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Relevant bibliographies by topics / Dropout prediction

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Academic literature on the topic 'Dropout prediction'

Author: Grafiati

Published: 5 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Dropout prediction"

1

Kim, Sangyun, Euteum Choi, Yong-Kee Jun, and Seongjin Lee. "Student Dropout Prediction for University with High Precision and Recall." Applied Sciences 13, no. 10 (2023): 6275. http://dx.doi.org/10.3390/app13106275.

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Since a high dropout rate for university students is a significant risk to local communities and countries, a dropout prediction model using machine learning is an active research domain to prevent students from dropping out. However, it is challenging to fulfill the needs of consulting institutes and the office of academic affairs. To the consulting institute, the accuracy in the prediction is of the utmost importance; to the offices of academic affairs and other offices, the reason for dropping out is essential. This paper proposes a Student Dropout Prediction (SDP) system, a hybrid model to predict the students who are about to drop out of the university. The model tries to increase the dropout precision and the dropout recall rate in predicting the dropouts. We then analyzed the reason for dropping out by compressing the feature set with PCA and applying K-means clustering to the compressed feature set. The SDP system showed a precision value of 0.963, which is 0.093 higher than the highest-precision model of the existing works. The dropout recall and F1 scores, 0.766 and 0.808, respectively, were also better than those of gradient boosting by 0.117 and 0.011, making them the highest among the existing works; Then, we classified the reasons for dropping out into four categories: “Employed”, “Did Not Register”, “Personal Issue”, and “Admitted to Other University.” The dropout precision of “Admitted to Other University” was the highest, at 0.672. In post-verification, the SDP system increased counseling efficiency by accurately predicting dropouts with high dropout precision in the “High-Risk” group while including more dropouts in total dropouts. In addition, by predicting the reasons for dropouts and presenting guidelines to each department, the students could receive personalized counseling.

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Ameen, Ahmed O., Moshood Alabi Alarape, and Kayode S. Adewole. "STUDENTS’ ACADEMIC PERFORMANCE AND DROPOUT PREDICTION." MALAYSIAN JOURNAL OF COMPUTING 4, no. 2 (2019): 278. http://dx.doi.org/10.24191/mjoc.v4i2.6701.

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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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Song, Zihan, Sang-Ha Sung, Do-Myung Park, and Byung-Kwon Park. "All-Year Dropout Prediction Modeling and Analysis for University Students." Applied Sciences 13, no. 2 (2023): 1143. http://dx.doi.org/10.3390/app13021143.

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The core of dropout prediction lies in the selection of predictive models and feature tables. Machine learning models have been shown to predict student dropouts accurately. Because students may drop out of school in any semester, the student history data recorded in the academic management system would have a different length. The different length of student history data poses a challenge for generating feature tables. Most current studies predict student dropouts in the first academic year and therefore avoid discussing this issue. The central assumption of these studies is that more than 50% of dropouts will leave school in the first academic year. However, in our study, we found the distribution of dropouts is evenly distributed in all academic years based on the dataset from a Korean university. This result suggests that Korean students’ data characteristics included in our dataset may differ from those of other developed countries. More specifically, the result that dropouts are evenly distributed throughout the academic years indicates the importance of a dropout prediction for the students in any academic year. Based on this, we explore the universal feature tables applicable to dropout prediction for university students in any academic year. We design several feature tables and compare the performance of six machine learning models on these feature tables. We find that the mean value-based feature table exhibits better generalization, and the model based on the gradient boosting technique performs better than other models. This result reveals the importance of students’ historical information in predicting dropout.

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Amare, Meseret Yihun, and Stanislava Simonova. "Global challenges of students dropout: A prediction model development using machine learning algorithms on higher education datasets." SHS Web of Conferences 129 (2021): 09001. http://dx.doi.org/10.1051/shsconf/202112909001.

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Research background: In this era of globalization, data growth in research and educational communities have shown an increase in analysis accuracy, benefits dropout detection, academic status prediction, and trend analysis. However, the analysis accuracy is low when the quality of educational data is incomplete. Moreover, the current approaches on dropout prediction cannot utilize available sources. Purpose of the article: This article aims to develop a prediction model for students’ dropout prediction using machine learning techniques. Methods: The study used machine learning methods to identify early dropouts of students during their study. The performance of different machine learning methods was evaluated using accuracy, precision, support, and f-score methods. The algorithm that best suits the datasets for these performance measurements was used to create the best prediction model. Findings & value added: This study contributes to tackling the current global challenges of student dropouts from their study. The developed prediction model allows higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education. It can also help the institutions to plan resources in advance for the coming academic semester and allocate it appropriately. Generally, the learning analytics prediction model would allow higher education institutions to target students who are likely to dropout and intervene timely to improve retention rates and quality of education.

