Relevant bibliographies by topics / Achievement Prediction

Contents

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

Academic literature on the topic 'Achievement Prediction'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Achievement Prediction"

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Ahmad, Imam, Heni Sulistiani, and Hendrik Saputra. "The Application Of Fuzzy K-Nearest Neighbour Methods for A Student Graduation Rate." Indonesian Journal of Artificial Intelligence and Data Mining 1, no. 1 (2018): 47. http://dx.doi.org/10.24014/ijaidm.v1i1.5654.

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The absence of prediction system that can provide prediction analysis on the graduation rate of students becomes the reason for the research on the prediction of the level of graduation rate of students. Determining predictions of graduation rates of students in large numbers is not possible to do manually because it takes a long time. For that we need an algorithm that can categorize predictions of students' graduation rates in computing. The Fuzzy Method and KNN or K-Nearest Neighbor Methods are selected as the algorithm for the prediction process. In this study using 10 criteria as a materi
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Liu, Chuang, Haojie Wang, Yingkui Du, and Zhonghu Yuan. "A Predictive Model for Student Achievement Using Spiking Neural Networks Based on Educational Data." Applied Sciences 12, no. 8 (2022): 3841. http://dx.doi.org/10.3390/app12083841.

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Student achievement prediction is one of the most important research directions in educational data mining. Student achievement directly reflects students’ course mastery and lecturers’ teaching level. Especially for the achievement prediction of college students, it not only plays an early warning and timely correction role for students and teachers, but also provides a method for university decision-makers to evaluate the quality of courses. Based on the existing research and experimental results, this paper proposes a student achievement prediction model based on evolutionary spiking neural
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Mantzicopoulos, Panayota Y., and Delmont Morrison. "Early Prediction of Reading Achievement." Remedial and Special Education 15, no. 4 (1994): 244–51. http://dx.doi.org/10.1177/074193259401500406.

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The present study examined the early profiles of students who were screened for learning difficulties with search (Silver & Hagin, 1981), an early identification measure, and were found to be adequate or inadequate readers at the end of second grade. In order to gain insight into the problem of inaccurate classifications of atrisk status at kindergarten, this study investigated students' performance on cognitive measures and behavior problems, as well as socioeconomic background and teacher predictions of learning difficulties. Our results indicated that search tends to underidentify as no
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Nuankaew, Pratya, Tipparat Sittiwong, and Wongpanya Sararat Nuankaew. "Characterization Clustering of Educational Technologists Achievement in Higher Education Using Machine Learning Analysis." International Journal of Information and Education Technology 12, no. 9 (2022): 881–87. http://dx.doi.org/10.18178/ijiet.2022.12.9.1697.

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The research purpose was to develop a model for predicting cluster achievement of educational technologists. There are three research objectives: 1) to study the context of educational technologists’ achievements in higher education, 2) to construct a model for predicting learning achievement of educational technologists in higher education, and 3) to evaluate a model for predicting learning achievement of educational technologists in higher education. The research scope was to study the success cluster of educational technologists in Thailand. The research data were 98 students from the Bache
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Tong, Tingting, and Zhen Li. "Predicting learning achievement using ensemble learning with result explanation." PLOS ONE 20, no. 1 (2025): e0312124. https://doi.org/10.1371/journal.pone.0312124.

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Predicting learning achievement is a crucial strategy to address high dropout rates. However, existing prediction models often exhibit biases, limiting their accuracy. Moreover, the lack of interpretability in current machine learning methods restricts their practical application in education. To overcome these challenges, this research combines the strengths of various machine learning algorithms to design a robust model that performs well across multiple metrics, and uses interpretability analysis to elucidate the prediction results. This study introduces a predictive framework for learning
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Song, Yinqiu, Xianqiu Meng, and Jianhua Jiang. "Multi-Layer Perception model with Elastic Grey Wolf Optimization to predict student achievement." PLOS ONE 17, no. 12 (2022): e0276943. http://dx.doi.org/10.1371/journal.pone.0276943.

