Mining the Student Dropout in Higher Education

Hsu, Cheng–Che; Yeh, Ching-Chiang

doi:10.1520/jte20180021

Cited by 7 publications

(4 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, this study proposes using data mining techniques to look at the issues that may contribute towards student dropout. Data mining techniques can identify and predict future trends, track the behaviours and habits of participants and, lastly, assist with decision-making (Hsu & Yeh, 2019). In particular, the focus of the study was on profiling students at risk of dropout using administrative data obtained from a university in South Africa.…”

Section: Review Of the Literaturementioning

confidence: 99%

Profiling students at risk of dropout at a university in South Africa

Ntema

2022

JSAA

View full text Add to dashboard Cite

Background: Higher education authorities continue to be concerned about dropout rates among university students. Dropping out affects cost efficiency and tarnishes the reputation of the institution. As a result, profiling at risk student of dropout is crucial. Purpose: To build a profile of students at risk of dropout using administrative university data. Methods: The researcher employed a data mining technique in which predictors were chosen based on their weight of evidence (WOE) and information value (IV). The chosen predictors were then used to build a profile of students at risk of dropout. Findings: According to the findings, the student is at risk if was born in the years 1931 to 1967 or 1994 to 2001; fails more than four modules in a year with a participation average mark of 43percent or less; and has joined the university in the second academic year. Conclusions: This study concludes that matric scores had no bearing on the chance of a student dropping out for the cohorts of students who attended the institution between 2008 and 2018. However, the variables number of modules failed, participation average marks, entry level and year of birth had a bearing for building a profile for the students at risk of dropout for the cohorts of students who attended the institution between 2008 and 2018.

show abstract

Section: Review Of the Literaturementioning

confidence: 99%

Profiling students at risk of dropout at a university in South Africa

Ntema

2022

JSAA

View full text Add to dashboard Cite

show abstract

“…To conclude, it can be stated that to prevent the high dropout rate of university students, especially those with psychosocial needs and increased social vulnerability, educational intervention is required [48][49][50][51][52][53]. This study analyses the above mentioned variables to establish how they affect academic success or failure in order to promote effective prevention measures, create new support opportunities, and reduce the university dropout rate, especially during the first year of university.…”

Section: Previous Academic Recordsmentioning

confidence: 99%

Academic Achievement and Failure in University Studies: Motivational and Emotional Factors

et al. 2020

View full text Add to dashboard Cite

Universities are committed to offering quality education; however, a high rate of academic failure is often observed in the first year of studies. Considering the impact that motivation and emotional aspects can have on students’ commitment to study and therefore on their academic performance, achievement, and well-being, this study aims to identify the factors associated with academic success or failure in 1071 students entering the National Polytechnic School (Quito, Ecuador). The data were compiled from the existing computer records of the university with the permission of the responsible administrative staff. A predictive model has been used and a binary logistic regression analysis was carried out through the step-forward regression procedure based on the Wald statistic to analyze the predictive capacity of the variables related to emotional intelligence, motivational and self- regulated socio-cognitive skills, goal orientation, and prior academic achievement (measured by university entrance marks and through a knowledge test carried out at the beginning of the university academic year). To determine the cut-off point for the best discriminatory power of each of the variables, a Receiver Operating Characteristics (ROC) curve analysis has been used. The results indicate that the variables that are significant in the prediction of academic success or failure are the two academic performance measures: the emotional attention variable, and the performance-approach goals and the motivational self-efficacy variable. Additionally, the highest predictive power is displayed by the prior academic performance measure obtained through the knowledge test conducted at the beginning of the university course.

show abstract

“…Reducing student dropouts is an important challenge that high schools and higher education must face. The loss of students who are beginning their high school or undergraduate studies constitutes a worldwide concern (e.g., Heublein, 2014;Aulck et al, 2016;Hsu and Yeh, 2019;Olaya et al, 2020). Several factors have been studied as the origins of dropping out, including unfavorable sociodemographic conditions, insufficient academic support, underprivileged economic income, and poor academic and social capabilities.…”

Section: Introductionmentioning

confidence: 99%

“…Studying the impact of the diverse factors that produce dropping out in middle and higher education has pushed institutions to perform statistical studies to disclose the relative importance of these factors and to apply suitable and timely measures to predict students at risk of dropping out (Hsu and Yeh, 2019). In this regard, the incorporation of learning analytics techniques that involve simultaneous analysis of students' social and performance data can disclose the factors that have a larger impact on dropping out.…”

Section: Introductionmentioning

confidence: 99%

Predictive analytics study to determine undergraduate students at risk of dropout

Gonzalez-Nucamendi,

Noguez,

Neri

et al. 2023

Front. Educ.

View full text Add to dashboard Cite

In this this work, a study is presented with quantitative variables using machine learning tools to detect undergraduate students at risk of dropping out and the factors associated with this behavior. Clustering algorithms and classification methods were tested to determine the predictive power of several variables regarding the dropout phenomenon on an unbalanced database of 14,495 undergraduate students with a real dropout rate of 8.5% and a retention rate of 91.5%. The usual classification criterion that assigns individuals to a class if their probability of belonging to it is greater than 50% provided accuracies of 13.2% in the dropout prediction and 99.4% in the retention prediction. Among eight classifiers, Random Forest was selected and applied along with Threshold Probability, which allowed us to gradually increase the dropout precision to more than 50%, while maintaining retention and global precisions above 70%. Through this study, it was found that the main variables associated with student dropouts were their academic performance during the early weeks of the first semester, their average grade in the previous academic levels, the previous mathematics score, and the entrance exam score. Other important variables were the number of class hours being taken, student age, funding status of scholarships, English level, and the number of dropped subjects in the early weeks. Given the trade-off between dropout and retention precisions, our results can guide educational institutions to focus on the most appropriate academic support strategies to help students at real risk of dropping out.

show abstract

Mining the Student Dropout in Higher Education

Cited by 7 publications

References 50 publications

Profiling students at risk of dropout at a university in South Africa

Profiling students at risk of dropout at a university in South Africa

Academic Achievement and Failure in University Studies: Motivational and Emotional Factors

Predictive analytics study to determine undergraduate students at risk of dropout

Contact Info

Product

Resources

About