Relevant bibliographies by topics / Dropouts

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Dropouts'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Dropouts"

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Wolman, Clara, Robert Bruininks, and Martha L. Thurlow. "Dropouts and Dropout Programs." Remedial and Special Education 10, no. 5 (September 1989): 6–20. http://dx.doi.org/10.1177/074193258901000504.

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Burton, Damon, and Rainer Martens. "Pinned by Their Own Goals: An Exploratory Investigation into Why Kids Drop out of Wrestling." Journal of Sport Psychology 8, no. 3 (September 1986): 183–97. http://dx.doi.org/10.1123/jsp.8.3.183.

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Previous research concludes that athletes drop out of sport because of conflicts of interest, but these findings cannot clarify whether dropouts find other activities more appealing or turn to new activities because sport fails to meet their achievement needs. This investigation assessed dropout motives by testing explanations derived from Nicholls' (1984) motivational model and comparing them with traditional dropout questionnaire responses. Wrestling coaches, participants, participants' parents, dropouts, and dropouts' parents completed a 23-item dropout inventory; and participants and dropouts responded to questionnaire items testing Nicholls' task choice predictions. Dropout inventory responses confirmed previous conflict-of-interest findings. In data testing Nicholls' model, participants demonstrated significantly higher perceived ability, better won-loss records, more functional attributions, and more positive expectancies, and valued wrestling success more than dropouts did. These findings supported predictions that wrestlers change activities when continued participation threatens their perceived ability. Disagreement between the conclusions concerning why young athletes drop out of wrestling drawn from the conflict-of-interest explanation and from Nicholls' perceived ability model are discussed, and suggestions for reducing dropout rates are offered.
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Löbig, Anna, Peter Ehnold, and Torsten Schlesinger. "„Da hatte ich einfach kein Interesse mehr daran.“ Analyse der Verläufe von Vereinsmitgliedschaftskarrieren jugendlicher Fußballer unter besonderer Berücksichtigung des Dropouts." Sport und Gesellschaft 17, no. 1 (April 28, 2020): 35–68. http://dx.doi.org/10.1515/sug-2020-0003.

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ZusammenfassungAusgehend von erhöhten Dropout-Quoten, welche mit negativen Konsequenzen für Verbände, Vereine oder Mitglieder einhergehen können, steht die Analyse typischer Vereinskarrieren jugendlicher Fußballer unter besonderer Berücksichtigung des Dropouts im Fokus des Beitrags. Ergänzend zu bisherigen, meist querschnittlich angelegten Studien wird dabei eine lebenslaufbezogene Perspektive eingenommen. Jugendliche Fußballer (n=15) aus verschiedenen Vereinen (n=4) wurden mit Hilfe des biografischen Mappings befragt. Dabei zeigen sich typische Verläufe in Abhängigkeit von der subjektiven Relevanz des Vereinswechsels (Vereins-Dropout) sowie des Fußball-Dropouts: „Vereins-Dropouts mit stabiler Fußballmitgliedschaft“ (Typ 1), „Plötzliche Fußball-Dropouts mit oder ohne vorherige Vereinswechsel“ (Typ 2), „Temporäre Fußball-Dropouts mit anschließendem Vereinswechsel“ (Typ 3) und „Instabile Fußballmitgliedschaftskarrieren“ (Typ 4). Diese Verlaufstypologien erweitern die bis-herige Befundlage um eine prozessuale/verlaufsbezogene Perspektive und eröffnen dadurch alternative Steuerungsoptionen im Umgang mit Dropouts.
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Andrea King-Dominguez, Andrea, Luis Amestica-Rivas, Victor Ramirez Gonzalez, and Francisco Ganga Contreras. "Student dropout, the economic cost for Chilean universities." Universidad Ciencia y Tecnología 27, no. 118 (February 27, 2023): 17–28. http://dx.doi.org/10.47460/uct.v27i118.683.

