Discovering and Mapping LMS Course Usage Patterns to Learning Outcomes

Etinger, Darko

doi:10.1007/978-3-030-39512-4_76

Cited by 7 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…PM techniques were used and outcomes showed that teachers paid attention on the group alerts indicating support need. Etinger [31] applied PM techniques in order to discover LMS Course Usage Patterns. Using a new PM algorithm, Inductive Miner (IM), Bogarín et al [32] tried to discover learning processes under LMS Moodle.…”

Section: Lms Lad and Online Coursesmentioning

confidence: 99%

Reviewing Process Mining Applications and Techniques in Education

Sypsas¹,

Kalles²

2022

IJAIA

View full text Add to dashboard Cite

Process Mining (PM) emerged from business process management but has recently been applied to educational data and has been found to facilitate the understanding of the educational process. Educational Process Mining (EPM) bridges the gap between process analysis and data analysis, based on the techniques of model discovery, conformance checking and extension of existing process models. We present a systematic review of the recent and current status of research in the EPM domain, focusing on application domains, techniques, tools and models, to highlight the use of EPM in comprehending and improving educational processes.

show abstract

Section: Lms Lad and Online Coursesmentioning

confidence: 99%

Reviewing Process Mining Applications and Techniques in Education

Sypsas¹,

Kalles²

2022

IJAIA

View full text Add to dashboard Cite

show abstract

“…The discovered process model is the starting point for evaluating the usage behavior of Moodle LMS. Etinger [42] strived to reveal the relationship between user behavior that creates online course usage patterns and students' grades. Doleck et al [43] applied process mining to the online" modeling and simulation" course at the Faculty of Informatics.…”

Section: B Process Mining In Universitiesmentioning

confidence: 99%

Akıllı Kampüs Konsepti Altında Dijital Süreçler İçin Süreç Madenciliği Metodolojisi

Doğan

TIRPAN

2022

Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

Digital transformation affects universities as well as many industries. Universities are increasingly using various digital resources and systems to manage their knowledge. The smart campus, on the other hand, supports informed decision-making by integrating these resources and systems. Process mining provides real insights for digital transformation, allowing processes to be examined more transparently. This study aims to examine the proposed project implementation processes related to the smart university with the process mining methodology. For this purpose, 32 completed projects submitted to İzmir Bakırçay University Scientific Research Projects Coordinatorship (BAPK) with the proposed methodology adapted from Deming's continuous improvement cycle were examined. The data are taken from two different pages in the project automation system. According to the research findings, Projects are grouped into three categories: Guided Projects (GDM, 5 projects), Graduate Thesis Projects (TEZ, 5 projects), and Career Start Support Projects (KBP, 22 projects). 40.6% (13 projects) of the applications went directly to the project review stage, while 19 (59.4%) needed procedural correction. Considering the time from the creation of the application of 32 projects to the signing of the contract, it is seen that the arithmetic average of the cycle time is 15.1 weeks, and the median average is 52.5 days. The notable difference between arithmetic and median mean is that very few projects are of long duration. Procedural adjustments affect project evaluation cycle time by an additional 14 days. The carelessness or lack of knowledge of the applicants extends the cycle time of the process from 15 days to 53 days. The total duration of unnecessary waiting time in the process is 17 days. This study primarily proposes that non-digital processes should be digitized as soon as possible.

show abstract

A Combined Approach of Process Mining and Rule-Based AI for Study Planning and Monitoring in Higher Education

Wagner

Helal

Roepke

et al. 2023

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

This paper presents an approach of using methods of process mining and rule-based artificial intelligence to analyze and understand study paths of students based on campus management system data and study program models. Process mining techniques are used to characterize successful study paths, as well as to detect and visualize deviations from expected plans. These insights are combined with recommendations and requirements of the corresponding study programs extracted from examination regulations. Here, event calculus and answer set programming are used to provide models of the study programs which support planning and conformance checking while providing feedback on possible study plan violations. In its combination, process mining and rule-based artificial intelligence are used to support study planning and monitoring by deriving rules and recommendations for guiding students to more suitable study paths with higher success rates. Two applications will be implemented, one for students and one for study program designers.

show abstract

Discovering and Mapping LMS Course Usage Patterns to Learning Outcomes

Cited by 7 publications

References 4 publications

Reviewing Process Mining Applications and Techniques in Education

Reviewing Process Mining Applications and Techniques in Education

Akıllı Kampüs Konsepti Altında Dijital Süreçler İçin Süreç Madenciliği Metodolojisi

A Combined Approach of Process Mining and Rule-Based AI for Study Planning and Monitoring in Higher Education

Contact Info

Product

Resources

About