process mining in education
15309283
Comparative Process Mining in Education: An Approach Based on Process Cubes
Process mining techniques enable the analysis of a wide variety of processes using event data. For example, event logs can be used to automatically learn a process model (e.g. a Petri net or BPMN model) Next to the automated discovery of the real underlying process, there are process mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards "better" processes. Dozens (if not hundreds) of process mining techniques are available and their value has been proven in many case studies. However, existing techniques focus on the analysis of a single process rather than the comparison of different processes. In this paper, we propose comparative process mining using process cubes. An event has attributes referring to the dimensions of the process cube. Through slicing, dicing, rolling up, and drilling down we can view event data from different angles and produce process mining results that can be compared. To illustrate the process cube concept, we focus on educational data. In particular, we analyze data of students watching video lectures given by the first author. The dimensions of the process cube allow us to compare the process of students that passed the course versus the process of students that failed. We can also analyze differences between male and female students, between different parts of the course, and between Dutch students and international students. The initial analysis provided in this paper is used to elicit requirements for better tool support facilitating comparative process mining.
SIMPDA
Wil M P van der Aalst Shengna Guo Pierre Gorissen
61668337
Process Mining in the Education Domain
Given the ever changing needs of the job markets, education and training centers are increasingly held accountable for student success. Therefore, education and training centers have to focus on ways to streamline their offers and educational processes in order to achieve the highest level of quality in curriculum contents and managerial decisions. Educational process mining is an emerging field in the educational data mining (EDM) discipline, concerned with developing methods to better understand students' learning habits and the factors influencing their performance. It aims, particularly, at discovering, analyzing, and providing a visual representation of complete educational processes. In this paper, in continuity of the work presented in (1) we investigate further the potential, challenges and feasibility of the educational process mining in the field of professional trainings. First, we focus on the mining and the analysis of social networks, from educational event logs, between courses units, resources or training providers. Second, we propose a clustering approach to decompose educational processes following key performance indicators. We have experimented this approach using the ProM Framework.
Awatef Hicheur Cairns Billel Gueni Mehdi Fhima Andrew Cairns Stephane David Nasser Khelifa Paul Dautier
15290678
Exploiting Process Cubes, Analytic Workflows and Process Mining for Business Process Reporting: A Case Study in Education
Business Process Intelligence (BPI) is an emerging topic that has gained popularity in the last decade. It is driven by the need for analysis techniques that allow businesses to understand and improve their processes. One of the most common applications of BPI is reporting, which consists on the struc tured generation of information (i.e. reports) from raw data. In this article, state of the art process mining techniques are used to periodically produce automated reports that relate the actual performance of students of Eindhoven University of Technology to their studying behavior. To avoid the tedious manual repetition of the same process mining procedure for each course, we have designed a work flow calling various process mining techniques using RapidProM. To ensure that the actual students' behavior is related to their actual performance (i.e. grades for courses) our analytic workflows approach leverages on process cubes, which enable the dataset to be sliced and diced based on courses and grades. The article discusses how the approach has been operationalized and what is the structure and concrete results of the reports that have been automatically generated. The reports were sent to lecturers and feedback was collected through an evaluation form. Also, we analyzed an example report to show the range of insights that they provide.
SIMPDA
Alfredo Bolt Massimiliano de Leoni Wil M P van der Aalst Pierre Gorissen
63862426
Web Data Mining in Education: Decision Support by Learning Analytics with Bloom's Taxonomy
Web data mining for extracting meaningful information from large amount of web data has been explored over a decade. The concepts and techniques have been borrowed into the education sector and the new research discipline of learning analytics has emerged. With the development of web technologies, it has been a common practice to design online collaborative learning activities to enhance learning. To apply learning analytics techniques to monitor the online collaborative process enables a lecturer to make instant and informed pedagogical decisions. However, it is still a challenge to build strong connection between learning analytics and learning science for understanding cognitive progression in learning. In this connection, this chapter reports a study to apply learning analytics techniques in the aspect of web usage mining and clustering analysis with underpinning Bloom's taxonomy to analyze students' performance in the online collaborative learning process. The impacts of intermediate interventions are also elaborated.
Wing Shui Ng
18019486
Data mining in education
Applying data mining (DM) in education is an emerging interdisciplinary research field also known as educational data mining (EDM) It is concerned with developing methods for exploring the unique types of data that come from educational environments. Its goal is to better understand how students learn and identify the settings in which they learn to improve educational outcomes and to gain insights into and explain educational phenomena. Educational information systems can store a huge amount of potential data from multiple sources coming in different formats and at different granularity levels. Each particular educational problem has a specific objective with special characteristics that require a different treatment of the mining problem. The issues mean that traditional DM techniques cannot be applied directly to these types of data and problems. As a consequence, the knowledge discovery process has to be adapted and some specific DM techniques are needed. This paper introduces and reviews key milestones and the current state of affairs in the field of EDM, together with specific applications, tools, and future insights. (c) 2012 Wiley Periodicals, Inc.
