Academic Dashboard—Prediction of Institutional Student Dropout Numbers Using a Naïve Bayesian Algorithm

Suresh, Aishwarya; Rao, H. S. Sushma; Hegde, Vinayak

doi:10.1007/978-981-10-3935-5_8

Cited by 9 publications

(2 citation statements)

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“…Random Forest was used to forecast the outcomes on 772 instances and provided 88.3% accuracy. In this research work [10], Suresh et al took into account a number of factors, including educational records, parental education quali cations and nancial status, student medical history, and student conduct. Additionally, Nave Bayes was used to calculate the student dropout rate.…”

Section: Introductionmentioning

confidence: 99%

Student Academic Performance prognosticative Using optimized Hybrid Machine Learning Algorithms

Kannan

Abarna

Vairachilai

2023

Preprint

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Prediction of student performance at early stage in higher education is important for academic society so that strategic decisions can be made before students are placed to keep them from dropping out of the course. Due to India's massive student population and extremely ancient educational system, there are significant difficulties in measuring and forecasting students' performance. Every institution in India has its own unique set of criteria for measuring student achievement, and there is no formal process for keeping track of and evaluating a student's progress and improvement. Over the last decade, researchers in the education domain have presented numerous types of machine learning techniques. However, there are significant obstacles to dealing with imbalanced datasets in order to predict the performance of students. In this paper, the first phase of traditional classification algorithms has been applied to the dataset, which contains the progress of 4424 students. In the second phase, novel hybrid machine learning (ML) algorithms were used to get better predictions. The outcome of the proposed model makes it easier to predict how well students will do so that early decisions can be made about the growth of higher education institutions.

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Section: Introductionmentioning

confidence: 99%

Student Academic Performance prognosticative Using optimized Hybrid Machine Learning Algorithms

Kannan

Abarna

Vairachilai

2023

Preprint

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“…So far, the researches have been focusing on estimates based on high school performance [19]. The interesting factors are the gathered background information combined with the semester performance [20], as well as the financial [21] and family background [22]. However, there are also some universal factors in the studies in addition to the factors mentioned earlier.…”

Section: Introductionmentioning

confidence: 99%

Integrated Survival Analysis and Frequent Pattern Mining for Course Failure-Based Prediction of Student Dropout

Csalódi

Abonyi

2021

Mathematics

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A data-driven method to identify frequent sets of course failures that students should avoid in order to minimize the likelihood of their dropping out from their university training is proposed. The overall probability distribution of the dropout is determined by survival analysis. This result can only describe the mean dropout rate of the undergraduates. However, due to the failure of different courses, the chances of dropout can be highly varied, so the traditional survival model should be extended with event analysis. The study paths of students are represented as events in relation to the lack of completing the required subjects for every semester. Frequent patterns of backlogs are discovered by the mining of frequent sets of these events. The prediction of dropout is personalised by classifying the success of the transitions between the semesters. Based on the explored frequent item sets and classifiers, association rules are formed providing the estimates of the success of the continuation of the studies in the form of confidence metrics. The results can be used to identify critical study paths and courses. Furthermore, based on the patterns of individual uncompleted subjects, it is suitable to predict the chance of continuation in every semester. The analysis of the critical study paths can be used to design personalised actions minimizing the risk of dropout, or to redesign the curriculum aiming the reduction in the dropout rate. The applicability of the method is demonstrated based on the analysis of the progress of chemical engineering students at the University of Pannonia in Hungary. The method is suitable for the examination of more general problems assuming the occurrence of a set of events whose combinations may trigger a set of critical events.

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Digital India eGovernance Initiative for Tribal Empowerment: Performance Dashboard of the Ministry of Tribal Affairs

Kapoor

Maurya

Raman

et al. 2021

Information and Communication Technology for Competitive Strategies (ICTCS 2020)

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Academic Dashboard—Prediction of Institutional Student Dropout Numbers Using a Naïve Bayesian Algorithm

Cited by 9 publications

References 1 publication

Student Academic Performance prognosticative Using optimized Hybrid Machine Learning Algorithms

Student Academic Performance prognosticative Using optimized Hybrid Machine Learning Algorithms

Integrated Survival Analysis and Frequent Pattern Mining for Course Failure-Based Prediction of Student Dropout

Digital India eGovernance Initiative for Tribal Empowerment: Performance Dashboard of the Ministry of Tribal Affairs

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