Simultaneously, we collected events from their IDE work sessions (320) and used process mining to model their processes and extract the correspondent metrics. Results: Most teams using a plugin for refactoring (JDeodorant) reduced software complexity more effectively and with simpler processes than the ones that performed refactoring using only Eclipse native features. We were able to find moderate correlations (≈43%) between software cyclomatic complexity and process cyclomatic complexity. Using only process driven metrics, we computed ≈30,000 models aiming to predict the type of refactoring method

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

Cited by 16 publications

References 20 publications

A Reference Data Model for Process-Related User Interaction Logs

A Reference Data Model for Process-Related User Interaction Logs

Online Courses Performance Prediction Using Machine Learning Techniques

Unveiling process insights from refactoring practices

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