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Awasthi, Shivani. "Dropout Prediction with Supervised Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44873.

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Student dropout is a critical issue in the education sector, impacting institutional efficiency and student success. This project, Dropout Prediction with Supervised Learning, leverages machine learning models—Random Forest (RF), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbours (KNN), and Naïve Bayes (NB)—to predict student dropouts based on historical academic, demographic, and behavioural data. The study involves data preprocessing, feature selection, and model evaluation to identify key factors influencing dropout rates. Supervised learning techniques are employed to classify students into "at-risk" and "not at-risk" categories. The performance of each model is assessed using accuracy, precision, recall, and F1-score metrics to determine the most effective predictor. The findings aim to provide educational institutions with actionable insights, enabling early intervention strategies such as academic counselling and financial aid support. By implementing predictive analytics, institutions can enhance student retention and improve overall educational outcomes. Keywords – Dropout Prediction, Supervised Learning, Machine Learning Models, Student Retention, Predictive Analytics, Classification Algorithms

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Yujiao, Zhang, Ling Weay Ang, Shi Shaomin, and Sellappan Palaniappan. "Dropout Prediction Model for College Students in MOOCs Based on Weighted Multi-feature and SVM." Journal of Informatics and Web Engineering 2, no. 2 (2023): 29–42. http://dx.doi.org/10.33093/jiwe.2023.2.2.3.

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Due to the COVID -19 pandemic, MOOCs have become a popular form of learning for college students. However, unlike traditional face-to-face courses, MOOCs offer little faculty supervision, which may result in students being insufficiently motivated to continue learning, ultimately leading to a high dropout rate. Consequently, the problem of high dropout rates in MOOCs requires urgent attention in MOOC research. Predicting dropout rates is the first step to address this problem, and MOOCs have a large amount of behavioral data that can be used for such predictions. Most existing models for predicting MOOC dropout based on behavioral data assign equal weights to each behavioral characteristic, despite the fact that each behavioral characteristic has a different effect on predicting dropout. To address this problem, this paper proposes a dropout prediction model based on the fusion of behavioral data and Support Vector Machine (SVM). This innovative model assigns different weights to different behavior features based on Pearson principle and integrates them as data inputs to the model. Dropout prediction is essentially a binary problem, Support Vector Machine Classifier is then trained using the training dataset 1 and dataset 2. Experimental results on both datasets show that this predictive model outperforms previous models that assign the same weights to the behavior features.

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Radovanovic, Sandro, Boris Delibasic, and Milija Suknovic. "Predicting dropout in online learning environments." Computer Science and Information Systems, no. 00 (2020): 53. http://dx.doi.org/10.2298/csis200920053r.

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Online learning environments became popular in recent years. Due to high attrition rates, the problem of student dropouts became of immense importance for course designers, and course makers. In this paper, we utilized lasso and ridge logistic regression to create a prediction model for dropout on the Open University database. We investigated how early dropout can be predicted, and why dropouts occur. To answer the first question, we created models for eight different time frames, ranging from the beginning of the course to the mid-term. There are two results based on two definitions of dropout. Results show that at the beginning AUC of the prediction model is 0.549 and 0.661 and rises to 0.681 and 0.869 at mid-term. By analyzing logistic regression coefficients, we showed that at the beginning of the course demographic features of the student and course description features are the most important variables for dropout prediction, while later student activity gains more importance.

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Arthana, I. Ketut Resika. "Optimizing Dropout Prediction in University Using Oversampling Techniques for Imbalanced Datasets." International Journal of Information and Education Technology 14, no. 8 (2024): 1052–60. http://dx.doi.org/10.18178/ijiet.2024.14.8.2133.

