Relevant bibliographies by topics / Dropout behavior, Prediction of Australia

Academic literature on the topic 'Dropout behavior, Prediction of Australia'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Dropout behavior, Prediction of Australia"

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Chi, Zengxiao, Shuo Zhang, and Lin Shi. "Analysis and Prediction of MOOC Learners’ Dropout Behavior." Applied Sciences 13, no. 2 (January 13, 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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Keijsers, Ger P. J., Mirjam Kampman, and 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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Shou, Zhaoyu, Pan Chen, Hui Wen, Jinghua Liu, and Huibing Zhang. "MOOC Dropout Prediction Based on Multidimensional Time-Series Data." Mathematical Problems in Engineering 2022 (April 28, 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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Albán, Mayra, David Mauricio, and . "Decision Trees for the Early Identification of University Students at Risk of Desertion." International Journal of Engineering & Technology 7, no. 4.44 (December 1, 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, and Gabriela Perry. "Identifying student behavior in MOOCs using Machine Learning." International Journal of Innovation Education and Research 7, no. 3 (March 31, 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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Siebra, Clauirton Albuquerque, Ramon N. Santos, and 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 (April 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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Tang, Xingqiu, Hao Zhang, Ni Zhang, Huan Yan, Fangfang Tang, and Wei Zhang. "Dropout Rate Prediction of Massive Open Online Courses Based on Convolutional Neural Networks and Long Short-Term Memory Network." Mobile Information Systems 2022 (May 16, 2022): 1–11. http://dx.doi.org/10.1155/2022/8255965.

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Massive open online courses (MOOC) is characterized by large scale, openness, autonomy, and personalization, attracting increasingly students to participate in learning and gaining recognition from more and more people. This paper proposes a network model based on convolutional neural networks and long short-term memory network (CNN-LSTM) for MOOC dropout prediction task. The model selects 43-dimensional behavioral features as input from students’ learning activity logs and adopts the CNN model to automatically extract continuous features over a period of time from students’ learning activity logs. At the same time, considering the time sequence of students’ learning behavior characteristics, a MOOC dropout prediction model was established by using long short-term memory network to obtain students’ learning status at different time steps. The algorithm proposed in this chapter was trained and evaluated on the public dataset provided by the KDD Cup 2015 competition. Compared with the dropout prediction methods based on LSTM and CNN-RNN, the model improved the AUC by 2.7% and 1.4%, respectively. The result in this paper is a good predictor of dropout rates and is expected to provide teaching aid to teachers.
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Chen, Jing, Jun Feng, Xia Sun, Nannan Wu, Zhengzheng Yang, and Sushing Chen. "MOOC Dropout Prediction Using a Hybrid Algorithm Based on Decision Tree and Extreme Learning Machine." Mathematical Problems in Engineering 2019 (March 18, 2019): 1–11. http://dx.doi.org/10.1155/2019/8404653.

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Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior discrepancy leading to a wide range of fluctuation of prediction results. Besides, previous methods require iterative training which is time intensive. To address these problems, we propose DT-ELM, a novel hybrid algorithm combining decision tree and extreme learning machine (ELM), which requires no iterative training. The decision tree selects features with good classification ability. Further, it determines enhanced weights of the selected features to strengthen their classification ability. To achieve accurate prediction results, we optimize ELM structure by mapping the decision tree to ELM based on the entropy theory. Experimental results on the benchmark KDD 2015 dataset demonstrate the effectiveness of DT-ELM, which is 12.78%, 22.19%, and 6.87% higher than baseline algorithms in terms of accuracy, AUC, and F1-score, respectively.
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Tamada, Mariela Mizota, Rafael Giusti, and José Francisco de Magalhães Netto. "Predicting Students at Risk of Dropout in Technical Course Using LMS Logs." Electronics 11, no. 3 (February 5, 2022): 468. http://dx.doi.org/10.3390/electronics11030468.

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Educational data mining is a process that aims at discovering patterns that provide insight into teaching and learning processes. This work uses Machine Learning techniques to create a student performance prediction model, using academic data and records from a Learning Management System, that correlates with success or failure in completing the course. Six algorithms were employed, with models trained at three different stages of their two-year course completion. We tested the models with records of 394 students from 3 courses. Random Forest provided the best results with 84.47% on the F1 score in our experiments, followed by Decision Tree obtaining similar results in the first subjects. We also employ clustering techniques and find different behavior groups with a strong correlation to performance. This work contributes to predicting students at risk of dropping out, offers insight into understanding student behavior, and provides a support mechanism for academic managers to take corrective and preventive actions on this problem.
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Shang, Xiaoran, Bangbo Huang, and Hongbin Ma. "Multifeedback Behavior-Based Interest Modeling Network for Adaptive Click-Through Rate Prediction." Mobile Information Systems 2022 (August 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/3529928.

