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Littérature scientifique sur le sujet « Prediction of Dropout behavior »

Auteur : Grafiati
Publié le 4 juin 2021
Mis à jour le 28 juillet 2024

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Articles de revues sur le sujet "Prediction of Dropout behavior"

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Yujiao, Zhang, Ling Weay Ang, Shi Shaomin et 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 (13 septembre 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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Chi, Zengxiao, Shuo Zhang et Lin Shi. « Analysis and Prediction of MOOC Learners’ Dropout Behavior ». Applied Sciences 13, no 2 (13 janvier 2023) : 1068. http://dx.doi.org/10.3390/app13021068.

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With the wide spread of massive open online courses ( MOOC ), millions of people have enrolled in many courses, but the dropout rate of most courses is more than 90%. Accurately predicting the dropout rate of MOOC is of great significance to prevent learners’ dropout behavior and reduce the dropout rate of students. Using the PH278x curriculum data on the Harvard X platform in spring 2013, and based on the statistical analysis of the factors that may affect learners’ final completion of the curriculum from two aspects: learners’ own characteristics and learners’ learning behavior, we established the MOOC dropout rate prediction models based on logical regression, K nearest neighbor and random forest, respectively. Experiments with five evaluation metrics (accuracy, precision, recall, F1 and AUC) show that the prediction model based on random forest has the highest accuracy, precision, F1 and AUC, which are 91.726%, 93.0923%, 95.4145%, 0.925341, respectively, its performance is better than that of the prediction model based on logical regression and that of the model based on K-nearest neighbor, whose values of these metrics are 91.395%, 92.8674%, 95.2337%, 0.912316 and 91.726%, 93.0923%, 95.4145% and 0.925341, respectively. As for recall metrics, the value of random forest is higher than that of KNN, but slightly lower than that of logistic regression, which are 0.992476, 0.977239 and 0.978555, respectively. Then, we conclude that random forests perform best in predicting the dropout rate of MOOC learners. This study can help education staff to know the trend of learners’ dropout behavior in advance, so as to put some measures to reduce the dropout rate before it occurs, thus improving the completion rate of the curriculum.
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Shou, Zhaoyu, Pan Chen, Hui Wen, Jinghua Liu et Huibing Zhang. « MOOC Dropout Prediction Based on Multidimensional Time-Series Data ». Mathematical Problems in Engineering 2022 (28 avril 2022) : 1–12. http://dx.doi.org/10.1155/2022/2213292.

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Massive open online courses have attracted millions of learners worldwide with flexible learning options. However, online learning differs from offline education in that the lack of communicative feedback is a drawback that magnifies high dropout rates. The analysis and prediction of student’s online learning process can help teachers find the students with dropout tendencies in time and provide additional help. Previous studies have shown that analyzing learning behaviors at different time scales leads to different prediction results. In addition, noise in the time-series data of student behavior can also interfere with the prediction results. To address these issues, we propose a dropout prediction model that combines a multiscale fully convolutional network and a variational information bottleneck. The model extracts multiscale features of student behavior time-series data by constructing a multiscale full convolutional network and then uses a variational information bottleneck to suppress the effect of noise on the prediction results. This study conducted multiple cross-validation experiments on KDD CUP 2015 data set. The results showed that the proposed method achieved the best performance compared to the baseline method.
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Zhang, Tiancheng, Hengyu Liu, Jiale Tao, Yuyang Wang, Minghe Yu, Hui Chen et Ge Yu. « Enhancing Dropout Prediction in Distributed Educational Data Using Learning Pattern Awareness : A Federated Learning Approach ». Mathematics 11, no 24 (16 décembre 2023) : 4977. http://dx.doi.org/10.3390/math11244977.