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The phenomenon of student dropout is a significant concern within universities. Institutions must accurately predict the likelihood of student dropout to address this issue effectively. The prediction of student dropout aids universities in identifying early signs of student challenges. Moreover, it enables institutions to implement proactive measures to mitigate dropout rates. This paper presents a novel approach for selecting a classification algorithm to predict student dropout to aid universities in identifying early signs of student dropout. Moreover, it enables institutions to implement proactive measures to mitigate dropout rates. Each university possesses its academic dataset attributes, which can be leveraged for predicting potential dropout cases of student dropout. Our methodology begins with attribute selection, dataset preprocessing, and comparative evaluation of classification algorithms based on priority performance metrics. The research case study is conducted at Universitas Pendidikan Ganesha (Undiksha). The model selection was based on comparing classification algorithm performance, including Naïve Bayesian, Decision Tree (DT), and K-Nearest Neighbors (KNN). The dataset for this research was collected from the Information Academic System of Undiksha, encompassing students who graduated or dropped out between 2013 and 2023. It should be noted that the dataset exhibits class imbalance. Hence, this research utilized the Synthetic Minority Over Sampling Technique (SMOTE) algorithm to address the imbalance in lowsized datasets. The original and oversampled datasets were subjected to each classification algorithm. We chose Recall as the primary evaluation metric to prioritize ensuring that actual dropouts are not incorrectly predicted as graduates. This research demonstrates that the KNN classification algorithm, applied to the oversampled dataset, achieves the highest Recall value of 93.5%, Precision of 94.1%, F1-Score of 93.5%, and AUC value of 97.9%.

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Nicoletti, Maria do Carmo, and Osvaldo Luiz de Oliveira. "A Machine Learning-Based Computational System Proposal Aiming at Higher Education Dropout Prediction." Higher Education Studies 10, no. 4 (2020): 12. http://dx.doi.org/10.5539/hes.v10n4p12.

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In the literature related to higher education, the concept of dropout has been approached from several perspectives and, over the years, its definition has been influenced by the use of diversified semantic interpretations. In a general higher education environment dropout can be broadly characterized as the act of a student engaged in a course leaving the educational institution without finishing the course. This paper describes the proposal of the architecture of a computational system, PDE (Predicting Dropout Events), based on machine learning (ML) algorithms and specifically designed for predicting dropout events in a higher level educational environment. PDE&rsquo;s main subsystem implements a group of instance-based learning (IBL) algorithms which, taking into account a particular university-course environment, and based on log files containing descriptions of previous dropouts events, is capable to predict when a student already engaged in the course, is prone to dropout, so preventive measures could be quickly implemented.

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Zhou, Fanhao, and Neil Agarwal. "Student Performance Prediction Based on Decision Trees." Journal of Research in Applied Mathematics 10, no. 12 (2024): 114–20. https://doi.org/10.35629/0743-1012114120.

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This study applies a decision tree machine learning model to predict student dropouts and academic performance using a UCI Machine Learning Repository dataset. The dataset includes academic, demographic, and socioeconomic factors to identify key predictors of student outcomes. The goal is to assist educators in developing targeted interventions to reduce dropout rates and improve academic success. The model achieved 71% accuracy in predicting dropout, enrolled, and graduate categories, with a recall of 76% for dropouts, demonstrating its effectiveness in identifying at-risk students. However, the model struggled to differentiate enrolled from graduate students, a challenge heightened by class imbalance. A comparison between a 95/5 and 50/50 train-test split revealed better performance with a more extensive training set, particularly in classifying enrolled students. Key predictors such as second-semester curricular unit performance and tuition payment status were critical to the model’s accuracy. However, additional behavioral and engagement features are emphasized to improve prediction accuracy further. This research provides actionable insights for educational institutions, supporting data-driven interventions to enhance student retention and academic outcomes.

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Dissertations / Theses on the topic "Dropout prediction"

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Poza-Juncal, Inés Victoria. "Predicting dropout among inner-city Latino youth using psychological indices /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Owens, Mario Antonio. "Variables that impact high school dropout." Diss., Mississippi State : Mississippi State University, 2009. http://library.msstate.edu/etd/show.asp?etd=etd-03312009-151116.

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Smarn, Ganmol Halinski Ronald S. "Differences between persisters and dropouts in a private industrial technology school in Thailand." Normal, Ill. Illinois State University, 1995. http://wwwlib.umi.com/cr/ilstu/fullcit?p9604371.

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Thesis (Ph. D.)--Illinois State University, 1995.<br>Title from title page screen, viewed April 21, 2006. Dissertation Committee: Ronald S. Halinski (chair), Kenneth H. Strand, James C. Palmer, George Padavil. Includes bibliographical references (leaves 109-116) and abstract. Also available in print.

