Knowledge Discovery for Higher Education Student Retention Based on Data Mining: Machine Learning Algorithms and Case Study in Chile

Abstract

Data mining is employed to extract useful information and to detect patterns from often large data sets, closely related to knowledge discovery in databases and data science. In this investigation, we formulate models based on machine learning algorithms to extract relevant information predicting student retention at various levels, using higher education data and specifying the relevant variables involved in the modeling. Then, we utilize this information to help the process of knowledge discovery. We predict student retention at each of three levels during their first, second, and third years of study, obtaining models with an accuracy that exceeds 80% in all scenarios. These models allow us to adequately predict the level when dropout occurs. Among the machine learning algorithms used in this work are: decision trees, k-nearest neighbors, logistic regression, naive Bayes, random forest, and support vector machines, of which the random forest technique performs the best. We detect that secondary educational score and the community poverty index are important predictive variables, which have not been previously reported in educational studies of this type. The dropout assessment at various levels reported here is valid for higher education institutions around the world with similar conditions to the Chilean case, where dropout rates affect the efficiency of such institutions. Having the ability to predict dropout based on student’s data enables these institutions to take preventative measures, avoiding the dropouts. In the case study, balancing the majority and minority classes improves the performance of the algorithms.

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

“…ML algorithms are widely used for prediction purposes and applied to provide solutions to questions such as global solar radiation [9], accuracy in determining the mortality rate in COVID-19 patients [10], and efficient processes for manufacturing industries [11]. In addition, ML algorithms assist the educational sector to evaluate student performance [12], forecast student dropout rates in any course [13], and understand students' unique learning styles [14]. Due to ML's vast and dynamic implementation and its capability to learn from any dataset, and predict and classify future transactions, we have selected multiple ML algorithms for this study.…”

Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction

“…Tables [3][4][5][6][7] show the predictive performance of the seven comparison algorithms in five metrics: AP, RL, HL, CV, and OE, respectively. The symbol ↑ indicates a larger value for better performance; the symbol ↓ indicates a smaller value for better performance.…”

“…One of the key indicators of high-level education quality is students' performance in the setting of the learning environment. Studies have shown that the early learning stage of the course is crucial [1][2][3], in which the students are able to nurture their interests in the relevant learning through the understanding and digestion of the syllabus structure and content organization, forming a solid foundation for the subsequent learning stages [4,5]. Adelman et al [6] conducted a long-term and systematic statistical study on behalf of the National Center for Education Statistics in the US, in order to reveal the constellational correlation and significance of the class attainment, attendance, curriculum, and student performance with the elucidation of what, when, where, and how they study.…”

Modeling COVID-19 Cases Statistically and Evaluating Their Effect on the Economy of Countries

“…These methods are an appropriate step to employ in all statistical/econometric modeling. Furthermore, there exists a potential use in machine, deep, and statistical learning models [39,[59][60][61]. Future research will consider other variables and relationships, such as production linkages and employment effects of the COVID-19 pandemic phenomena.…”