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Learning patterns are crucial for predicting student dropout in educational settings, providing insights into students’ behaviors and motivations. However, existing mainstream dropout prediction models have limitations in effectively mining these learning patterns and cannot mine these learning patterns in large-scale, distributed educational datasets. In this study, we analyze the representations of mainstream models and identify their inability to capture students’ distinct learning patterns and personalized variations across courses. Addressing these challenges, our study adopts a federated learning approach, tailoring the analysis to leverage distributed data while maintaining privacy and decentralization. We introduce the Federated Learning Pattern Aware Dropout Prediction Model (FLPADPM), which utilizes a one-dimensional convolutional neural network (CNN) and a bidirectional long short-term memory (LSTM) layer within a federated learning framework. This model is designed to effectively capture nuanced learning patterns and adapt to variations across diverse educational settings. To evaluate the performance of LPADPM, we conduct an empirical evaluation using the KDD Cup 2015 and XuetangX datasets. Our results demonstrate that LPADPM outperforms state-of-the-art models in accurately predicting student dropout behavior. Furthermore, we visualize the representations generated by LPADPM, which confirm its ability to effectively mine learning patterns in different courses. Our results showcase the model’s ability to capture and analyze learning patterns across various courses and institutions within a federated learning context.
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Janosz, Michel, Marc LeBlanc et Bernard Boulerice. « Consommation de psychotropes et délinquance : de bons prédicteurs de l’abandon scolaire ? » Criminologie 31, no 1 (1 septembre 2005) : 87–107. http://dx.doi.org/10.7202/017413ar.

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Although empirical links between deviant behavior and school dropout have been extensively demonstrated, the specific influence of drug use and delinquency on school dropout is still not clear and varies across studies. One reason for this lack of consistency may rests upon the way samples of dropouts have been analysed. Recently, Janosz, Le Blanc, Boulerice and Tremblay (1996) constructed and validated a typology of school dropout highlithing the social and psychological diversity of this population. Using a longitudinal sample of adolescents (N=791), we analyzed the predictive relationships of family rebelliousness, drug use and delinquency on school dropout. The results showed an important variability in the predictive relationships according to the type of dropouts. The necessity of considering the psychosocial heterogeneity of dropouts when conducting such studies is discussed.
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Keijsers, Ger P. J., Mirjam Kampman et Cees A. L. Hoogduin. « Dropout prediction in cognitive behavior therapy for panic disorder ». Behavior Therapy 32, no 4 (2001) : 739–49. http://dx.doi.org/10.1016/s0005-7894(01)80018-6.

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Siebra, Clauirton Albuquerque, Ramon N. Santos et Natasha C. Q. Lino. « A Self-Adjusting Approach for Temporal Dropout Prediction of E-Learning Students ». International Journal of Distance Education Technologies 18, no 2 (avril 2020) : 19–33. http://dx.doi.org/10.4018/ijdet.2020040102.

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This work proposes a dropout prediction approach that is able to self-adjust their outcomes at any moment of a degree program timeline. To that end, a rule-based classification technique was used to identify courses, grade thresholds and other attributes that have a high influence on the dropout behavior. This approach, which is generic so that it can be applied to any distance learning degree program, returns different rules that indicate how the predictions are adjusted along with academic terms. Experiments were carried out using four rule-based classification algorithms: JRip, OneR, PART and Ridor. The outcomes show that this approach presents better accuracy according to the progress of students, mainly when the JRip and PART algorithms are used. Furthermore, the use of this method enabled the generation of rules that stress the factors that mainly affect the dropout phenomenon at different degree moments.
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Albán, Mayra, David Mauricio et . « Decision Trees for the Early Identification of University Students at Risk of Desertion ». International Journal of Engineering & ; Technology 7, no 4.44 (1 décembre 2018) : 51. http://dx.doi.org/10.14419/ijet.v7i4.44.26862.

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The student's dropout at the universities is a topic that has generated controversy in Higher Education Institutions. It has negative effects which cause problems in the social, academic and economic context of the students. One of the alternatives used to predict the dropout at the universities is the implementation of machine learning techniques such as decision trees, known as prediction models that use logical construction diagrams to characterize the behavior of students and identify early students that at in risk of leaving university. Based on a survey of 3162 students, it was possible to obtain 10 variables that have influence into the dropout, that’s why, a CHAID decision tree model is proposed that presents the 97.95% of the accuracy in the prediction of the university students’ dropout. The proposed prediction model allows the administrators of the universities developing strategies for effective intervention in order to establish actions that allow students finishing their university careers successful.
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De Souza, Vanessa Faria, et Gabriela Perry. « Identifying student behavior in MOOCs using Machine Learning ». International Journal of Innovation Education and Research 7, no 3 (31 mars 2019) : 30–39. http://dx.doi.org/10.31686/ijier.vol7.iss3.1318.

