Predicting Success : An Application of Data Mining Techniques to Student Outcomes

Gilbert, Noah

doi:10.5121/ijdkp.2017.7201

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…When RF is employed for classification, each trees votes and the most popular class is returned (Han et al,, 2012). Random Forest was used by Gilbert (2017) to predict student outcomes. RF and genetic algorithm (GA) were applied on California State University freshmen and transfer students data from Fall 2000 through Fall 2010 of over 31,000 students.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

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Students’ Academic Performance and Dropout Prediction

Ameen¹,

Alarape²,

Adewole³

2019

MJoC

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Students’ Academic Performance (SAP) is an important metric in determining the status of students in any academic institution. It allows the instructors and other education managers to get an accurate evaluation of the students in different courses in a particular semester and also serve as an indicator to the students to review their strategies for better performance in the subsequent semesters. Predicting SAP is therefore important to help learners in obtaining the best from their studies. A number of researches in Educational Psychology (EP), Learning Analytics (LA) and Educational Data Mining (EDM) has been carried out to study and predict SAP, most especially in determining failures or dropouts with the goal of preventing the occurrence of the negative final outcome. This paper presents a comprehensive review of related studies that deal with SAP and dropout predictions. To group the studies, this review proposes taxonomy of the methods and features used in the literature for SAP and dropout prediction. The paper identifies some key issues and challenges for SAP and dropout predictions that require substantial research efforts. Limitations of the existing approaches for SAP and dropout prediction are identified. Finally, the paper exposes the current research directions in the area.

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Section: Random Forest (Rf)mentioning

confidence: 99%

“…It includes sponsorship or financial aid from third parties and selfsponsorship. Studies like Gilbert (2017) and Adejo and Connolly (2018) applied these features.…”

Section: Student-related Economic Featuresmentioning

confidence: 99%

Students’ Academic Performance and Dropout Prediction

Ameen¹,

Alarape²,

Adewole³

2019

MJoC

View full text Add to dashboard Cite

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“…Machine learning is an emerging trend in education, where it is applied in learning historical data and using it to predict learners' future behavior [6]. Therefore, many researchers have used the machine learning technique to predict students' future outcomes by classification, a popular machine learning technique [7], [8], [9]. Notably, classification offers a simple means to classify a large population and generate a robust prediction model to predict student performance [10], [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive Model of Postgraduate Student’s Dropout and Delay Using Machine Learning Algorithms

2021

IJATCSE

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Neural networks and Logical Regression algorithm provide the best ways to classify data, but they are outperformed continuously by the Decision Tree in analyzing student performance. Therefore, many scholars have used the Decision Tree to predict student performance with greater success. This research analyzed postgraduate student degree outcomes using socioeconomic data to develop a prediction model, where Decision Tree recorded the highest accuracy of 92.79%, better than Logical Regression and Neural Network. For brevity, the Decision Tree was used to produce the prediction model. Based on the study findings, postgraduate students who delay or drop out at the university mostly lack sponsors or had decreased income. Besides, male students delay or drop out if they had financial issues more than their female counterparts. Age, money management skills, number of children, and health expenses are the other factors that contribute to higher dropout or delay at the university. Therefore, this study provides a reliable prediction model for degree outcomes, allowing personalized follow-up to improve graduation rates.

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“…Predictive strength was assessed by matthews correlation coefficient. Attributes used for the study were GPA, retention and graduation [3]. In this paper, the author designed a classification based algorithm (CBA) for predicting slow learners among students.…”

Section: Literature Reviewmentioning

confidence: 99%

Student Placement Prediction Model: A Data Mining Perspective for Outcome-Based Education System

Rao¹,

V²,

Jogi³

et al. 2019

IJRTE

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Campus placement plays a vital role in every educational institution in helping students to achieve their goals. Data mining classification can be used as a useful tool for extracting the associated information from the large scale student dataset. Data mining methods have been used broadly in the area of the education system which involves various methods and approach for discovering knowledge. In this paper, a predictive model is designed which can predict the category of placements (dream companies, super dream companies and mass recruiter companies) in which students are eligible by considering their past performance in academics and other curricular activities. The model will also suggest further skills required for future recruitments which may help the students for placement preparation. The paper also provides real-time experimental results and findings along with performance measures used for model validation which helps in achieving the milestone of outcome-based education (OBE) in educational institutes as it isgiven utmost importance in present scenario to ensure better placement prospects in students, which would in turn help the students for carrier building.

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Predicting Success : An Application of Data Mining Techniques to Student Outcomes

Abstract: ABSTRACT

Cited by 4 publications

References 8 publications

Students’ Academic Performance and Dropout Prediction

Students’ Academic Performance and Dropout Prediction

Predictive Model of Postgraduate Student’s Dropout and Delay Using Machine Learning Algorithms

Student Placement Prediction Model: A Data Mining Perspective for Outcome-Based Education System

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