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This study proposes a Grey Wolf Optimization (GWO) variant named Elastic Grey Wolf Optimization algorithm (EGWO) with shrinking, resilient surrounding, and weighted candidate mechanisms. Then, the proposed EGWO is used to optimize the weights and biases of Multi-Layer Perception (MLP), and the EGWO-MLP model for predicting student achievement is thus obtained. The training and verification of the EGWO-MLP prediction model are conducted based on the thirty attributes from the University of California (UCI) Machine Learning Repository dataset’s student performance dataset, including family featu
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Pradnyana, I. Putu Yudi, I. Gusti Ayu Sri Darmayani, I. Gusti Ayu Harry Sundariyati, and Ida Bagus Ametha Putra Manuaba. "RELATIONSHIP BETWEEN UNIVERSITY ADMISSION SELECTION AND ACADEMIC PREDICTION IN MEDICAL STUDENTS." E-Jurnal Medika Udayana 14, no. 03 (2025): 107. https://doi.org/10.24843/mu.2025.v14.i3.p17.

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Selection of new students at the undergraduate level in public universities is carried out in 3 selection pathways, namely SNMPTN, SBMPTN, and Mandiri. Selection of student admission exams will affect the quality of students which can be measured by student academic achievement, including GPA and academic competition winners. The purpose of this study was to see the differences in academic achievement and the relationship between admission pathways and student academic achievement. This study is an analytic study through a cross-sectional approach conducted at the Faculty of Medicine, Udayana
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Nugraha, Fajar, Widowati Widowati, and Aris Sugiharto. "Machine Learning Methods for Academic Achievement Prediction: A Bibliometric Review." Jurnal Sistem Informasi Bisnis 15, no. 2 (2025): 221–26. https://doi.org/10.14710/vol15iss2pp221-226.

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This study examines research trends regarding the prediction of academic achievement using machine learning. Research in the field of academic achievement is currently continuing to develop, but has not been explored comprehensively in a bibliometric context. The visualization provided includes a map of publication development using machine learning methods based on country, analysis of bibliographic pairs and keywords used. To find out the visualization results, bibliographic analysis was used using VOSviewer. The data used in this analysis were 76 articles collected from the Scopus database
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Musfikar, Rahmat, Henita Apriadinata, and Bustami Yusuf. "Aplikasi Prediksi Prestasi pada Siswa Menggunakan Algoritma C4.5." Jurnal Manajemen Informatika (JAMIKA) 13, no. 2 (2023): 148–62. http://dx.doi.org/10.34010/jamika.v13i2.10649.

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The development of computer technology that is increasingly modern, fast and practical greatly affects the lives of the wider community. With the existence of increasingly sophisticated technology, a country's education can foster people's abilities, form personalities and progress a dignified country in the context of educating the country's life. Therefore, education is becoming an increasingly large impact that encourages students to achieve the best achievements. One of the factors that support student achievement is craft, adequate facilities, academic grades and also quality educators. T
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Jaiswal, K. C., P. D. Pathak, V. J. Pawar, and K. D. Kharat. "Prediction of degree student achievement analysis." IOP Conference Series: Materials Science and Engineering 1070, no. 1 (2021): 012057. http://dx.doi.org/10.1088/1757-899x/1070/1/012057.

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Dissertations / Theses on the topic "Achievement Prediction"

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Gamaroff, Raphael. "English proficiency testing and the prediction of academic achievement." Doctoral thesis, University of Cape Town, 1998. http://hdl.handle.net/11427/17870.

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Includes bibliographical references.<br>The study investigates the ability of English proficiency tests (1) to measure levels of English proficiency among learners who have English as the medium of teaching and learning, and (2) to predict long-term academic achievement (Grade 7 to Grade 12). The tests are "discrete-point" tests, namely, error recognition and grammar tests (both multiple-choice tests), and "integrative" tests, namely, cloze tests, essay tests and dictation tests. The sample of subjects consists of two groups: (1) those taking English as a First Language subject and those takin
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McIlroy, David. "Achievement in education : improving measurement and testing models and predictors." Thesis, University of Ulster, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342390.