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This paper presents the results literature review, carried out with the objective of identifying prevalent research goals and challenges in the prediction of student behavior in MOOCs, using Machine Learning. The results allowed recognizingthree goals: 1. Student Classification and 2. Dropout prediction. Regarding the challenges, five items were identified: 1. Incompatibility of AVAs, 2. Complexity of data manipulation, 3. Class Imbalance Problem, 4. Influence of External Factors and 5. Difficulty in manipulating data by untrained personnel.
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Bremer, V., P. Chow, B. Funk, F. Thorndike et L. Ritterband. « 1204 Analyzing User Journey Data In Digital Health : Predicting Dropout From A Digital CBT-I Intervention ». Sleep 43, Supplement_1 (avril 2020) : A460. http://dx.doi.org/10.1093/sleep/zsaa056.1198.

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Abstract Introduction Intervention dropout is an important factor for the evaluation and implementation of digital therapeutics, including in insomnia. Large amounts of individualized data (logins, questionnaires, EMA data) in these interventions can combine to create user journeys - the data generated by the path an individual takes to navigate the digital therapeutic. User journeys can provide insight about how likely users are to drop out of an intervention on an individual level and lead to increased prediction performance. Thus, the goal of this study is to provide a step-by-step guide for the analysis of user journeys and utilize this guide to predict intervention dropout, illustrated with an example from a data in a RCT of digital therapeutic for chronic insomnia, for which outcomes have previously been published. Methods Analysis of user journeys includes data transformation, feature engineering, and statistical model analysis, using machine learning techniques. A framework is established to leverage user journeys to predict various behaviors. For this study, the framework was applied to predict dropouts of 151 participants from a fully automated web-based program (SHUTi) that delivered cognitive behavioral therapy for insomnia. For this task, support vector machines, logistic regression with regularization, and boosted decision trees were applied at different points in 9-week intervention. These techniques were evaluated based on their predictive performance. Results After model evaluation, a decision tree ensemble achieved AUC values ranging between 0.6-0.9 based on application of machine earning techniques. Various handcrafted and theory-driven features (e.g., time to complete certain intervention steps, time to get out of bed after arising, and days since last system interaction contributed to prediction performance. Conclusion Results indicate that utilizing a user journey framework and analysis can predict intervention dropout. Further, handcrafted theory-driven features can increase prediction performance. This prediction of dropout could lead to an enhanced clinical decision-making in digital therapeutics. Support The original study evaluating the efficacy of this intervention has been reported elsewhere and was funded by grant R01 MH86758 from the National Institute of Mental Health.
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Thèses sur le sujet "Prediction of Dropout behavior"

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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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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.
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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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.
Education, Faculty of
Educational Studies (EDST), Department of
Graduate
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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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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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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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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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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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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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Livres sur le sujet "Prediction of Dropout behavior"

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

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Turner, Chandra Ramphal. Factors that put students at risk of leaving school before graduation. Scarborough, Ont : 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. [Washington, D.C.] : Center for Education Statistics, Office of Educational Research and Improvement, U.S. Dept. of Education, 1987.

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ERIC Clearinghouse on Adult, Career, and Vocational Education., dir. Adult learner retention revisited. Columbus, OH : 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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Fowler, Timothy B. Making the decision to drop out of high school : A bi-level analysis of the process in American schools. Chicago, Ill : University of Chicago, 1991.

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Rossi, Robert J. Evaluation of projects funded by the School Dropout Demonstration Assistance Program : Final evaluation report. [Washington, D.C.] : 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. [Washington, D.C.] : 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. [Washington, D.C.] : 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. Palo Alto, Calif : American Institutes for Research, 1995.