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King, Teresa C. "Examining the Relationship Between Persistence in Attendance in an Afterschool Program and an Early Warning Index for Dropout." Thesis, University of North Texas, 2014. https://digital.library.unt.edu/ark:/67531/metadc500218/.

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School districts constantly struggle to find solutions to address the high school dropout problem. Literature supports the need to identify and intervene with these students earlier and in more systemic ways. The purpose of this study was to conduct a longitudinal examination of the relationship between sustained afterschool participation and the host district’s early warning index (EWI) associated with school dropout. Data included 65,341 students participating in an urban school district’s after school program from school years 2000-2001 through 2011-2012. The district serves more than 80,000 students annually. Data represented students in Pre-Kindergarten through Grade 12, and length of participation ranged from 1 through 12 years. Results indicated that student risk increased over time and that persistent participation in afterschool programming had a significant relationship with student individual growth trajectories. Slower growth rates, as evidenced through successive models, supported students being positively impacted by program participation. Additionally, participation was more meaningful if students persisted, as noted in the lower EWI rates, as compared to students who attended less consistently.

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Taylor, Sarah Cecelia Ferguson. "Pathways to dropping out." Diss., This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-06062008-144845/.

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Semmelroth, Carrie Lisa. "Response to intervention at the secondary level : identifying students at risk for high school dropout /." [Boise, Idaho] : Boise State University, 2009. http://scholarworks.boisestate.edu/td/30/.

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Foster, Edward C. 1946. "Prediction of High School Dropouts and Teen-Aged Parents from Student Permanent Records." Thesis, University of North Texas, 1995. https://digital.library.unt.edu/ark:/67531/metadc277964/.

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Research has reported that a predictive link exists between socio-economic risk factors and high school dropouts, including teen-aged parents. Educators have little control over socio-economic risk factors. However, school records and classroom performance data can point to in-school risk factors. The purpose of this study was to help all students by using the in-school data to pinpoint the indicators that predict potential student achievement difficulties in specific areas of curricula. This study was an anteriospective longitudinal study of the 1995 graduating class of a suburban school district composed of approximately 920 seniors. The sample consisted of 344 graduates, 114 dropouts, and 42 teenaged parents. Backward stepwise logistic regression analysis was the statistical method used for model building. An analysis was done by gender at the 2nd, 4th, 6th, and 8th grades from the permanent records of sample students. The study found that significant predictors exist at each grade level and are different for each group, grade level, and gender with some predictors in common: language arts and attendance. The most consistent male dropout predictors were found to be absenteeism, grades in language arts, spelling, and achievement test scores in language arts. The most consistent female dropout predictors were found to be absenteeism, elementary retention, course failures, and achievement test scores in language arts. Achievement test scores in language arts were found to be the most important in-school predictors for teen-aged parents. The predictors for teenaged parents followed the same pattern as female dropouts and graduates until the 8th grade where achievement test scores in vocabulary, math, and total battery became important predictors. Teen-aged parents were found to be a sub-population of dropouts or graduates. Teen-aged parents dropped out or graduated from school based on the early predictors of dropouts or graduates, not based on parenting or single status.

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MacNeill, Rodney M. "The prediction of dropout in an entry level trades training program." Thesis, University of British Columbia, 1989. http://hdl.handle.net/2429/31102.