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With the rapid development of the Internet, the recommendation system is becoming more and more important in people’s life. Click-through rate prediction is a crucial task in the recommendation system, which directly determines the effect of the recommendation system. Recently, researchers have found that considering the user behavior sequence can greatly improve the accuracy of the click-through rate prediction model. However, the existing prediction models usually use the user click behavior sequence as the input of the model, which will make it difficult for the model to obtain a comprehensive user interest representation. In this paper, a unified multitype user behavior sequence modeling framework named as MBIN, a.k.a. multifeedback behavior-based Interest modeling network, is proposed to cope with uncertainties in the noisy data. The proposed adaptive model uses deep learning technology, obtains user interest representation through multihead attention, denoises user interest representation using the vector projection method, and fuses the user interests using adaptive dropout technology. First, an interest denoising layer is proposed in the MBIN, which can effectively mitigate the noise problem in user behavior sequences to obtain more accurate user interests. Second, an interest fusion layer is introduced so as to effectively model and fuse various types of interest representations of users to achieve personalized interest fusion. Then, we used auxiliary losses based on behavior sequences to enhance the effect of behavior sequence modeling and improve the effectiveness of user interest characterization. Finally, we conduct extensive experiments based on real-world and large-scale dataset to validate the effectiveness of our approach in CTR prediction tasks.
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Dissertations / Theses on the topic "Dropout behavior, Prediction of Australia"

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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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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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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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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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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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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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Mayer, Jill A. "An analysis of at-risk rural Wisconsin high school student deficient reading skills and the potential of students to drop out of high school." Online version, 2009. http://www.uwstout.edu/lib/thesis/2009/2009mayerj.pdf.

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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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Wilsie, Carisa Caro Knight Elizabeth Brestan. "An evaluation of treatment drop-out families with a history of child physical abuse /." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Psychology/Thesis/Wilsie_Carisa_33.pdf.

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Books on the topic "Dropout behavior, Prediction of Australia"

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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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Dropout prevention handbook: Apprenticeships and other solutions. Lancaster, Pa: Technomic Pub. Co., 1994.

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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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Gillespie, Maggie. Factors affecting student persistence: A longitudinal study. Iowa City, Iowa: American College Testing Program, 1992.

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Gillespie, Maggie. Factors affecting student persistence: A longitudinal study. Iowa City, Iowa: American College Testing Program, 1992.

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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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Bonikowske, Dennis. Truancy: A prelude to dropping out. Bloomington, Ind: National Educational Service, 1987.

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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. [Washington, D.C.]: U.S. Dept. of Education, Office of the Undersecretary, 1995.

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Conference papers on the topic "Dropout behavior, Prediction of Australia"

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Wang, Lutong, and Hong Wang. "Learning Behavior Analysis and Dropout Rate Prediction Based on MOOCs Data." In 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, and Zhongfu Wu. "Dropout prediction in MOOCs using behavior features and multi-view semi-supervised learning." In 2016 International Joint Conference on Neural Networks (IJCNN). IEEE, 2016. http://dx.doi.org/10.1109/ijcnn.2016.7727598.

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

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Erickson, Dale Douglas, and Greg Metcalf. "Online Modeling of CO2 Storage Systems." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31202-ms.

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Abstract This paper discusses the development and deployment of a specialized online and offline integrated model to simulate the CO2 (Carbon Dioxide) Injection process. There is a very high level of CO2 in an LNG development and the CO2 must be removed in order to prepare the gas to be processed into LNG. To mitigate the global warming effects of this CO2, a large portion of the CO2 Rich Stream (98% purity) is injected back into a depleted oil field. To reduce costs, carbon steel flowlines are used but this introduces a risk of internal corrosion. The presence of free water increases the internal corrosion risk, and for this reason, a predictive model discussed in this paper is designed to help operations prevent free water dropout in the network in real time. A flow management tool (FMT) is used to monitor the current state of the system and helps look at the impact of future events (startup, shutdowns etc.). The tool models the flow of the CO2 rich stream from the outlet of the compressor trains, through the network pipeline and manifolds and then into the injection wells. System behavior during steady state and transient operation is captured and analyzed to check water content and the balance of trace chemicals along with temperature and pressure throughout the network helping operators estimate corrosion rates and monitor the overall integrity of the system. The system has been running online for 24/7 for 2 years. The model has been able to match events like startup/shutdown, cooldowns and blowdowns. During these events the prediction of temperature/pressure at several locations in the field matches measured data. The model is then able to forecasts events into the future to help operations plan how they will operate the field. The tool uses a specialized thermodynamic model to predict the dropout of water in the near critical region of CO2 mixtures which includes various impurities. The model is designed to model startup and shutdown as the CO2 mixture moves across the phase boundary from liquid to gas or gas to liquid during these operations.
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