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

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Chapitres de livres sur le sujet "Prediction of Dropout behavior"

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Shayan, Parisa, Menno van Zaanen et Martin Atzmueller. « Predicting User Dropout from Their Online Learning Behavior ». Dans Discovery Science, 243–52. Cham : Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18840-4_18.

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Ma, Xiaoxuan, Huan Huang, Shuai Yuan et Rui Hou. « Dropout Prediction in MOOC Combining Behavioral Sequence Characteristics ». Dans Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 265–78. Cham : Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33614-0_18.

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Stewart, Latoya S., Andy V. Pham et John S. Carlson. « School Dropout ». Dans Encyclopedia of Child Behavior and Development, 1293–94. Boston, MA : Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_2501.

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Glenn, Andrea L., Farah Focquaert et Adrian Raine. « Prediction of Antisocial Behavior ». Dans Handbook of Neuroethics, 1689–701. Dordrecht : Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-4707-4_149.

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Krohn, Marvin D., Terence P. Thornberry, Lori Collins-Hall et Alan J. Lizotte. « School Dropout, Delinquent Behavior, and Drug Use ». Dans Drugs, Crime, and Other Deviant Adaptations, 163–83. Boston, MA : Springer US, 1995. http://dx.doi.org/10.1007/978-1-4899-0970-1_7.

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Saaty, Thomas L., et Luis G. Vargas. « Modeling Behavior in Competition : Chess ». Dans Prediction, Projection and Forecasting, 55–93. Dordrecht : Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-015-7952-0_5.

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Del Bonifro, Francesca, Maurizio Gabbrielli, Giuseppe Lisanti et Stefano Pio Zingaro. « Student Dropout Prediction ». Dans Lecture Notes in Computer Science, 129–40. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52237-7_11.

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Boukhechba, Mehdi, Abdenour Bouzouane, Bruno Bouchard, Charles Gouin-Vallerand et Sylvain Giroux. « Online Prediction of People’s Next Point-of-Interest : Concept Drift Support ». Dans Human Behavior Understanding, 97–116. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24195-1_8.

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Plumtree, A., et G. Shen. « Cyclic Deformation and Life Prediction Using Damage Mechanics ». Dans Mechanical Behavior of Materials, 77–85. Dordrecht : Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-1968-6_9.

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Summala, Heikki. « Modeling Driver Behavior : A Pessimistic Prediction ». Dans Human Behavior and Traffic Safety, 43–65. Boston, MA : Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-2173-6_4.

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Actes de conférences sur le sujet "Prediction of Dropout behavior"

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Wang, Lutong, et Hong Wang. « Learning Behavior Analysis and Dropout Rate Prediction Based on MOOCs Data ». Dans 2019 10th International Conference on Information Technology in Medicine and Education (ITME). IEEE, 2019. http://dx.doi.org/10.1109/itme.2019.00100.

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Li, Wentao, Min Gao, Hua Li, Qingyu Xiong, Junhao Wen et Zhongfu Wu. « Dropout prediction in MOOCs using behavior features and multi-view semi-supervised learning ». Dans 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727598.

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Prasanth, Anupama, et Haitham Alqahtani. « Predictive Modeling of Student Behavior for Early Dropout Detection in Universities using Machine Learning Techniques ». Dans 2023 IEEE 8th International Conference on Engineering Technologies and Applied Sciences (ICETAS). IEEE, 2023. http://dx.doi.org/10.1109/icetas59148.2023.10346531.

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Sigua, Edisson, Bryan Aguilar, Paola Pesantez-Cabrera et Jorge Maldonado-Mahauad. « Proposal for the Design and Evaluation of a Dashboard for the Analysis of Learner Behavior and Dropout Prediction in Moodle ». Dans 2020 XV Conferencia Latinoamericana de Tecnologias de Aprendizaje (LACLO). IEEE, 2020. http://dx.doi.org/10.1109/laclo50806.2020.9381148.