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Student dropout continues to be a significant challenge in higher education. Studies have shown that it has economic impacts on institutions. However, the cost associated with student dropout needs to be better understood. This research focuses on first-year dropouts in 51 Chilean universities, intending to analyze the costs generated by student dropouts in all their careers, allowing to project in an average horizon the resources that would no longer be received as a result of dropouts. It is descriptive research with a documentary analysis technique. Among the most outstanding results, it is shown that the institutions would lose around USD 1,169,853,410 and that private universities are the ones that register a higher cost associated with first-year university dropouts. On the other hand, it is statistically proven that for those universities that register a higher number of enrollments, the cost associated with dropout will be higher.
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Putri, Mailian, Jasmienti Jasmienti, Iswantir Iswantir, and Fauzan Fauzan. "Faktor-Faktor Yang Mempengaruhi Akhlak Anak Putus Sekolah di Jorong Balai Cubadak Nagari Taram Kecamatan Harau Kabupaten Lima Puluh Kota." Indonesian Research Journal On Education 3, no. 2 (March 8, 2023): 935–46. http://dx.doi.org/10.31004/irje.v3i2.7.

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This research is motivated by the morals of school dropouts in various forms. The morals of these school dropouts are commendable and some have despicable characters, not all school dropouts have despicable morals, but there are also commendable ones. Out-of-school children certainly do not get a full education so dropouts have despicable morals such as: being dishonest, likes to lie, talk rudely, and are also easily influenced by the outside environment which makes dropout children like to steal otherpeople's things. The purpose of this study was to determine the factors that influence the morals of school dropouts in Jorong Balai Cubadak Nagari Taram, Harau District, Lima Puluh Kota Regency so that the morals of dropouts have noble character. This type of research is descriptive qualitative research. In collecting data, the writer uses observation, interview, and documentation techniques. Meanwhile, to analyze it, the researcher took several steps, namely data reduction, presenting data, and drawingconclusions. Based on the results of the research that has been carried out, it can be concluded that the factors that influence the morals of school dropouts in Jorong Balai Cubadak are instinct, innate or inherited, environment, habits, will, and education. A bad environment will affect the morals of school dropouts to behave despicably and if the innate or inherited parents have good morals, the morals of dropout children will have noble character.
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Blais, Régis. "Assessing Patient Satisfaction with Health Care: Did You Drop Somebody?" Canadian Journal of Program Evaluation 5, no. 2 (September 1990): 1–13. http://dx.doi.org/10.3138/cjpe.5.001.

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Abstract: This study has three objectives: (a) to compare socio-demographic characteristics of dropouts and completers of a health promotion program, (b) to assess each group's satisfaction with specific aspects of the program, and (c) to investigate the reasons for dropout. Data were collected by means of individual structured interviews with 19 dropouts and 20 completers randomly selected. Results show that the two groups do not differ on socio-demographic variables. Completers were significantly more satisfied than dropouts with only a few aspects of care. Investigating dropouts' motives added key information. We conclude that useful measurement of patient satisfaction should focus on specific aspects of care and on survey dropouts as well as completers, and should assess dropouts' motives.
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Janosz, Michel, Marc LeBlanc, and Bernard Boulerice. "Consommation de psychotropes et délinquance : de bons prédicteurs de l’abandon scolaire ?" Criminologie 31, no. 1 (September 1, 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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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 (May 20, 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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Hammack, Floyd Morgan. "Large School Systems' Dropout Reports: An Analysis of Definitions, Procedures, and Findings." Teachers College Record: The Voice of Scholarship in Education 87, no. 3 (March 1986): 324–41. http://dx.doi.org/10.1177/016146818608700303.

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One basic problem for both researchers and policymakers is obtaining accurate information about dropouts. In this article, Floyd Hammack examines school district reports on the dropout problem in Boston, Los Angeles, Miami, New York City, San Diego, and Chicago. Citing the great diversity in the processes for the classification of students as dropouts, he raises important concerns about the comparability of dropout rates between districts.
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Wayman, Jeffrey C. "Factors Influencing Dropouts' GED & Diploma Attainment." education policy analysis archives 9 (February 8, 2001): 4. http://dx.doi.org/10.14507/epaa.v9n4.2001.