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Smits, Niels. "Academic specialization choices and academic achievement prediction and incomplete data /." [S.l. : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2003. http://dare.uva.nl/document/66978.

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Moore, Debra. "IQ and achievement verbal-performance differences as moderators /." Access abstract and link to full text, 1988. http://0-wwwlib.umi.com.library.utulsa.edu/dissertations/fullcit/8805400qm.

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Henschler, M. Jane Lamal. "COMPASS-Reading Scores as a predictor for success in the general education course, Written Communication." Menomonie, WI : University of Wisconsin--Stout, 2006. http://www.uwstout.edu/lib/thesis/2006/2006henschlerj.pdf.

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Frisco, Michelle Lynn. "Adolescents' future opportunity : family, sexual decision-making, and academic achievement /." Access restricted to users with UT Austin EID Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3031598.

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Rich, Laura M. "A comparison of achievement-related parenting styles for children with average or low achievement and with learning disabilities." Virtual Press, 1996. http://liblink.bsu.edu/uhtbin/catkey/1019478.

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In order to study Maccoby & Martin's (1983) dimensions of parental responsiveness and demandingness among different achievement groups, 237 students were divided into average and low achievers and those with learning disabilities. The students completed the Children's Report of Parental Behavior Inventory (Schaefer, 1965) and their group scores on the three demandingness (Lax Discipline, Enforcement of Discipline, and Extreme Autonomy) and three responsiveness (Child-Centeredness, Rejection, and Acceptance of Individuation) subscales were compared. The MANOVA investigating responsiveness uncov
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DeFelice, Carol Esselborn. "The relationship between self-efficacy and academic achievement in associate degree nursing programs /." Access Digital Full Text version, 1989. http://pocketknowledge.tc.columbia.edu/home.php/bybib/10901449.

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Thesis (Ed.D.)--Teachers College, Columbia University, 1989.<br>Typescript; issued also on microfilm. Sponsor: Elizabeth H. Tucker. Dissertation Committee: Elizabeth M. Maloney. Bibliography: leaves 75-78.
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Hoveland, Carole Munson. "Relationships between learning and study strategies and academic achievement in associate degree nursing students." Laramie, Wyo. : University of Wyoming, 2006. http://proquest.umi.com/pqdweb?did=1136092611&sid=3&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Braun, Gretchen Louise. "Prediction of K-5 Student Achievement Using Teaching Candidate Performance on the Teacher Work Sample: A Predictive Validity Study." VCU Scholars Compass, 2005. http://hdl.handle.net/10156/2184.

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Books on the topic "Achievement Prediction"

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Tremblay, Pierre. Patterns in criminal achievement: Wilson and Abrahamse revisited. Université de Montréal, 1999.

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Menard, Darcy M. Gender orientation in measuring achievement orientation: The prediction of persistence. Laurentian University, Department of Psychology, 1996.

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Randhawa, Bikkar S. Structural links of achievement and contextual measures. Research Centre, Saskatchewan School Trustees Association, 1990.

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Kaestner, Robert. Are brothers really better?: Sibling sex composition and educational achievement revisited. National Bureau of Economic Research, 1996.

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Noble, Julie. Predicting college grades from ACT assessment scores and high school course work and grade information. American College Testing Program, 1991.

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Sue, Stanley. Predictors of academic achievement among Asian American and white students. College Entrance Examination Board, 1988.

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Dovoedo, Amen Tayo. Les déterminants de la réussite en deuxième année de sciences économiques: Une étude de cas. Université Cheikh Anta Diop de Dakar, Faculté des sciences économiques et de gestion, Centre de recherches économiques appliquées, 1997.