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Haiyang, Liu, Zhihai Wang, Phillip Benachour et Philip Tubman. « A Time Series Classification Method for Behaviour-Based Dropout Prediction ». Dans 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT). IEEE, 2018. http://dx.doi.org/10.1109/icalt.2018.00052.

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Ohira, Taku, Ryo Morita, Kazuhiro Tanji, Shinji Akiba, Koichi Niiyama, Fumio Inada et Kimitoshi Yoneda. « Prediction of Liquid Droplet Impingment Erosion (LDI) Trend in Actual NPP ». Dans ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77375.

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Liquid droplet impediment erosion (LDI) is a pipe wall thinning phenomena that a droplet accelerated by steam flow attacks pipe surface. It is difficult to evaluate LDI behavior in the system of NPP. Therefore, in current pipe wall thinning management, the pipe element is replaced conservatively when it is recognized to become thinner by pipe wall thinning measurement result. If LDI behavior at each point of the system can be estimated, it is possible to measure the pipe wall thickness and replace the pipe element at an appropriate time, in accordance with reasonable pipe wall measurement schedule. CRIEPI has constructed the evaluation system for LDI by using flow dynamics. This paper is concerned with the following items: • the applicability of the LDI evaluation system by a comparative verification between pipe wall thinning rate calculated by LDI system and that by actual measurement result; • establishment of remaining life assessment method of pipe in LDI environment by using LDI model.
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HAQUE, BAZLE Z. (GAMA), TAM NGUYEN, ISABEL CATUGAS, DANIEL J. O’BRIEN et JOHN W. GLLESPIE, JR. « MICROMECHANICAL FINITE ELEMENT MODELING OF UNIDIRECTIONAL COMPOSITES IN THREE DIMENSIONS : PREDICTION OF TRANSVERSE TENSILE & ; COMPRESSIVE, TRANSVERSE SHEAR & ; IN-PLANE SHEAR PROGRESSIVE DAMAGE BEHAVIOR ». Dans Thirty-sixth Technical Conference. Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/asc36/35839.

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Predicting the rate-dependent non-linear progressing damage behavior of unidirectional composites from the rate dependent properties of the constituents will enable computational materials-by-design and provide the fundamental understanding of the energy dissipating damage mechanisms. In this study, micromechanical finite element models of unidirectional glass-epoxy composites have been developed with fiber volume fractions, FVF = 0.40, 0.45, 0.50, 0.55, 0.60, 0.65, & 0.70; respectively with zero thickness fiber-matrix cohesive interfaces between the fibers and the surrounding matrix. Experimentally determined rate dependent non-linear stress-strain behavior of DER353 epoxy resin [1] (Tamrakar 2019) has been used to model the large deformation matrix behavior in conjunction with a rate dependent fiber-matrix interface traction law obtained from S-2 Glass/DER353 micro-droplet experiments & simulations [2] (Tamrakar 2019). Transverse tension, compression, in-plane shear, and transverse shear loads have been applied in predicting the progressive damage behavior of unidirectional S-2 Glass/DER353 epoxy composites.
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Liu, Kai, S. Tatinati et Andy W. H. Khong. « A Weighted Feature Extraction Technique Based on Temporal Accumulation of Learner Behavior Features for Early Prediction of Dropouts ». Dans 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). IEEE, 2020. http://dx.doi.org/10.1109/tale48869.2020.9368317.

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Gerber, A. G. « Inhomogeneous Multiphase Model for Nonequilibrium Phase Transition and Droplet Dynamics ». Dans ASME 2006 2nd Joint U.S.-European Fluids Engineering Summer Meeting Collocated With the 14th International Conference on Nuclear Engineering. ASMEDC, 2006. http://dx.doi.org/10.1115/fedsm2006-98460.