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This study examined correlates of degree attainment in high school dropouts. Participants were high school dropouts of Mexican American or non-Latino white descent who had no degree, a high school degree, or a GED certificate. This study was unique in that it accounted for sample bias of missing data through the use of multiple imputation, it considered students who had dropped out as early as 7th grade, and it was able to include variables found significant in previous research on returning dropouts. Logistic regression analyses identified a parsimonious set of factors which distinguished dropouts who held degrees (diploma or GED) from those who did not. Similar analyses were performed to distinguish participants who had attained diplomas from those who had attained GEDs. It was estimated that 59.2% of dropouts return to obtain high school credentials. School capability, age at dropout, and socio-economic status significantly predicted degree attainment. Presence of children, higher school capability and socio-economic status were associated with GED attainment, while later grade at dropout was associated with diploma attainment. These relationships did not vary by ethnicity, although degree attainment was less likely for Mexican American dropouts. The study concludes that dropping out is not the end of a student's education, and more research should be directed toward returning dropouts. Further, the focus of such research should be expanded to include a more positive and broader range of correlates.
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Dissertations / Theses on the topic "Dropouts"

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Huffman, Karen L. "Dropouts and dropout prevention programs how West Virginia high school principals view their roles /." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=631.

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Thesis (Ed. D.)--West Virginia University, 1999.
Title from document title page. Document formatted into pages; contains xii, 153 p. : ill. Vita. Includes abstract. Includes bibliographical references (p. 124-137).
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Clement, Priscillia. "High school dropout : systemic implications and the GED as a model of redress for dropouts /." View abstract, 2001. http://library.ccsu.edu/ccsu%5Ftheses/showit.php3?id=1639.

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Thesis (M.S.)--Central Connecticut State University, 2001.
Thesis advisor: Jane Fried. " ... in partial fulfillment of the requirements for the degree of Master of Science in School Counseling." Includes bibliographical references (leaves 75-80). Also available via the World Wide Web.
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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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Russell, Alecia Marie. "An Effective Dropout Prevention Program for Urban Students." Ashland University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ashland1260899676.

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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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Zabloski, James. "Gifted dropouts a phenomenological study /." Lynchburg, Va. : Liberty University, 2010. http://digitalcommons.liberty.edu.

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Byrne, Gregory A. "High school dropouts in Boston." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/71403.

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Miller, Cheryl. "Dropped out or pushed out a case study on why students drop out /." Menomonie, WI : University of Wisconsin--Stout, 2006. http://www.uwstout.edu/lib/thesis/2006/2006millerc.pdf.

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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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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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Books on the topic "Dropouts"

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Library of Congress. Major Issues System, ed. High school dropouts. [Washington, D.C.]: Congressional Research Service, Library of Congress, Major Issues System, 1986.

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Bartos, Judeen. High school dropouts. Detroit: Greenhaven Press, 2013.

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Marshood, Nabil Moh'd. Community Adjustment of Chronic Psychiatric Patients: Dropouts vs. Non-Dropouts. [New York, N.Y.?]: [publisher not identified], 1987.

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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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Pillay, G. Subramonia. Nondetention units and dropouts. Madurai: Nirmal, 1985.

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Institute for Educational Leadership (Washington, D.C.), ed. School dropouts: Everybody's problem. Washington, D.C: Institute for Educational Leadership, 1986.

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

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California. State Department of Education. Special Studies and Evaluation Reports Unit. California dropouts: A status report. Sacramento, Calif: California State Dept. of Education, 1986.

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Vance, Mary A. High school dropouts: A bibliography. Monticello, Ill: Vance Bibliographies, 1989.

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Earle, Janice. Female dropouts: A new perspective. Alexandria, VA: National Association of State Boards of Education, 1989.

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Book chapters on the topic "Dropouts"

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Nahler, Gerhard. "dropouts." In Dictionary of Pharmaceutical Medicine, 55. Vienna: Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_420.