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United States. National Weather Service, ed. Climate Prediction Center FY 1997 (October 1, 1996 - September 30, 1997)... Achievement... U.S. Department Of Commerce. s.n., 1998.

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(Project), Education Partners. Improving student achievement: Linking state policy to effective practice : a report. National Conference of State Legislatures, 2001.

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Krane, David, and Julia Resnick. Defining success: American women, achievement, and the Girl Scouts. Girl Scouts of the USA, 1999.

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Book chapters on the topic "Achievement Prediction"

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Zanellati, Andrea, Stefano Pio Zingaro, and Maurizio Gabbrielli. "Student Low Achievement Prediction." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11644-5_76.

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Xu, Li’ang, Zuwang He, Shunzhang Chen, Yifan Zhan, and Shaojie Qu. "Students’ Achievement Prediction via MOOC Data." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3738-6_7.

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Zhang, Shaofeng, Meng Liu, and Jingtao Zhang. "An Academic Achievement Prediction Model Enhanced by Stacking Network." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3341-9_20.

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Lowman, Joseph, and M. David Galinsky. "MMPI Profiles in Adolescence as Indicators of Achievement and Adjustment in Young Adulthood." In Life-Span Research on the Prediction of Psychopathology. Routledge, 2021. http://dx.doi.org/10.4324/9781003165187-9.

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Zhu, Jianlin, Yilin Kang, Rongbo Zhu, et al. "College Academic Achievement Early Warning Prediction Based on Decision Tree Model." In Big Data. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1899-7_25.

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Peng, Haowei, Xiaomei Yu, Xiaotong Jiao, Qiang Yin, and Lixiang Zhao. "Online Learning Behavior Analysis and Achievement Prediction with Explainable Machine Learning." In Artificial Intelligence Security and Privacy. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9788-6_3.

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Pan, Deng, Shuanghong Wang, Congyuan Jin, Han Yu, Chunbo Hu, and ChunZhi Wang. "Research on Student Achievement Prediction Based on BP Neural Network Method." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67133-4_27.

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Li, Xiao, Ting Wang, and Huaimin Wang. "Exploring N-gram Features in Clickstream Data for MOOC Learning Achievement Prediction." In Database Systems for Advanced Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55705-2_26.

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Zhao, Guanhua, Wenwen Yan, and Yue Li. "LS-SVM Financial Achievement Prediction Based on Targets Optimization of Neighborhood Rough Sets." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21402-8_28.

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Luo, Yangyang, and Yiran Cui. "Improving Students’ Achievement Prediction in Blended Learning Environments with Integrated Machine Learning Methods." In Machine Learning in Educational Sciences. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9379-6_9.

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Conference papers on the topic "Achievement Prediction"

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Chen, Yan. "Achievement Prediction System Based on Stacking Integration Algorithm." In 2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI). IEEE, 2024. https://doi.org/10.1109/icdacai65086.2024.00021.

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Xu, Fangfang, and Cizhen Yu. "Academic Achievement Prediction Based on Attention Neural Network Algorithm." In 2024 IEEE 2nd International Conference on Sensors, Electronics and Computer Engineering (ICSECE). IEEE, 2024. http://dx.doi.org/10.1109/icsece61636.2024.10729422.

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Li, Li. "Annual Academic Achievement Level Prediction Based on Machine Learning." In 2024 International Conference on Power, Electrical Engineering, Electronics and Control (PEEEC). IEEE, 2024. https://doi.org/10.1109/peeec63877.2024.00226.

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Yi, Zixuan. "Research on Neural Network-Based Achievement Prediction of Middle School Students." In International Conference on Engineering Management, Information Technology and Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012886800004508.

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Dong, Jing, Zhenting Wan, Jiang Wu, Wenyu Dong, Tianxu Ma, and Yang Zhan. "Student Achievement Prediction Based on NN-Stacking Model with Hyperparameter Optimization." In 2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII). IEEE, 2024. http://dx.doi.org/10.1109/icmiii62623.2024.00024.