Wiley Interdiscip. Rev. Data Min. Knowl. Discov.
Cristobal Romero Sebastian Ventura
207759057
When Industry 4.0 meets Process Mining
Abstract Digitization relies on emerging technologies driven by Internet of Things. This not only affects businesses, but also the entire society: smart homes, smart agriculture, and intelligent transportation. The connection of "things" under the same umbrella applies also in education through massive open online courses (MOOCs) Each industry should adapt to the changes brought by Digital Era. This paper makes an introduction to the challenges that come together with Industry 4.0 and proposes Process Mining techniques as a new approach for business process analysis in order to support companies to rapidly adapt to market changes by offering customized solutions for customers. The study provides a brief overview of Industry 4.0 and gradually introduces Process Mining as a purview of Industry 4.0. A case study approach is used to confirm the advantages brought by Process Mining. Different types of graphical visualization describing both control flow and resource perspectives are also presented.
KES
Cristina-Claudia Osman Ana-Maria Ghiran
204094502
Research in Education with Data Mining
Data mining (DM) is popular in education area especially when examining student's learning performances. DM does help education institutions provide high quality education for its leaners. Applying data mining in education also known as educational data mining (EDM) which enables to better understand how student learn and identify how improve educational outcomes. This paper is designed to justify the capabilities of data mining approaches in the field of education. There exit various methods and application in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as objective tend to improving our understanding of the learning process. Several specific algorithms, methods, applications and gaps in the current literature and future insights are discussed here.
Khin Htay Mie Mie Aung Yin Yin Cho Moe Moe Thein
208112458
Evaluating Student Learning Effect Based on Process Mining
As education is taking an increasingly significant role in society today, efficient and precise evaluation of student learning effect is calling for more attention. With recent advances of information technology, learning effect can now be evaluated via mining student's learning process. This paper proposes an interactive student learning effect evaluation framework which focuses on in process learning effect evaluation. In particular, our proposal analyzes students modeling assignment based on their operation records by using techniques of frequent sequential pattern mining, user behavior analysis, and feature engineering. In order to enable effective student learning evaluation and deliver practical value, we have developed a comprehensive online modeling platform to collect operation data of modelers and to support the corresponding analysis. We have carried out a case study, in which we applied our approach to a real dataset, consisting of student online modeling behavior data collected from 24 students majoring in computer science. The results of our analysis show that our approach can effectively and practically mine student modeling patterns and interpret their behaviors, contributing to assessment of their learning effect.
ICAI
Tong Li Congkai Geng Yihan Wang
56718370
Discovering Bottlenecks in a Computer Science Degree through Process Mining techniques
A Higher Education degree is composed by courses which can be organized in areas or modules. Over last years, time invested by students to complete Higher Education degrees has increased. This increment can be caused by the existence of bottlenecks in the courses of academic programs. We aim to carry out an analysis of students' performance to detect courses which represent bottlenecks in the process of completing a degree, because of many student failing compulsory courses. Students' performance can be stored in data sets. Unfortunately, analysis of large data set can lead to scalability problems not being comprehensive applying manual analysis methods because of its extension. We applied Process Mining techniques to overtake these scalability problems. Process Mining is a set of Sequence Analysis techniques to analyze event logs. In this paper, we conducted an analysis of a data set which includes the performance of 612 students applying Process Mining tools. We obtained frequencies of students according time invested to complete a course. We compared these frequencies to detect possible bottlenecks. Finally, some requirements to consider courses as bottlenecks are proposed.
2018 International Symposium on Computers in Education (SIIE)
Juan Antonio Caballero-Hernandez Juan Manuel Dodero Ivan Ruiz-Rube Manuel Palomo-Duarte Jose F Argudo Juan Jose Dominguez-Jimenez
70019288
Process Mining Applied to Player Interaction and Decision Taking Analysis in Educational Remote Games
The usage of Game Based Learning in engineering education, especially games and simulations to foster students interest and motivation, has been gaining attention by researchers. The present study has as main objective to explore possibilities of application of Process Mining techniques in the analysis of interactions among players, and the involved decision taking processes, in non digital games. The analysis was applied to board games and to interactive remote experiments enhanced with ludic elements (remote games, as long as the game structure keeps similarity with board games) Activity logs collected during both board and remote game sessions were analyzed through process mining techniques available in ProM 5.2 software. From results, positive hypothesis were established regarding the applicability of process mining in finding behavior patterns presented by students playing educational games.
REV
Thiago Schaedler Uhlmann Eduardo Alves Portela Santos Luciano Antonio Mendes