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Withdrawal from a program of studies can have negative consequences that extend beyond those that directly affect the dropouts. Beyond the lack of employment related skills and the impact that dropping out may have on students' confidence in their ability as learners, attrition also has an effect on the educational institute and sponsoring agencies. For example, program attrition leaves the training provider with empty seats but no corresponding reduction in training costs and the sponsoring agencies with a limited return on their training investments. This study examined attrition in short-term vocational programs to determine if factors from research on other postsecondary populations are applicable to these kinds of students. A formula was also developed to predict, early in the program, which students are most likely to withdraw. A review of the research confirmed that what is known about factors related to attrition for students in short-term vocational programs is limited. This necessitated a "borrowing" of factors from research directed at high school students and those in adult and higher education programs. By means of a mailed questionnaire, and using institute records, data were collected for those factors relevant to the population and program under study. These factors were divided into those students brought with them and those they experienced after they began their training. Of the 36 pre-entry factors studied, 12 produced statistically significant relationships when compared to persistence/withdrawal. The significant factors included high school graduation; test scores on reading vocabulary, reading comprehension, reference skills, math computation, math concepts and applications, and combined reading and combined math scores; mean differences in age; the student's socioeconomic status; certainty of program choice; and locus of control as related to high school persistence/withdrawal. Of those categorized as postentry, 10 of the 28 factors produced statistically significant relationships when compared to the indicator variable. These factors were enough study time, study time compared to others, hours per week at PVI, tests passed per attempt, tests exceeded per attempt, feeling that friends had gained from the program, estimation of program success, financial concern, agency sponsorship, and the use of Training Consultants. Combining the statistically significant factors using multiple regression analysis produced a prediction formula which included tests passed per attempt, combined math scores, study time compared, age, and feeling that friends had gained from the program. Conclusions based upon the results of the study centered around the application of attrition factors from the study of other populations and the utility of prediction for practitioners. In essence, the researcher believes it is inappropriate to make assumptions regarding attrition by short-term vocational students based upon research findings from other populations. In addition, even though the findings which characterized persisters as "good students" indicate that attrition rates may be reduced by either restricting admission by students who do not fit the profile or by providing these students with additional support, the amount of variance accounted for (16 percent) based upon the results of the multiple regression analysis suggest caution be used in making any decision. The researcher concludes by recommending that future studies examine attrition by using a variety of research methods in an attempt to clarify which factors are related to student attrition.<br>Education, Faculty of<br>Educational Studies (EDST), Department of<br>Graduate

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Babers, Tracy Allen Sr. "The determining factors of high school dropouts." CSUSB ScholarWorks, 2007. https://scholarworks.lib.csusb.edu/etd-project/3126.

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The purpose of this study is to examine the factors that cause high school students to drop out. The method for this project was a review of literature collected through journal articles, the internet and books. The factors found to play the biggest role were race, academic age/grade, and gender.

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Wilde, Richard Wayne. "Early Identification of At-Risk Children in a Rural School District Using Multiple Predictor Variables." PDXScholar, 1991. https://pdxscholar.library.pdx.edu/open_access_etds/1401.

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The purpose of this study was to determine if data routinely collected during the kindergarten year and at entry into first grade could be used to predict whether a child would be perceived as successful or not successful by the end of first grade. The need for immediate continued research on this topic was established through the review of literature, which highlighted the extent of the at-risk problem both locally and nationally. The growing number of at-risk students combined with the minimal impact of the educational programs mandates the need to identify these students in time to prevent school failure. However, clear identification procedures are not currently available and previous studies have raised substantial questions regarding the accuracy of early identification procedures. The presenting problem of this study was to determine the sensitivity and specificity of a set of predictor variables, and then to analyze these findings as to whether or not they were accurate enough for use as an initial identification process for subsequent classes. The primary research approach of this study was a longitudinal data collection and correlational analysis, with discriminant analysis techniques used to determine predictive accuracy. The study was limited to data on the class of 2001 from two elementary schools within the Washougal School District. The data collected and the subsequent analysis were used to answer six exploratory research questions. No hypothesis was proposed. This study used ratings and scores obtained from the administration of the Preschool Screening system, kindergarten teacher ratings, the School Success Rating Scale, and the Gates-MacGinitie Reading Readiness Tests as predictor variables. Criterion measures of school success/failure were: placement into special programs or grade retention, and end-of-first-grade evaluations of individual student success (report cards, teacher ratings, Gates-MacGinitie Reading Achievement, and the School Success Ratings Scale). The demographic variables of gender, age, parent marital status, and eligibility for free or reduced lunch were analyzed for their potential to exceed or enhance the accuracy of the predictor variables. Three types of measurement were defined and required in order for a predictor or predictor combination to be considered adequate for use in an identification process. These were overall accuracy, criterion sensitivity and specificity accuracy, and prediction sensitivity and specificity accuracy. An 80 percent accuracy level was desired on all three types of measurement. Findings of this study indicated that no single or combination of predictor, and/or demographic variables produced all three desired levels of accuracy. Various combinations of the predictor and demographic variables produced overall accuracy rates exceeding 80 percent for each of the criterion variables. Criterion measured sensitivity and specificity were found to be adequate for use in the prediction of at-risk students. Prediction measured specificity was also found to be highly accurate. Prediction sensitivity, however, was below the desired 80 percent level, indicating that the predictor variables over identify at-risk students. It was concluded that the predictor variables could be used in an identification process if mild over-identification of at-risk students was acceptable to the district. Any use of these identification procedures is assumed to be in connection with ethical intervention practices. Recommendations from this study included cross validation of the results and continuation of the study regarding the predictive accuracy of the identified variables as the students move through higher grade levels. The study also encouraged the Washougal School District to develop a formal collection and processing procedure for their routinely collected data.