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Gong, Jieqiong. "Analysis and Prediction Model Construction of Educational Achievement Based on Data Mining." In 2025 International Conference on Digital Analysis and Processing, Intelligent Computation (DAPIC). IEEE, 2025. https://doi.org/10.1109/dapic66097.2025.00084.

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Lin, Zhutian. "Prediction method for foreign language teaching achievement utilizing an enhanced BP neural network." In Second International Conference on Big Data, Computational Intelligence and Applications (BDCIA 2024), edited by Sos S. Agaian. SPIE, 2025. https://doi.org/10.1117/12.3058899.

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hu, Xiaoyan. "Prediction of sports achievements based on time series analysis." In International Conference on Cloud Computing, Performance Computing, and Deep Learning, edited by Wanyang Dai and Xiangjie Kong. SPIE, 2024. http://dx.doi.org/10.1117/12.3050728.

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Parjan, Yeni Anistyasari, Ekohariadi, and Shintami Chusnul Hidayati. "Predicting Academic Achievement Through Engagement Analysis with Sparse Logistic Regression." In 2024 International Seminar on Intelligent Technology and Its Applications (ISITIA). IEEE, 2024. http://dx.doi.org/10.1109/isitia63062.2024.10667991.

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Anistyasari, Yeni, Shintami C. Hidayati, and Ekohariadi. "A Tree-Based Model for Predicting Student Achievement in Online Learning Environments." In 2024 Seventh International Conference on Vocational Education and Electrical Engineering (ICVEE). IEEE, 2024. https://doi.org/10.1109/icvee63912.2024.10823659.

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Reports on the topic "Achievement Prediction"

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Ozen, Zafer, and Nielsen Pereira. Predicting Indiana Public Middle School Science Achievement by School Demographics. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317460.

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Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, 2002. http://dx.doi.org/10.32747/2002.7585193.bard.

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This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are ob
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Wideman, Jr., Robert F., Nicholas B. Anthony, Avigdor Cahaner, Alan Shlosberg, Michel Bellaiche, and William B. Roush. Integrated Approach to Evaluating Inherited Predictors of Resistance to Pulmonary Hypertension Syndrome (Ascites) in Fast Growing Broiler Chickens. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7575287.bard.

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Background PHS (pulmonary hypertension syndrome, ascites syndrome) is a serious cause of loss in the broiler industry, and is a prime example of an undesirable side effect of successful genetic development that may be deleteriously manifested by factors in the environment of growing broilers. Basically, continuous and pinpointed selection for rapid growth in broilers has led to higher oxygen demand and consequently to more frequent manifestation of an inherent potential cardiopulmonary incapability to sufficiently oxygenate the arterial blood. The multifaceted causes and modifiers of PHS make
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Soloviev, V. N., and Y. V. Romanenko. Economic analog of Heisenberg uncertainly principle and financial crisis. ESC "IASA" NTUU "Igor Sikorsky Kyiv Polytechnic Institute", 2017. http://dx.doi.org/10.31812/0564/2463.

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The Heisenberg uncertainty principle is one of the cornerstones of quantum mechanics. The modern version of the uncertainty principle, deals not with the precision of a measurement and the disturbance it introduces, but with the intrinsic uncertainty any quantum state must possess, regardless of what measurement is performed. Recently, the study of uncertainty relations in general has been a topic of growing interest, specifically in the setting of quantum information and quantum cryptography, where it is fundamental to the security of certain protocols. The aim of this study is to analyze the
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Weller, Joel I., Ignacy Misztal, and Micha Ron. Optimization of methodology for genomic selection of moderate and large dairy cattle populations. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7594404.bard.