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Tieben, Nicole. "Dropping Out of Higher Education in Germany: Using Retrospective Life Course Data to Determine Dropout Rates and Destinations of Non-completers." In Methodology of Educational Measurement and Assessment, 225–48. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27007-9_10.

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AbstractIn recent years, the high rates of higher education dropout have raised attention of education research and policy makers in Germany. However, due to data privacy legislation, it remains challenging to obtain information about the individual progress of students through higher education and the destinations of non-completers. With conventional administrative or cross-sectional data, it is not possible to distinguish non-completion from dropout, so that it mostly has to remain unclear if non-completers reach graduation elsewhere. This contribution uses the retrospective life course data of the NEPS starting cohort 6 to empirically disentangle non-completion and dropout of full-time students in higher education. We discuss the methodological challenges of conventional approaches and show how the advantages of retrospective life course data can be exploited for higher education research. We furthermore examine the destinations of non-completers and dropouts as well as the labour market returns of dropouts, using sequence data analyses and logistic regressions. Our results show that conventional designs possibly are prone to overestimate dropout rates. Longitudinal analyses of destinations after dropout reveal that the permeability between vocational training and higher education is not unidirectional. Vocational training is a relevant absorber of higher education dropouts, but at the same time, vocational qualifications that were gained prior to higher education work as safety-net that buffers labour market risks of dropouts.
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Ginzberg, Eli, Howard S. Berliner, and Miriam Ostow. "School Dropouts." In Young People at Risk, 105–21. New York: Routledge, 2021. http://dx.doi.org/10.4324/9780429267963-6.

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Pillai, S. Devadas. "Social dropouts." In Sociology Through Literature, 173–78. New York : Routledge, 2019.: Routledge India, 2019. http://dx.doi.org/10.4324/9780429288050-16.

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Stearns, Elizabeth. "High School Dropouts." In Encyclopedia of Adolescence, 1303–8. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-1695-2_58.

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Mahmoud, Magdi S. "Nonstationary Packet Dropouts." In Control and Estimation Methods over Communication Networks, 39–125. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04153-7_3.

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Yuan, Ying, Hoang Q. Nguyen, and Peter F. Thall. "Dealing with Dropouts." In Bayesian Designs for Phase I–II Clinical Trials, 245–52. Boca Raton : Taylor & Francis, 2016.: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/b21600-12.

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Stearns, Elizabeth. "High School Dropouts." In Encyclopedia of Adolescence, 1765–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-33228-4_58.

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Oliver, Tim. "A Europe of dropouts?" In The Crisis of the European Union, 129–40. Abingdon, Oxon ; New York, NY : Routledge, 2018. | Series: Routledge advances in European politics ; 134: Routledge, 2017. http://dx.doi.org/10.4324/9781315443683-10.

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Egger, Rudolf, and Sandra Hummel. "STOPOUTS, DROPOUTS ODER FLEXIBLE PATHWAYS?" In Lernweltforschung, 31–57. Wiesbaden: Springer Fachmedien Wiesbaden, 2020. http://dx.doi.org/10.1007/978-3-658-23283-2_2.

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Conference papers on the topic "Dropouts"

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Chicioreanu, Teodora daniela, and Paulina Spanu. "STUDENTS' PERSPECTIVE. THE MAIN FACTORS THAT CONTRIBUTE TO STUDENTS' DROPOUTS." In eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-155.

