Page 29 - 2023-Vol19-Issue2

Received: 21 December 2022 | Revised: 21 January 2023 | Accepted: 27 January 2023

DOI: 10.37917/ijeee.19.2.4 Vol. 19 | Issue 2 | December 2023

Open Access

Iraqi Journal for Electrical and Electronic Engineering

Original Article

Fairness Analysis in the Assessment of Several Online

Parallel Classes using Process Mining

Rachmadita Andreswari*1, Ismail Syahputra2
1Department of Information System - Telkom University, Bandung, Indonesia
2Department of Information System - Widyatama University, Bandung, Indonesia

Correspondance
*Rachmadita Andreswari
Department of Information System,
Telkom University,
Bandung,West Java, Indonesia
Email: andreswari@telkomuniversity.ac.id

Abstract
The learning process in online lectures through the Learning Management System (LMS) will produce a learning flow
according to the event log. Assessment in a group of parallel classes is expected to produce the same assessment point
of view based on the semester lesson plan. However, it does not rule out the implementation of each class to produce
unequal fairness. Some of the factors considered to influence the assessment in the classroom include the flow of
learning, different lecturers, class composition, time and type of assessment, and student attendance. The implementation
of process mining in fairness assessment is used to determine the extent to which the learning flow plays a role in the
assessment of ten parallel classes, including international classes. Moreover, a decision tree algorithm will also be
applied to determine the root cause of the student assessment analysis based on the causal factors. As a result, there
are three variables that have effects on student graduation and assessment, i.e attendance, class and gender. Variable
lecturer does not have much impact on the assessment, but has an influence on the learning flow.

Keywords
Fairness Analysis, Process Mining, Parallel Class, Website Development Course.

I. INTRODUCTION The use of machine learning has become an integral part
of educational technology. With a growing number of appli-
Online learning has changed paradigms and perspectives cations using machine learning modeling such as student per-
in education. Online learning activities provide very useful formance prediction, course recommendations, and dropout
data to improve the quality, process, and learning methods. predictions, there are concerns about model bias and inequity.
This improvement has the aim of producing graduates who Unfair models lead to unfair outcomes for learning outcomes
have the required competencies. By implementing machine [1]. The application of these techniques is also not always
learning and process mining, data from learning activities can acceptable. In certain situations, unfair diagnoses lead to un-
be used to determine student learning styles, predict student fair conclusions and discrimination [2]. Moreover, research
performance, course recommendations, and predict student on the implementation of process mining has carried out a lot
drop-outs. The implementation of machine learning, data min- of participatory analysis on active learning [3], prediction of
ing or mining processes on online learning data has resulted assessment based on the use of LMS based on petri net [4] and
in a lot of research, frameworks and applications that help the suitability between lecturer planning and use of LMS to
education in developing learning methods, improving learn- improve learning ability [5] [6]. Overall, the research frame-
ing experiences, and predicting student performance and even work related to process mining in the field of education has
predicting student drop-outs.

This is an open-access article under the terms of the Creative Commons Attribution License,
which permits use, distribution, and reproduction in any medium, provided the original work is properly cited.
©2023 The Authors.
Published by Iraqi Journal for Electrical and Electronic Engineering | College of Engineering, University of Basrah.

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