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Books on the topic "Dropout prediction"

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Nichols, Clarence E. Dropout prediction and prevention. Clinical Psychology Pub. Co., 1990.

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Turner, Chandra Ramphal. Factors that put students at risk of leaving school before graduation. Program Dept., Research Centre, Scarborough Board of Education, 1993.

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Barro, Stephen M. Who drops out of high school?: Findings from high school and beyond. Center for Education Statistics, Office of Educational Research and Improvement, U.S. Dept. of Education, 1987.

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Fowler, Timothy B. Making the decision to drop out of high school: A bi-level analysis of the process in American schools. University of Chicago, 1991.

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ERIC Clearinghouse on Adult, Career, and Vocational Education., ed. Adult learner retention revisited. ERIC Clearinghouse on Adult, Career, and Vocational Education, Center on Education and Training for Employment, College of Education, the Ohio State University, 1995.

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Rossi, Robert J. Evaluation of projects funded by the School Dropout Demonstration Assistance Program: Final evaluation report. U.S. Dept. of Education, Office of the Undersecretary, 1995.

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Rossi, Robert J. Evaluation of projects funded by the School Dropout Demonstration Assistance Program: Final evaluation report. U.S. Dept. of Education, Office of the Undersecretary, 1995.

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Rossi, Robert J. Evaluation of projects funded by the School Dropout Demonstration Assistance Program: Final evaluation report. U.S. Dept. of Education, Office of the Undersecretary, 1995.

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Rossi, Robert J. Evaluation of projects funded by the School Droupout Demonstration Assistance Program: Final evaluation report. U.S. Dept. of Education, Office of the Undersecretary, 1994.

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Rossi, Robert J. Evaluation of projects funded by the School Dropout Demonstration Assistance Program: Final evaluation report. American Institutes for Research, 1995.

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Book chapters on the topic "Dropout prediction"

1

Del Bonifro, Francesca, Maurizio Gabbrielli, Giuseppe Lisanti, and Stefano Pio Zingaro. "Student Dropout Prediction." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52237-7_11.

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Guarda, Teresa, Oscar Barrionuevo, and José Avelino Victor. "Higher Education Students Dropout Prediction." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7689-6_11.

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Shi, Shuang, Shu Zhang, Jia Hao, Ken Chen, and Jun Wang. "MOOC Dropout Prediction Based on Bayesian Network." In Machine Learning for Cyber Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20102-8_40.

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Núñez-Naranjo, Aracelly Fernanda, Manuel Ayala-Chauvin, and Genís Riba-Sanmartí. "Prediction of University Dropout Using Machine Learning." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68285-9_38.

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Ouabou, Said, Abdellah Idrissi, Abdeslam Daoudi, and Moulay Ahmed Bekri. "School Dropout Prediction using Machine Learning Algorithms." In Modern Artificial Intelligence and Data Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33309-5_12.

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Ma, Xiaoxuan, Huan Huang, Shuai Yuan, and Rui Hou. "Dropout Prediction in MOOC Combining Behavioral Sequence Characteristics." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33614-0_18.

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Domínguez-Gómez, Daniel, Eduardo Sánchez-Jiménez, Yasmín Hernández, Juan de Dios González Torres, and Javier Ortiz-Hernandez. "Enhancing Dropout Prediction Models Through Feature Selection Techniques." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83882-8_6.

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Cuevas-Chávez, P. Alejandra, Samuel Narciso, Eduardo Sánchez-Jiménez, Itzel Celerino Pérez, Yasmín Hernández, and Javier Ortiz-Hernandez. "School Dropout Prediction with Class Balancing and Hyperparameter Configuration." In Advances in Computational Intelligence. MICAI 2023 International Workshops. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51940-6_2.

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Han, Tianxing, Pengyi Hao, and Cong Bai. "Structural and Temporal Learning for Dropout Prediction in MOOCs." In Knowledge Science, Engineering and Management. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10986-7_24.

