Analysis of Learners' Participative Behavior from Active Learning Management by Process Mining Technique

Theptudborvomnun, Chonwarin; Narksarp, Wipavan; Porouhan, Parham; Arpasat, Poohridate; Intarasema, Sarayut; Premchaiswadi, Wichian

doi:10.1109/ictke50349.2020.9289866

Cited by 2 publications

(5 citation statements)

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“…Disco is a process mining technology that runs based on fuzzy miner algorithm [34][35][36][37][38][39] and can automate process discovery, create appealing process diagrams, intelligently automate the processing of raw data, and filter processes from activities or the process path fast regardless of the large or complicated process. It can also display the discovered processes with a bird-eye view and detailed statistical data that can delve into detailed information on individual activities, resources, and data that can be imported into complex CSV and MS Excel files.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The purposive sampling method was used on a sample of online learners who used the e-learning system of Thonburi University for a course (202111 Applied Business Programing) in the first semester (126 students) of the 2021 academic year, from May 28 to October 31, 2021, and in the second semester (121 students) of the 2021 academic year, from November 4, 2021, to April 10, 2022. Thus, the final sample was 247 students [34][35][36][37][38][39].…”

Section: A Populationmentioning

confidence: 99%

“…C. How can we analyze the event log data of students' video learning process via an e-learning system by using process mining techniques, such as fuzzy miner algorithm and dotted chart analysis [34][35][36][37][38][39].…”

Section: A Populationmentioning

confidence: 99%

“…Using these two process mining techniques, we distinguished significant and insightful process differences in the layout (and sequence of performed activities), the type of the executed activities, and the time spent on each task, which were obtained from our raw data. Another reason why a cutoff of 70%, instead of, for example, 90%, was used because we wanted to increase the number of students in the two groups [34][35][36][37][38][39].…”

Section: B Research Toolsmentioning

confidence: 99%

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Creating and Collecting e-Learning Event Logs to Analyze Learning Behavior of Students through Process Mining

Nammakhunt¹,

Porouhan²,

Premchaiswadi³

2023

IJIET

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Many traditional educational management models are being switched or shifted into online platforms; thus, assessing behavioral aspects of learners is essential to improving the quality of online teaching and learning processes. Currently, a problem in managing online teaching of courses is that instructors do not have the appropriate tools and techniques to be fully aware of students’ behavioral patterns in a data-driven and process-aware approach. This study is divided into three main parts. In the first part, a dataset of online students is transformed and preprocessed. In the second part, the Fuzzy Miner algorithm supported by Fluxicon Disco is applied to the dataset to understand the learning process of the students in terms of the duration and length of the tutorial videos watched online (i.e., fully watched, partially watched, paused, and resumed intervals) and in terms of the frequencies of all activities. In the third part, a comparison between behavioral patterns of high-performance group of students versus their low-performance counterparts attending the same course was conducted, and we used the Dotted Chart Analysis technique supported by ProM to conduct and make the comparisons. The results of the study showed significant differences between the two groups in terms of the duration spent on the tutorial videos and in terms of the sequence and order of the activities performed and executed. The findings of the research can be used by instructors, administrators, and educational managers to improve the course curriculum management process or to boost effective coaching and teaching styles, leading to the optimization of students’ learning process by increasing educators’ awareness about students’ weaknesses and strengths.

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Section: Literature Reviewmentioning

confidence: 99%

Section: A Populationmentioning

confidence: 99%

Section: A Populationmentioning

confidence: 99%

Section: B Research Toolsmentioning

confidence: 99%

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Creating and Collecting e-Learning Event Logs to Analyze Learning Behavior of Students through Process Mining

Nammakhunt¹,

Porouhan²,

Premchaiswadi³

2023

IJIET

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“…Process mining is a science that combines machine learning and data mining as well as processing modeling and analysis [12]. Process mining is a method used to generate business process models to find, monitor, and improve real processes by extracting knowledge from event logs, which are available in information systems derived from event log processes [3,11,13,21]. Process mining has three processes, including Discovery, Conformance Checking, and Enhancement [15].…”

Section: Introductionmentioning

confidence: 99%

Conformance Analysis of Student Activities to Evaluate Implementation of Outcome-Based Education in Early of Pandemic using Process Mining

et al. 2022

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The learning management system has a core component of a system event log that contains data on activities carried out by students and lecturers in the system. Educational process mining is a field in educational data mining that is concerned with finding, analyzing, and improving the overall educational process based on information hidden in educational data sets and event logs. The learning process in student lectures through the learning management system will produce a process flow according to the event data. In one semester in the information technology-based study program, the subjects taken are data from programming and non-programming courses in the 5th semester of the information systems department, namely Data Warehouse and Business Intelligence (DWBI) and Enterprise Architecture (EA). The Data Warehouse and Business Intelligence course is chosen because the main role in the graduate profile is as a data engineer. While the Enterprise Architecture course is chosen because being an IT Consultant requires knowledge of EA. Each course has different measured learning outcomes and each course has a different pattern in obtaining learning outcomes. To get a pattern for each learning achievement, an analysis of learning patterns, Bloom’s taxonomy level, and CLO pass scores was carried out using process mining. Course Learning Outcomes (CLO) is a competency standard or minimum qualification criteria for graduates’ abilities which include attitudes, knowledge, and skills assigned to courses. The existence of a bloom level indicates the level of expected learning achievement, where the higher the bloom level, the higher the expected ability. The mining process is carried out using Disco and PROM 5.2. The modeling process uses a heuristic miner algorithm because of its ability to express the main behavior recorded in the event log well. Heuristic miner algorithm can also take into account the frequency of the relationship between activities in the log to determine causal dependencies. The results of this study indicate that there is a difference between those that pass the course learning outcomes and those that do not pass. The passed CLO is indicated by the realization value of passing the course exceeding the threshold of 85.50%, while the failed CLO is indicated by the realization value of course graduation that is less than the threshold. In addition, control-flow, the frequency of activities that are often carried out indicate the appropriate learning path and are carried out by students to achieve a minimal assessment of course learning outcomes. In the Enterprise Architecture course, the activity that has the highest frequency in CLO1 is Attempt Quiz, while in CLO6 is View Course. In the Data Warehouse and Business Intelligence course, the activity that has the highest frequency in CLO3 is View Course, while in CLO4 is Attempt Quiz. The initial activity of the learning pattern produced in the two courses begins always with the view course activity. The highest bloom level in the Data Warehouse and Business Intelligence course is C6 Creation, while in the Enterprise Architecture course is C5 Evaluation. Thus, it can be said that Data Warehouse and Business Intelligence courses have a higher level of difficulty than Enterprise Architecture. Previously, in the DWBI course there was one CLO that failed in its implementation. With this research, it is hoped that this research can have a positive impact on adding new insights regarding the use of event logs in the field of education, so implementation of outcome-based education can be used as a benchmark for student learning to succeed in the course which include attitudes, knowledge, and skills.

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Analysis of Learners' Participative Behavior from Active Learning Management by Process Mining Technique

Cited by 2 publications

References 14 publications

Creating and Collecting e-Learning Event Logs to Analyze Learning Behavior of Students through Process Mining

Creating and Collecting e-Learning Event Logs to Analyze Learning Behavior of Students through Process Mining

Conformance Analysis of Student Activities to Evaluate Implementation of Outcome-Based Education in Early of Pandemic using Process Mining

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