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This paper describes the development of an inhomogeneous multiphase model for the prediction of phase transition and nonequilibrium droplet dynamics under transonic flow conditions. The primary application of interest is low pressure steam turbines, where high speeds and complex geometry result in a second phase exhibiting significant droplet size variation, with associated thermal and inertial nonequilibrium relative to the vapor phase. The formulation uses a pressure based, implicit in time, algorithm with finite-volume/finite-element discretization of the conservation equations. For each phase, the velocity, energy state, volume fraction and droplet number are computed. For a two material phase system (water vapor and liquid) a parent and any number of (source based) condensed liquid phases are possible to handle the variety (and complexity) of droplet behavior as found in low pressure steam turbines. The model is tested against experimental data available in the steam turbine community. In particular the influence of inertial nonequilibrium on the phase transition behavior in a steam turbine cascade geometry is examined.
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Yan, Na, Tianwu Lin, Yiheng Zheng et Zhexin Xu. « Combining Periodic Feature and Behavioral Transfer With Ensemble of Models for MOOCs Dropout Prediction ». Dans 2021 IEEE International Conference on Engineering, Technology & Education (TALE). IEEE, 2021. http://dx.doi.org/10.1109/tale52509.2021.9678819.

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Rapports d'organisations sur le sujet "Prediction of Dropout behavior"

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Rimpel, Aaron, et Amy McCleney. PR-316-17200-R02 A Study of the Effects of Liquid Contamination on Seal Performance. Chantilly, Virginia : Pipeline Research Council International, Inc. (PRCI), juillet 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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Hanushek, Eric, Victor Lavy et Kohtaro Hitomi. Do Students Care about School Quality ? Determinants of Dropout Behavior in Developing Countries. Cambridge, MA : National Bureau of Economic Research, décembre 2006. http://dx.doi.org/10.3386/w12737.

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Valencia Quecano, Lira Isis, et Alfredo Guzmán Rincón. Explanatory Variables of Dropout in Postgraduate Education : A Scope Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, juin 2023. http://dx.doi.org/10.37766/inplasy2023.6.0011.

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Review question / Objective: To identify the individual, academic, socioeconomic, and institutional variables that influence student dropout at the postgraduate level (master's and doctoral), through a scope review. The following guiding questions were established: • RQ 1: What has been the bibliometric behavior of dropout publications in postgraduate students (master's and doctoral)? • RQ 2: What variables explain the dropout of postgraduate students (master's and doctoral) based on their categorization in individual, socioeconomic, academic, and institutional determinants? • RQ 3: What are the future research directions that should be addressed by academia in the study of dropout at the postgraduate level (master's and doctoral)?
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Linn, R. R. A transport model for prediction of wildfire behavior. Office of Scientific and Technical Information (OSTI), juillet 1997. http://dx.doi.org/10.2172/505313.

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Kimagai, Toru, et Motoyuki Akamatsu. Human Driving Behavior Prediction Using Dynamic Bayesian Networks. Warrendale, PA : SAE International, mai 2005. http://dx.doi.org/10.4271/2005-08-0305.

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Nau, Dana, et V. S. Subrahmanian. 2011 International Conference on Socio-Cultural Behavior and Prediction. Fort Belvoir, VA : Defense Technical Information Center, avril 2012. http://dx.doi.org/10.21236/ada567100.

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Nicholson, Sean, et Nicholas Souleles. Physician Income Prediction Errors : Sources and Implications for Behavior. Cambridge, MA : National Bureau of Economic Research, avril 2002. http://dx.doi.org/10.3386/w8907.

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Kolemen, Egemen, Jeff Schneider, Ryan Coffee et David Smith. Machine Learning for Real-time Fusion Plasma Behavior Prediction and Manipulation. Office of Scientific and Technical Information (OSTI), mars 2024. http://dx.doi.org/10.2172/2331298.

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Case, Scott W., et Kenneth L. Reifsnider. Advanced Composite Performance : Material Behavior and Life Cycle Prediction for Rotating Machines. Fort Belvoir, VA : Defense Technical Information Center, mars 2003. http://dx.doi.org/10.21236/ada414609.

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Andrews, Patricia L. BEHAVE : fire behavior prediction and fuel modeling system-BURN Subsystem, part 1. Ogden, UT : U.S. Department of Agriculture, Forest Service, Intermountain Research Station, 1986. http://dx.doi.org/10.2737/int-gtr-194.

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