Identifying key factors of student academic performance by subgroup discovery

Helal, Sumyea; Li, Jiuyong; Liu, Lin; Ebrahimie, Esmaeil; Dawson, Shane; Murray, Duncan

doi:10.1007/s41060-018-0141-y

Cited by 48 publications

(35 citation statements)

References 54 publications

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“…Association rules as a data mining technique are used to investigate the correlation between different enrollment factors. Some studies investigated the Apriori algorithm on enrollment to extract the behavior of low-and high-income students [35], the quality of talent training and enhance the overall competitiveness of colleges and universities [36], student performance for a certain outcome (Pass or Fail) [37]. This paper gets benefits from previous studies to determine the different factors influencing the enrollment decision.…”

Section: Related Workmentioning

confidence: 99%

Factors Influencing Students Decisions to Enrollment in Sudanese Higher Education Institutions

Sedahmed¹,

Noureldien²

2019

IIM

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There is a growing body of literature that recognizes the importance of data mining in educational systems. This recognition makes educational data mining a new growing research community. One way to achieve the highest level of quality in a higher education system is by discovering knowledge from educational data such as students' enrollment data. Many mining tools that aim to discover exciting correlations, frequent patterns, associations, or casual structures among sets of items in educational data sets have been proposed. One of the widely used tools is association rules. In this paper, the Apriori algorithm is used to generate association rules to discover the importance and correlation between factors that influence student's decision to enroll in higher education institutions in Sudan. The algorithm is applied using a student's enrollment data set that was created using a questionnaire and 800 students enrolled in governmental and private sector universities as a sample. This paper classifies factors that influence enrollment into: student's demographic factors and four categories of enrollment related factors (Student and Society, Educational Institution, Admission, and Employment related factors), and determines the most influential factors in determining student's decision to enroll in Sudanese universities. The analysis result shows that the Educational Institution related factors (50%) and Admission related factors (40%) are strongly influencing students' enrollment decision, while the Employment related factors (10%) and Student and Society related factors (0%) have weak influence. The factors out of the 14 Educational Institution related factors that have a high impact are: reputation, diversity of study, quality of education, education facilities, and feasibility.

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Section: Related Workmentioning

confidence: 99%

Factors Influencing Students Decisions to Enrollment in Sudanese Higher Education Institutions

Sedahmed¹,

Noureldien²

2019

IIM

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“…Only the relationships of quite predictable indicators are statistically confirmed such as: § in terms of the number of accesses to elements of the course -the number of quizzes passed predicts the final mark (Romero, Espejo, Zafra, Romero, & Ventura, 2013) or that low level of contributions to the course learning activities leads to fail (Helal et al, 2019). § in the area of time spent -with the help of Rip, the Ripper implementation in Weka it could be predicted that with a low initial level of knowledge and minimal time spent, the student will receive a low mark (Gaudioso, Montero, & Hernandez-del-Olmo, 2012, p. 624).…”

Section: Analysis Of the Dependence Of The Success Of The Course On Tmentioning

confidence: 99%

Possible Applications Of Analytics Data In Online Courses

Bylieva¹

2019

The European Proceedings of Social and Behavioural Sciences

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Information and communication technologies are fundamentally changing the sphere of education in modern society. Online education is evolving and offering new opportunities for students' data analysis. At the same time, there is a certain barrier in the interaction of specialists of different professions engaged in data analysis in online learning. The purpose of this article is to reduce this barrier by giving a brief analysis of the main directions of development and the possibility of using data in online learning and showing what results could be expected from the presented methods now and in the near future. This study presents the priority areas of learning analytics such as tracking students at risk, analysis of the most effective options for the course (face to face, blended, online), analysis of the relationship of student success from the key characteristics of the course and the behavior of students, the improvement of students' assessment. The article also discusses the options for using data on the behavior of students in an individual and generalized form.

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“…SumyeaHelal, et al [13] developed the subgroup discovery to extract the significant aspects related to the student performance outcome. The method uses the student demographic and academic data, course and data retrieved from the institution learning management system to investigate the student performance.…”

Section: Related Workmentioning

confidence: 99%

“…The general formula for calculating the precision and recall of the SAP prediction is given in the Eq. (12) and (13).…”

Section: Experimental Designmentioning

confidence: 99%

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

Deepika*¹,

Sathyanarayana²

2019

IJRTE

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The student academic prediction model helps to predict the student performance that helps the university to provide necessary care to the particular students. Efficient prediction model helps to encourage the student for better performance in the academic. In this research, the Relief-F Budget Tree Random Forest with Gray Wolf Optimization (RFBTRF-GWO) method is proposed for the feature selection. The Gray Wolf Optimization (GWO) helps to scale the relevant feature with ranking order from the features selected by the Relief-F Budget Tree Random Forest (RFBTRF). The selected features are given as input to the classifier for the effective prediction. The k-Nearest Neighbor (kNN) and Artificial Neural Network (ANN) are used for the classification. The proposed RFBTRF-GWO method is evaluated on the three datasets such as two UCI datasets and one collected dataset. The RFBTRF-GWO has a higher performance accuracy of 96.2 % while the existing method RFBTRF has an accuracy of 70.88 %.

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Identifying key factors of student academic performance by subgroup discovery

Cited by 48 publications

References 54 publications

Factors Influencing Students Decisions to Enrollment in Sudanese Higher Education Institutions

Factors Influencing Students Decisions to Enrollment in Sudanese Higher Education Institutions

Possible Applications Of Analytics Data In Online Courses

Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

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