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Students' Perspective. The Main Factors That Contribute To Students' Dropouts. This qualitative study aims at analyzing the factors that contribute to dropout among students. The research was conducted on six different campuses ( University of Camerino - Italy, University POLITEHNICA of Bucharest - Romania, University Panepistimio Thessalias - Greece, Socialini? Moksl? Kolegija - Lietuva, Spain Zaragoza's University - Spain, Cukurova University - Turkey) and consisted of an open-ended interview with a sample of students. Data from the interviews were analyzed using a thematic constant comparison approach. The analysis of the data was based on Qualitative Content Analysis. The main themes were identified. The results reveal that the factors contributing to dropout were mainly related with the personal characteristics of the students, such as gender, age, the type of institution, and the knowledge of the institution. These characteristics were also related to the attitudes of the institution and the faculty toward the students. College factors that can hinder students were also identified. The students' perspective is helpful in assisting the school and the parents to discover the causes of dropout. The school and the parents should be aware that dropouts have negative effects on their personal development by delaying the students' academic development. The school should also take care of the students' personal relationships with teachers, classmates, parents, and siblings. They should take care of the students' personal relationships with classmates and with teachers.A survey was conducted with 540 students who had completed the clinical clerkship. The response rate was 84%(475 valid answers)
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"VOCATIONAL SCHOOL DROPOUTS’ PERSPECTIVE OF DROPOUT RISK AND PROTECTIVE FACTORS." In International Psychological Applications Conference and Trends. inScience Press, 2023. http://dx.doi.org/10.36315/2023inpact128.

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Sánchez-Nítola, Mónica Natalí, Diana Grijalba, Manuel Embus, Andrés Suarez, and Juan Esteban Guevara Montoya. "Dropout and Engineering undergraduate programs at the Universidad Nacional de Colombia between 2012-2019." In Sixth International Conference on Higher Education Advances. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/head20.2020.11270.

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Dropout is a topic of significant concern to Higher Education Institutions, especially in Colombia it is considered one of the most important indicators to define higher education quality. Nevertheless, in Colombia, and particularly at the Universidad Nacional de Colombia, it has been generally assessed only academic dropout. This decision has prevented understanding other types of dropouts that also occur in the university. The research group performed Multiple Correspondence Analysis to assess the relationship between dropout types (academic, dropout on request, and expiry of time limits) and variables such as sex, socioeconomic stratum, type of admission, and semester of dropout. In the results, we found a disparity between types of dropout concerning the type of university admission, sex, socio-economic stratum, and the semester in which dropout occurs. Women are related to the dropout request while men are associated with the academic dropout. Additionally, the academic dropout occurs in the first 4 semesters, and the expiry of time limits occurs in the last ones.
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Maric, Jasmina, and Lekshmi Murali Rani. "The Wicked Problem of Dropouts." In CHI '24: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3613905.3637129.

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Deaconescu, R., G. Milescu, and N. Tapus. "Simulating connection dropouts in BitTorrent environments." In IEEE EUROCON 2011 - International Conference on Computer as a Tool. IEEE, 2011. http://dx.doi.org/10.1109/eurocon.2011.5929398.

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Jinguang Chen, Jiancheng Li, and Lili Ma. "Nonlinear filtering with multiple packet dropouts." In 2010 International Conference on Image Analysis and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/iasp.2010.5476156.

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Shaukat, Salman Ali, Khalid Munawar, Muhammad Arif, Aamer Iqbal Bhatti, Umar Iqbal Bhatti, and Ubaid M. Al-Saggaf. "Robust vehicle localization with GPS dropouts." In 2016 6th International Conference on Intelligent and Advanced Systems (ICIAS). IEEE, 2016. http://dx.doi.org/10.1109/icias.2016.7824135.

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Chen, Xiyuan, Xingyu Li, Yi Zhou, and Tianming Yang. "DDDM: A Brain-Inspired Framework for Robust Classification." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/397.

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Despite their outstanding performance in a broad spectrum of real-world tasks, deep artificial neural networks are sensitive to input noises, particularly adversarial perturbations. On the contrary, human and animal brains are much less vulnerable. In contrast to the one-shot inference performed by most deep neural networks, the brain often solves decision-making with an evidence accumulation mechanism that may trade time for accuracy when facing noisy inputs. The mechanism is well described by the Drift-Diffusion Model (DDM). In the DDM, decision-making is modeled as a process in which noisy evidence is accumulated toward a threshold. Drawing inspiration from the DDM, we propose the Dropout-based Drift-Diffusion Model (DDDM) that combines test-phase dropout and the DDM for improving the robustness for arbitrary neural networks. The dropouts create temporally uncorrelated noises in the network that counter perturbations, while the evidence accumulation mechanism guarantees a reasonable decision accuracy. Neural networks enhanced with the DDDM tested in image, speech, and text classification tasks all significantly outperform their native counterparts, demonstrating the DDDM as a task-agnostic defense against adversarial attacks.
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Han, Chunyan, and Wei Wang. "Deconvolution estimation of systems with packet dropouts." In 2013 25th Chinese Control and Decision Conference (CCDC). IEEE, 2013. http://dx.doi.org/10.1109/ccdc.2013.6561763.