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The main objectives of this research was to detect the specific polymorphisms responsible for observed quantitative trait loci and develop optimal strategies for genomic evaluations and selection for moderate (Israel) and large (US) dairy cattle populations. A joint evaluation using all phenotypic, pedigree, and genomic data is the optimal strategy. The specific objectives were: 1) to apply strategies for determination of the causative polymorphisms based on the “a posteriori granddaughter design” (APGD), 2) to develop methods to derive unbiased estimates of gene effects derived from SNP chips
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Chefetz, Benny, Baoshan Xing, and Yona Chen. Interactions of engineered nanoparticles with dissolved organic matter (DOM) and organic contaminants in water. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7699863.bard.

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Background: Engineered carbon nanotubes (CNTs) are expected to be increasingly released into the environment with the rapid increase in their production and use. The discharged CNTs may interact with coexisting contaminants and subsequently change environmental behaviors and ecological effects of both the CNTs themselves and the contaminants. Dissolved organic matter (DOM) plays a critical role in the transport of CNTs in the aquatic environment, affecting both CNT's surface properties through adsorption, and its colloidal stability in solution. Therefore, CNT-bound DOM complexes may interact
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Elizur, Abigail, Amir Sagi, Gideon Hulata, Clive Jones, and Wayne Knibb. Improving Crustacean Aquaculture Production Efficiencies through Development of Monosex Populations Using Endocrine and Molecular Manipulations. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7613890.bard.

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Background Most of Australian prawn aquaculture production is based on P. monodon. However, the Australian industry is under intense competition from lower priced overseas imports. The availability of all-female monosex populations, by virtue of their large size and associated premium prize, will offer competitive advantage to the industry which desperately needs to counteract competitors within this market. As for the redclaw production in Israel, although it is at its infancy, the growers realized that the production of males is extremely advantageous and that such management strategy will c
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Tanny, Josef, Gabriel Katul, Shabtai Cohen, and Meir Teitel. Application of Turbulent Transport Techniques for Quantifying Whole Canopy Evapotranspiration in Large Agricultural Structures: Measurement and Theory. United States Department of Agriculture, 2011. http://dx.doi.org/10.32747/2011.7592121.bard.

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Original objectives and revisions The original objectives of this research, as stated in the approved proposal were: 1. To establish guidelines for the use of turbulent transport techniques as accurate and reliable tool for continuous measurements of whole canopy ET and other scalar fluxes (e.g. heat and CO2) in large agricultural structures. 2. To conduct a detailed experimental study of flow patterns and turbulence characteristics in agricultural structures. 3. To derive theoretical models of air flow and scalar fluxes in agricultural structures that can guide the interpretation of TT measur
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Landau, Sergei Yan, John W. Walker, Avi Perevolotsky, Eugene D. Ungar, Butch Taylor, and Daniel Waldron. Goats for maximal efficacy of brush control. United States Department of Agriculture, 2008. http://dx.doi.org/10.32747/2008.7587731.bard.

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Background. Brush encroachment constitutes a serious problem in both Texas and Israel. We addressed the issue of efficacy of livestock herbivory - in the form of goat browsing - to change the ecological balance to the detriment of the shrub vegetation. Shrub consumption by goats is kept low by plant chemical defenses such as tannins and terpenes. Scientists at TAES and ARO have developed an innovative, cost-effective methodology using fecal Near Infrared Spectrometry to elucidate the dietary percentage of targeted, browse species (terpene-richredberry and blueberry juniper in the US, and tanni
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Granot, David, Richard Amasino, and Avner Silber. Mutual effects of hexose phosphorylation enzymes and phosphorous on plant development. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7587223.bard.

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Research objectives 1) Analyze the combined effects of hexose phosphorylation and P level in tomato and Arabidopsis plants 2) Analyze the combined effects of hexose phosphorylation and P level in pho1 and pho2 Arabidopsis mutants 3) Clone and analyze the PHO2 gene 4) Select Arabidopsis mutants resistant to high and low P 5) Analyze the Arabidopsis mutants and clone the corresponding genes 6) Survey wild tomato species for growth characteristics at various P levels Background to the topic Hexose phosphorylating enzymes, the first enzymes of sugar metabolism, regulate key processes in plants suc
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