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Ardchir, Soufiane, Amina Rachik, Youssef Ouassit, Reda Moulouki, and Mohamed Azzouazi. "An Efficient Feature Selection Approach for MOOCs Dropout Prediction." In Advanced Intelligent Systems for Sustainable Development (AI2SD’2020). Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90633-7_50.

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Conference papers on the topic "Dropout prediction"

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Surya, Anupma, Kunal Kumar, Megha Kumari, Kaushik Raj, and Pankaj Kumar. "Student Dropout Prediction for School Education." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895920.

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Kuntintara, Wichukorn, Piya Warabuntaweesuk, and Sountaree Rattapasakorn. "Student Dropout Prediction Using Machine Learning." In 2024 9th International Conference on Business and Industrial Research (ICBIR). IEEE, 2024. https://doi.org/10.1109/icbir61386.2024.10875840.

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Akter, Tamanna, Umme Ayman, Narayan Ranjan Chakraborty, Dewan Aminul Islam, Ayon Mazumder, and Md Hasan Imam Bijoy. "Dropout Prediction of University Students in Bangladesh using Machine Learning." In 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS). IEEE, 2024. https://doi.org/10.1109/compas60761.2024.10797033.

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Shrirao, Vedant, Parth Satokar, Monali Gulhane, et al. "Prediction Of Student Dropout Analysis For Education using Machine Learning." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932448.

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Gao, Qinghe, Daniel C. Miedema, Yidong Zhao, Jana M. Weber, Qian Tao, and Artur M. Schweidtmann. "Bayesian uncertainty quantification of graph neural networks using stochastic gradient Hamiltonian Monte Carlo." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.111298.

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Graph neural networks (GNNs) have proven state-of-the-art performance in molecular property prediction tasks. However, a significant challenge with GNNs is the reliability of their predictions, particularly in critical domains where quantifying model confidence is essential. Therefore, assessing uncertainty in GNN predictions is crucial to improving their robustness. Existing uncertainty quantification methods, such as Deep ensembles and Monte Carlo Dropout, have been applied to GNNs with some success, but these methods are limited to approximate the full posterior distribution. In this work, we propose a novel approach for scalable uncertainty quantification in molecular property prediction using Stochastic Gradient Hamiltonian Monte Carlo (SGHMC). Additionally, we utilize a cyclical learning rate to facilitate sampling from multiple posterior modes which improves posterior exploration within a single training round. Moreover, we compare the proposed methods with Monte Carlo Dropout and Deep ensembles, focusing on error analysis, calibration, and sharpness, considering both epistemic and aleatoric uncertainties. Our experimental results demonstrate that the proposed parallel-SGHMC approach significantly outperforms Monte Carlo Dropout and Deep ensembles in terms of calibration and sharpness. Specifically, parallel-SGHMC reduces the sum of squared errors by 99.4% and 75%, respectively, when compared to Monte Carlo Dropout and Deep Ensembles. These findings suggest that parallel-SGHMC is a promising method for uncertainty quantification in GNN-based molecular property prediction.

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Wu, Xiaoqin, Zhiwei Yu, Chi Zhang, and Zhiheng Zhiheng. "Research on MOOC dropout prediction by combining CNN-BiGRU and GCN." In Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), edited by Hui Yuan and Lu Leng. SPIE, 2025. https://doi.org/10.1117/12.3055872.

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Mojumder Anik, Md Sajjad, Sabiba Israt Zerin, Umme Ayman, Md Abdul Muntakim, Md Atif Asif Khan Akash, and Md Hasan Imam Bijoy. "Dropout Prediction of University Students in Bangladesh using Machine Learning Technique." In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE). IEEE, 2025. https://doi.org/10.1109/ecce64574.2025.11012934.

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Ohkawauchi, Takaaki, and Eriko Tanaka. "A Study on Real-Time Prediction of Course Dropout Students Using LMS Logs." In 2024 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2024. http://dx.doi.org/10.1109/iiai-aai63651.2024.00051.

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Luo, Guiqiang, Weizhong Zhao, Xingpeng Jiang, and Tingting He. "A Learning Style-aware Dropout Prediction Method via Fusing Global and Local Semantics." In 2024 International Conference on Intelligent Education and Intelligent Research (IEIR). IEEE, 2024. https://doi.org/10.1109/ieir62538.2024.10960124.