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Aboltins, Aivars, Svetlana Atslega, Natalija Sergejeva, and Liene Strupule. "One of opportunities to reduce student dropouts." In 18th International Scientific Conference Engineering for Rural Development. Latvia University of Life Sciences and Technologies, 2019. http://dx.doi.org/10.22616/erdev2019.18.n479.

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Reports on the topic "Dropouts"

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Heckman, James, Jeffrey Smith, and Christopher Taber. Accounting for Dropouts in Evaluations of Social Experiments. Cambridge, MA: National Bureau of Economic Research, September 1994. http://dx.doi.org/10.3386/t0166.

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Sealza, Lita. Factors affecting the family planning program drop-out rate in Bukidnon, Philippines. Population Council, 1994. http://dx.doi.org/10.31899/rh1994.1002.

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This study has involved interviews with a randomly selected sample of 389 family planning (FP) acceptors from 20 barangays in Bukidnon province, Philippines. Respondents were currently married women who had either continued to use family planning (FP) since their initial acceptance (current users) or who had stopped using FP altogether (dropouts). Findings from the present study are expected to shed light on several aspects of the dropout problem, namely: How extensive is this problem and how accurate are the DOH records in this regard? How do FP acceptors view their local FP clinic and the services offered? What reasons are given by the dropouts for their decision to stop using FP? What are the factors associated with dropping out? As noted in this report, the study was made possible through support provided by the Population Council under its Family Planning Operations Research and Training (FPORT) Program.
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Clemens, Jeffrey, Lisa Kahn, and Jonathan Meer. Dropouts Need Not Apply? The Minimum Wage and Skill Upgrading. Cambridge, MA: National Bureau of Economic Research, May 2020. http://dx.doi.org/10.3386/w27090.

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Hadley, William. Survey on Dropouts from Graduate Schools of Social Work, 1970-1972. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1725.

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Tyler, John, Richard Murnane, and John Willett. Do the Cognitive Skills of School Dropouts Matter in the Labor Market? Cambridge, MA: National Bureau of Economic Research, April 1999. http://dx.doi.org/10.3386/w7101.

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Tyler, John, and Magnus Lofstrom. Is the GED an Effective Route to Postsecondary Education for School Dropouts? Cambridge, MA: National Bureau of Economic Research, February 2008. http://dx.doi.org/10.3386/w13816.

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Oreopoulos, Philip. Do Dropouts Drop Out Too Soon? International Evidence From Changes in School-Leaving Laws. Cambridge, MA: National Bureau of Economic Research, December 2003. http://dx.doi.org/10.3386/w10155.

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Ortagus, Justin, Melvin Tanner, and Isaac McFarlin. Can Re-Enrollment Campaigns Help Dropouts Return to College? Evidence from Florida Community Colleges. Cambridge, MA: National Bureau of Economic Research, January 2020. http://dx.doi.org/10.3386/w26649.

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Crooker, N. U., D. E. Larson, S. W. Kahler, S. M. Lamassa, and H. E. Spence. Suprathermal Electron Isotropy in High-Beta Solar Wind and Its Role in Heat Flux Dropouts. Fort Belvoir, VA: Defense Technical Information Center, June 2003. http://dx.doi.org/10.21236/ada423089.

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Mouikis, C. G., A. Korth, R. W. Friedel, and J. Fennel. Dawn/Dusk Dropouts Due to Storms/Substorms Near the Outer Radiation Belt: Observations From CRRESS. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada383642.

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