Student Dropout Prediction

Bonifro, Francesca Del; Gabbrielli, Maurizio; Lisanti, Giuseppe; Zingaro, Stefano Pio

doi:10.1007/978-3-030-52237-7_11

Cited by 76 publications

(64 citation statements)

References 8 publications

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“…On the one hand, some of the features used in this paper still fall into these categories mentioned in Alban and Mauricio (2019). Some studies make predictions based on previously defined features like personal information, previous education, and academic performance (Berens et al, 2018;Chen et al, 2018;Nagy and Molontay, 2018;Ortiz-Lozano et al, 2018;Del Bonifro et al, 2020;Utari et al, 2020). Moreover, some studies cover data related to the economy (Sorensen, 2019;Delen et al, 2020), which is also mentioned in the previous survey paper ( Alban and Mauricio, 2019).…”

Section: Features For Dropout Predictionmentioning

confidence: 96%

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

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Anomalies in education affect the personal careers of students and universities' retention rates. Understanding the laws behind educational anomalies promotes the development of individual students and improves the overall quality of education. However, the inaccessibility of educational data hinders the development of the field. Previous research in this field used questionnaires, which are time- and cost-consuming and hardly applicable to large-scale student cohorts. With the popularity of educational management systems and the rise of online education during the prevalence of COVID-19, a large amount of educational data is available online and offline, providing an unprecedented opportunity to explore educational anomalies from a data-driven perspective. As an emerging field, educational anomaly analytics rapidly attracts scholars from a variety of fields, including education, psychology, sociology, and computer science. This paper intends to provide a comprehensive review of data-driven analytics of educational anomalies from a methodological standpoint. We focus on the following five types of research that received the most attention: course failure prediction, dropout prediction, mental health problems detection, prediction of difficulty in graduation, and prediction of difficulty in employment. Then, we discuss the challenges of current related research. This study aims to provide references for educational policymaking while promoting the development of educational anomaly analytics as a growing field.

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Section: Features For Dropout Predictionmentioning

confidence: 96%

Educational Anomaly Analytics: Features, Methods, and Challenges

et al. 2022

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“…For instance, it represents a big cost factor for society, existing relationships with fellow students can end, and life planning must be adjusted. In order to maximize student success probability and to reduce costs, it is necessary to identify dropout very early (Del Bonifro et al, 2020). Therefore, analyzing its conditions is becoming a focus of university research (Blüthmann et al, 2008).…”

Section: Conceptualizing Student Dropoutmentioning

confidence: 99%

Student dropout at university: a phase-orientated view on quitting studies and changing majors

2021

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Student dropout can be conceptualized as a decision-making process, consisting of different phases. Based on previous literature on student dropout, decision-making, and action-phases, we proposed that the process of developing dropout intentions includes the following phases: non-fit perception, thoughts of quitting/changing, deliberation, information search, and a final decision. In the present cross-sectional study, we empirically investigated if the assumed phases can be distinguished from each other, if the phases follow the presumed order, and whether each phase is associated with certain characteristics. Furthermore, we considered a strict separation between quitting studies completely and changing a major. For this purpose, we analyzed data of N = 1005 students (average age of 23.0 years; 53% female; 47% male) from a German University. By using confirmatory factor analyses, we found the supposed factor structure for the different phases concerning both kinds of dropout, quitting studies, and changing majors. In each process, structural equation modelling indicated positive relations between adjoining phases. The factor values correlated to a substantial amount with an assortment of variables associated with student dropout. On a theoretical level, the conception of different phases of student dropout helps to get a better understanding of regulatory processes in the context of student dropout.

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“…This fact can be used by the institution to decide whether to act as early as possible, based on the information available at enrolment time, or to wait for More data will be collected in the first year, allowing for more precise projections. In any event, the results suggest that, starting with data that has no educational or didactic value, our technology may practically aid in the effort to reduce dropout rates [7].There is strong evidence that university dropout prediction is of considerable interest among academic investigators, and that high precision algorithms are being developed to solve this critical issue [8].…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Approach to Detect Student Dropout at University

2021

IJATCSE

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In universities, student dropout is a major concern that reflects the university's quality. Some characteristics cause students to drop out of university. A high dropout rate of students affects the university's reputation and the student's careers in the future. Therefore, there's a requirement for student dropout analysis to enhance academic plan and management to scale back student's drop out from the university also on enhancing the standard of the upper education system. The machine learning technique provides powerful methods for the analysis and therefore the prediction of the dropout. This study uses a dataset from a university representative to develop a model for predicting student dropout. In this work, machine- learning models were used to detect dropout rates. Machine learning is being more widely used in the field of knowledge mining diagnostics. Following an examination of certain studies, we observed that dropout detection may be done using several methods. We've even used five dropout detection models. These models are Decision tree, Naïve bayes, Random Forest Classifier, SVM and KNN. We used machine-learning technology to analyze the data, and we discovered that the Random Forest classifier is highly promising for predicting dropout rates, with a training accuracy of 94% and a testing accuracy of 86%.

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Student Dropout Prediction

Cited by 76 publications

References 8 publications

Educational Anomaly Analytics: Features, Methods, and Challenges

Educational Anomaly Analytics: Features, Methods, and Challenges

Student dropout at university: a phase-orientated view on quitting studies and changing majors

A Machine Learning Approach to Detect Student Dropout at University

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