The impact of class absenteeism on student’s academic performance using regression models

Ahmad, Nurhafizah; Ul-Saufie, Ahmad Zia; Mohamed, Siti Asmah; Ahmat, Hasfazilah; Zahari, Mohd Fahmi

doi:10.1063/1.5041712

Cited by 3 publications

(3 citation statements)

References 12 publications

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“…Ahmad [1] shows throughout his research, absenteeism had a significant impact on academic performance. Their results showed that there was a modest negative association between the absenteeism along with the final achievement of assessments.…”

Section: Literature Reviewmentioning

confidence: 98%

Identifying the Dependency Outline of Students’ Attendance in Tertiary Level using Markov Chain and Logistic Regression Model

Suraiya¹,

Debnath²,

Anam³

2020

IJEAT

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The schooling of tertiary level is a source of culture for any nation and this issue has always been a major public concern. The success of this level depends on various issues that can influence the students to gather knowledge. Among them, class participation is vital for understanding the course contents. The education system of Bangladesh is almost sound and well-organized in all levels including tertiary stage. Even so sometimes, students are unwilling to attend in the classroom for a number of reasons. In this study, we measured how previous absence of a student impact on their further absence. Markov Chain is a mathematical tool that identified the chance of previous absence effect on the recent absent. In another methods, logistic regression showed the dependency of today’s absence on previous absence. In this work, we got, the previous day absence influenced the student to continue his/her absent on the current day. Therefore, students’ continuous class participation is important and any sorts of discontinuity makes a barrier to participate in the next class

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Section: Literature Reviewmentioning

confidence: 98%

Identifying the Dependency Outline of Students’ Attendance in Tertiary Level using Markov Chain and Logistic Regression Model

Suraiya¹,

Debnath²,

Anam³

2020

IJEAT

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“…Lastly, Ref. [48] found the impact of absences to lower grades in a calculus class. Predominantly, incurred absences have negative impact to course grades.…”

Section: Case Studymentioning

confidence: 98%

A Rule Induction Framework on the Effect of ‘Negative’ Attributes to Academic Performance

Gue

Sy²,

Nuñez³

et al. 2021

Int. J. Emerg. Technol. Learn.

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Attaining high retention rates among engineering institutions is a predominant is-sue. A significant portion of engineering students face challenges of retention. Academic advising was implemented to resolve the issue. Decision support sys-tems were developed to support the endeavor. Machine learning have been inte-grated among such systems in predicting student performance accurately. Most works, however, rely on a black box model approach. Rule induction generates simpler if-then rules, exhibiting clearer understanding. As most research works considered attributes for positive academic performance, there is the need to con-sider ‘negative’ attributes. ‘Negative’ attributes are critical indicators to possibility of failure. This work applied rule induction techniques for course grade predic-tion using ‘negative’ attributes. The dataset is the academic performance of 48 mechanical engineering students taking a machine design course. Students’ at-tributes on workload, course repetition, and incurred absences are the predictors. This work implemented two rule induction techniques, rough set theory (RST) and adaptive neuro fuzzy inference system (FIS). Both models attained a classifi-cation accuracy of 70.83% with better performance for course grades of ‘Pass’ and ‘High’. RST generated 16 crisp rules while ANFIS generated 27 fuzzy rules, yielding significant insights. Results of this study can be used for comparative analysis of student traits between institutions. The illustrated framework can be used in formulating linguistic rules of other institutions.

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“…Various studies have shown that successful students have a better attendance record as well as a higher grade. According to Ahmat and Zahari's research, there is a negative relationship between absenteeism and grades, implying that more absence equals a lower grade [1]. The study looks into the link between students' attendance in class and their total marks in a variety of computer science programmes [2].…”

Section: Introductionmentioning

confidence: 99%

A Study of Relationship to Absentees and Score Using Machine Learning Method: A Case Study of Linear Regression Analysis

Yadav

2022

IARS INT RES J

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Absenteeism from classrooms amongst students is an international problem that does not only affect Indian students. This research is focuses on absentees of student in class and score and has been carried out by using linear regression analysis. Linear regression analysis is one of excellent method of machine learning. The descriptive, student's t-test, Pearson correlation, and regression models were used in this study's statistical analysis. According to the results of this study, there are considerable variations between absentees and score (t-test=-4.06075, p < 0.05). The study also discovered that absenteeism from class had a negative link with the score (r = -0.6088) . To investigate the impact of class absentees on student score, a regression model was created. This study will benefit both the college administration and the students by raising awareness of the disadvantages of not attending classes.

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The impact of class absenteeism on student’s academic performance using regression models

Cited by 3 publications

References 12 publications

Identifying the Dependency Outline of Students’ Attendance in Tertiary Level using Markov Chain and Logistic Regression Model

Identifying the Dependency Outline of Students’ Attendance in Tertiary Level using Markov Chain and Logistic Regression Model

A Rule Induction Framework on the Effect of ‘Negative’ Attributes to Academic Performance

A Study of Relationship to Absentees and Score Using Machine Learning Method: A Case Study of Linear Regression Analysis

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