An Education Process Mining Framework: Unveiling Meaningful Information for Understanding Students’ Learning Behavior and Improving Teaching Quality

Al-Qaheri, Hameed; Panda, Mrutyunjaya

doi:10.3390/info13010029

Cited by 18 publications

(12 citation statements)

References 41 publications

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“…In another article in [19], the authors focus on the study of automated process discovery using the Inductive visual Miner (IvM) and Directly Follows visual Miner (DFvM) algorithms to produce a valid process model for educational process mining to understand and predict the learning behavior of students. However, the performance of process mining algorithms in improving quality education and other benchmarking data sets obtained from alternate data should be explored [19].…”

Section: Related Workmentioning

confidence: 99%

Creating a Data Generator and Implementing Algorithms in Process Analysis

Bakir

Yuzkat

Karabiber

2022

ELEKTRON ELEKTROTECH

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Process mining is a new field of work that aims to meet the need of the business world to improve efficiency and productivity. This field focuses on analysing, discovering, managing, and improving business processes. Process mining uses event logs as a resource and works on this resource. Hence, the system is developed by analysing the event logs, including each step in the process model. Our study is made up of two significant stages: a data generator for processes and algorithms applied for discovering the created processes. In the first stage, the aim was to develop a simulator with the ability to generate data that could help process modelling and development. Within the framework of this study, a system was created that could work with various process models and extract meaningful information from these models. More productive and efficient processes can be developed as a result of his system. The simulator consists of three modules. The first module is the part where users create a process model. In this module, the user can create his own business process model in the system’s interface or select from other registered models. In the second module, team-based data are simulated through these process models. These generated data are used in the third module, called “analysis”, and meaningful information is extracted. In conclusion, the process can be improved considering the information about time, resource, and cost in the generated data. At the second stage, processes were discovered using alpha, heuristic, and genetic algorithms, which are process mining discovery algorithms and synthetic and real event logs. The discovered processes were demonstrated with Petri nets, and the algorithms’ performances were compared using the fitness function, accuracy rates, and running times. In our study, the heuristic algorithm is more successful because it improves the noise in the data and incomplete processes, which are the disadvantages of the alpha algorithm. However, the genetic algorithm yielded more successful results than the alpha and heuristic algorithms due to its genetic operators.

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Section: Related Workmentioning

confidence: 99%

Creating a Data Generator and Implementing Algorithms in Process Analysis

Bakir

Yuzkat

Karabiber

2022

ELEKTRON ELEKTROTECH

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“…Исходные данные, по которым синтезируются модели, обычно получаются из разнообразных электронных образовательных систем [33][34][35].…”

Section: анализ процессов в образовании: обзор литературыunclassified

“…Существенное внимание в области уделяется анализу образовательных траекторий с целью выявления и последующего устранения неудачных вариантов организации образовательного процесса, которые ведут к нежелательным результатам [33,[35][36][37]. Симптомами такой нежелательной организации могут быть, например, общая низкая академическая успеваемость или большое число отчисляющихся по разным причинам студентов.…”

Section: анализ процессов в образовании: обзор литературыunclassified

Analysis of Students’ Academic Performance using LMS Event Logs

Shaimov¹,

Lomazova²,

Mitsyuk³

et al. 2022

Model. anal. inf. sist.

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Modern educational process involves the use of electronic educational environments. These are special information systems that are both a means for storing educational materials and a tool for conducting tests, collecting homework, keeping a grade book, and working together. Such environments produce a large amount of data containing the recorded behavior of students and teachers within the educational process. This paper proposes an approach that allows one to analyze such data and discover typical student trajectories that lead to successful or unsuccessful learning outcomes. It is shown how process mining can be used to build models of the educational process based on the available data. We also show how you can evaluate the extent to which the synthesized model reflects the actual behavior of the system recorded in event logs. The paper contains not only a description of the proposed approach, but also a case study with its application to a real data set for an undergraduate educational program. It is clearly shown how, using our approach, it is possible to find out what factors lead to the formation of successful and unsuccessful student trajectories. The bottlenecks of the educational process were identified, as well as errors in the data, indicating the incorrect operation of the system. As a result of the analysis, points of special attention for administrators of the educational program were identified, as well as some signal events, the appearance of which in a student’s individual trajectory can be an alarm. The application of the approach involves the use of free open source software, which further facilitates its deployment in a variety of educational organizations.

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“…The goal of the process mining techniques is to support organizations by discovering, monitoring, and improving their processes [1]. Process mining has different applications in a variety of fields such as health care [2][3][4], insurance [5], and in manufacturing industry [6]. Process mining consists of three steps: process discovery, conformance checking, and enhancement [7].…”

Section: Introductionmentioning

confidence: 99%

Improving Process Discovery Algorithms Using Event Concatenation

2022

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Process mining is the discipline of analyzing and improving processes which are known as an event log. The real-life event log contains noise, infrequent behaviors, and numerous concurrency, in effect the generated process model through process discovery algorithms will be inefficient and complex. Shortcomings in an event log result in current process discovery algorithms failing to pre-process data and describe real-life phenomena. Existing process mining algorithms are limited based on the algorithm's filtering, parameters, and pre-defined features. It is critical to use a high-quality event log to generate a robust process model. However, pre-processing of the event log is mostly cumbersome and is a challenging procedure. In this paper, we propose a novel pre-processing step aimed to obtain superior quality event log from a set of raw data, consequently a better performing process model. The proposed approach concatenates events which hold concurrent relations based on a probability algorithm, producing simpler and accurate process models. This proposed pre-processing step is based on the probability of the frequency of concurrent events. The performance of the pre-processing approach is evaluated on 18 real-life benchmark datasets that are publicly available. We show that the proposed pre-processing framework significantly reduces the complexity of the process model and improves the model's F-Measure.INDEX TERMS Process mining, concurrent relations, concatenation, pre-processing methods, probability.

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An Education Process Mining Framework: Unveiling Meaningful Information for Understanding Students’ Learning Behavior and Improving Teaching Quality

Cited by 18 publications

References 41 publications

Creating a Data Generator and Implementing Algorithms in Process Analysis

Creating a Data Generator and Implementing Algorithms in Process Analysis

Analysis of Students’ Academic Performance using LMS Event Logs

Improving Process Discovery Algorithms Using Event Concatenation

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