Application of Data Mining and Process Mining approaches for improving e-Learning Processes

Grigorova, Katalina; Malysheva, Elena; Bobrovskiy, Sergey

doi:10.18287/1613-0073-2017-1903-115-121

Cited by 11 publications

(4 citation statements)

References 25 publications

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“…The practical implications of conceptualising a simulation study based on the results of a process mining case in an educational environment are numerous. Based on the processrelated knowledge discovery from extensive records of educational events in the form of new process models that track key performance indicators, it is possible to: analyse the "real" educational processes and their alignment with the curricula; improve the performance indicators of educational processes such as execution time, bottlenecks, decision points, and similar; personalise educational processes by recommending the most suitable learning units or learning paths to students (all depending on their profiles, their preferences or target skills) (Grigorova et al, 2017). In addition to this, by building and using a simulation model based on LMS data, the testing of different what-if scenarios is possible without interfering with the actual educational process.…”

Section: Discussionmentioning

confidence: 99%

Process Mining Contributions to Discrete-event Simulation Modelling

Jadrić

Pašalić

Ćukušić

2020

Business Systems Research Journal

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Background: Over the last 20 years, process mining has become a vibrant research area due to the advances in data management technologies and techniques and the advent of new process mining tools. Recently, the links between process mining and simulation modelling have become an area of interest.Objectives: The objective of the paper was to demonstrate and assess the role of process mining results as an input for discrete-event simulation modelling, using two different datasets, one of which is considered data-poor while the other one data-rich.Methods/Approach: Statistical calculations and process maps were prepared and presented based on the event log data from two case studies (smart mobility and higher education) using a process mining tool. Then, the implications of the results across the building blocks (entities, activities, control-flows, and resources) of simulation modelling are discussed.Results: Apart from providing a rationale and the framework for simulation that is more efficient modelling based on process mining results, the paper provides contributions in the two case studies by deliberating and identifying potential research topics that could be tackled and supported by the new combined approach.Conclusions: Event logs and process mining provide valuable information and techniques that could be a useful input for simulation modelling, especially in the first steps of building discreteevent models, but also for validation purposes.

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Section: Discussionmentioning

confidence: 99%

Process Mining Contributions to Discrete-event Simulation Modelling

Jadrić

Pašalić

Ćukušić

2020

Business Systems Research Journal

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“…The data describes details about the educational procedure offers better insight into learning and enhancing its outputs. [34]. Education Data Mining (EDM) is a diverse disciplines research domain that is associated with producing techniques of research for the data that evolve in a process of learning.…”

Section: Methodsmentioning

confidence: 99%

User Behavioral Data Enhancement and Vectorization in E-Learning Models

Krupa¹

2021

cibg

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The data acquiring mechanism on the criteria of users-interaction to the users-interface has been developed in the initial stage of this applied science research. The input system gets multiple inhomogeneous texts about the interface actions of a specific user, whereas the vector has been demonstrated by the results that describe the user in a consolidated manner. Moreover, the vector collection for various users has been employed as an input of clustering algorithm (i.e. K-means), the outcomes of behaviors of users are among the one of these (k-clusters) that differentiates the users by their behavioral types. Almost, 67.8% of the Interactional data of the interface of the user is accessible to GlobalLab platform users. However, for the electronic diary (i.e.Diary.ru) there is no corresponding data. Although not every user of the GlobalLab system takes the initiative to create the projects, ideas, questionnaire task, and educational resources, the proportion of learners have been 9.7 thousand whose value with all 4 variables varied according to a neutral one.

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“…Segundo [Juhaňák et al 2019], com as técnicas de Minerac ¸ão de Processos -Process Mining (PM), torna-se possível a identificac ¸ão do comportamento estratégico dos alunos. [Grigorova et al 2017] destacam que o PM auxilia no fornecimento de feedback aos tutores de plataformas virtuais, ao se afirmar como suporte para a tomada de decisões que envolvam a melhoria da aprendizagem dos alunos. Com os resultados das análises de determinado conjunto de processos, torna-se possível desenvolver ac ¸ões preventivas que minimizem fatores negativos que impactam nos processos do saber.…”

Section: Minerac ¸ãO De Processosunclassified

Análise da trajetória de aprendizagem do aluno no ensino a distância por meio da mineração de processos

Daniel¹,

Lima²,

Geraldo³

et al. 2022

Anais Do XXXIII Simpósio Brasileiro De Informática Na Educação (SBIE 2022)

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Graças ao uso de ambientes virtuais de aprendizagem na educação a distância, ações de usuários podem ser registradas em logs de eventos, capturando as atividades dos estudantes em diferentes níveis de granularidade. Nesse cenário, aplicações da mineração de processos podem revelar a trajetória dos estudantes, ajudando a descobrir, monitorar e melhorar os processos educacionais. Este artigo apresenta a mineração de processos educacionais de uma universidade virtual brasileira para analisar os processos executados pelos estudantes durante sua trajetória de aprendizagem. A análise das interações entre os estudantes e os módulos de conteúdos mostrou quais atividades geram mais engajamento dos alunos e ajudou a compreender como os alunos aprendem.

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Application of Data Mining and Process Mining approaches for improving e-Learning Processes

Abstract: The article describes the basic principles and methods

Cited by 11 publications

References 25 publications

Process Mining Contributions to Discrete-event Simulation Modelling

Process Mining Contributions to Discrete-event Simulation Modelling

User Behavioral Data Enhancement and Vectorization in E-Learning Models

Análise da trajetória de aprendizagem do aluno no ensino a distância por meio da mineração de processos

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