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Gouda, Giridhara, and Suma R. "Robotic based Pneumonia Diagnosis and Prediction using Deep Learning with Dropout Regularization Technique." In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2025. https://doi.org/10.1109/icdcece65353.2025.11034953.

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Reports on the topic "Dropout prediction"

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Roberson, Madeleine, Kathleen Inman, Ashley Carey, Isaac Howard, and Jameson Shannon. Probabilistic neural networks that predict compressive strength of high strength concrete in mass placements using thermal history. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/44483.

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This study explored the use of artificial neural networks to predict UHPC compressive strengths given thermal history and key mix components. The model developed herein employs Bayesian variational inference using Monte Carlo dropout to convey prediction uncertainty using 735 datapoints on seven UHPC mixtures collected using a variety of techniques. Datapoints contained a measured compressive strength along with three curing inputs (specimen maturity, maximum temperature experienced during curing, time of maximum temperature) and five mixture inputs to distinguish each UHPC mixture (cement type, silicon dioxide content, mix type, water to cementitious material ratio, and admixture dosage rate). Input analysis concluded that predictions were more sensitive to curing inputs than mixture inputs. On average, 8.2% of experimental results in the final model fell outside of the predicted range with 67.9%of these cases conservatively underpredicting. The results support that this model methodology is able to make sufficient probabilistic predictions within the scope of the provided dataset but is not for extrapolating beyond the training data. In addition, the model was vetted using various datasets obtained from literature to assess its versatility. Overall this model is a promising advancement towards predicting mechanical properties of high strength concrete with known uncertainties.

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Peterson, Warren. PR-663-19600-Z01 Develop Guidance for Calculation of HCDP in Pipelines. Pipeline Research Council International, Inc. (PRCI), 2020. http://dx.doi.org/10.55274/r0011659.

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To maintain the integrity and reliability of natural gas transportation systems, system operators ensure that products in transit remain in the gas phase under foreseeable operating conditions. Compliance with pipeline hydrocarbon dew point (HCDP) specifications are demonstrated though in-situ testing or predictive models based on Equations of State (EOS) calculations. Numerical prediction of HCDP is a product of contributing elements, including gas chromatography, calibration gas quality, thermophysical science and the experimental data that underpins equations of state. Some hydrocarbon mixtures, such as those from non-traditional gas supplies, are more difficult to sample and assess than others. The methods described in this paper and accompanying spreadsheet examples are designed to assist persons in making technically defendable decisions with respect to predictive methods and the operational impacts of liquid dropout. The primary focus of this work is to connect the over-all performance of HCDP prediction to its operational implications. The secondary objective of the work is to provide tools for assessing the potential benefit from using C9+ versus C6+ gas chromatographs.

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Rimpel, Aaron, and Amy McCleney. PR-316-17200-R02 A Study of the Effects of Liquid Contamination on Seal Performance. Pipeline Research Council International, Inc. (PRCI), 2020. http://dx.doi.org/10.55274/r0011734.

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Liquid contamination in dry gas seals (DGS) can come from a variety of sources, including lube oil carryover and liquid dropout, due to the Joule-Thompson effect across the seal faces, which can cause DGS failure. The physical effect of liquids on DGS performance is a topic of limited understanding, and conflicting theories exist regarding liquid-induced failure mechanisms. While tests have been performed on DGS test rigs (primarily by seal OEMs), very little testing or analysis has been specifically aimed at studying the heat generation behind DGS behavior following liquid injection, and test results or conclusions have not been published for use in the industry. Therefore, this study develops a test rig and presents test results of a DGS in dry nitrogen at different supply pressures up to 1,000 psi, then intentionally introduce a liquid (light oil, up to ~3% liquid mass fraction) to measure any difference in performance that might indicate a possible failure mechanism. It was found that continuous injection of oil caused a distinct 2-8% increase in torque but no significant effect on seal temperature for the brief durations tested. In contrast, multiphase CFD predictions predicted generally higher torque values, in nitrogen only and with similar levels of oil injection than experiments, and a 3-6% increase in stationary ring temperatures. To the authors' knowledge, the CFD modeling approach used is a first of its kind for trying to study liquid contamination effects in a DGS, and further work is proposed to improve comparisons to the test data. This is research performed by the Gas Machinery Research Council with cofunding by PRCI.

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