Relevant bibliographies by topics / Graduation prediction

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

Academic literature on the topic 'Graduation prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Graduation 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 (November 25, 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 material to predict students' graduation rate consisting of: NPM, Student Name, Semester 1 achievement index, Semester 2 achievement index, Semester 3 achievement index, Semester 4 achievement index, SPMB, origin SMA, Gender , and Study Period. Fuzzyfication process aims to change the value of the first semester achievement index until the fourth semester achievement index into three sets of fuzzy values are satisfactory, very satisfying, and cum laude. Make predictions to improve the quality of students and implement KNN method into prediction, where there are some attributes that have preprocess data so that obtained a value, and the value is compared with training data, so as to produce predictions of graduating students will be on time and graduating students will be late. This study produces a prediction of student pass rate and accuracy.
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Marzuqi, Ahmad, Kusuma Ayu Laksitowening, and Ibnu Asror. "Temporal Prediction on Students’ Graduation using Naïve Bayes and K-Nearest Neighbor Algorithm." JURNAL MEDIA INFORMATIKA BUDIDARMA 5, no. 2 (April 25, 2021): 682. http://dx.doi.org/10.30865/mib.v5i2.2919.

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Accreditation is a form of assessment of the feasibility and quality of higher education. One of the accreditation assessment factors is the percentage of graduation on time. A low percentage of on-time graduations can affect the assessment of accreditation of study programs. Predicting student graduation can be a solution to this problem. The prediction results can show that students are at risk of not graduating on time. Temporal prediction allows students and study programs to do the necessary treatment early. Prediction of graduation can use the learning analytics method, using a combination of the naïve bayes and the k-nearest neighbor algorithm. The Naïve Bayes algorithm looks for the courses that most influence graduation. The k-nearest neighbor algorithm as a classification method with the attribute limit used is 40% of the total attributes so that the algorithm becomes more effective and efficient. The dataset used is four batches of Telkom University Informatics Engineering student data involving data index of course scores 1, level 2, level 3, and level 4 data. The results obtained from this study are 5 attributes that most influence student graduation. As well as the results of the presentation of the combination naïve bayes and k-nearest neighbor algorithm with the largest percentage yield at level 1 75.40%, level 2 82.08%, level 3 81.91%, and level 4 90.42%.
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Nandel Syofneri, Sarjon Defit, and Sumijan. "Implementasi Metode Backpropagation untuk Memprediksi Tingkat Kelulusan Uji Kopetensi Siswa." Jurnal Informasi & Teknologi 1, no. 4 (September 26, 2019): 12–17. http://dx.doi.org/10.37034/jidt.v1i4.13.

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Vocational High School (SMK) 2 Pekanbaru is a Vocational School in Industrial Technology. At present there are 2400 students with 14 majors. In students the level of will in students is still low. Resulting in a low graduation rate for students. This happened because of the difficulty in predicting the level of competency examination passing at SMK Negeri 2 Pekanbaru. The purpose of this study is to assist in predicting the passing level of competency exams so as to produce predictions of student graduation. The method used is the Backpropagation method. With this method data processing can be done using input values and targets that you want to produce. So that it can predict the graduation of students in the expertise competency test. Furthermore, the data to be managed is a recapitulation of the average vocational values majoring in computer network engineering from semester 1 to semester 5 with aspects of knowledge on the target students of 2017 Academic Year and 2018 Academic Year obtained from the sum of all subjects in each semester. The results of calculations using the Backpropagation method with the Matlab application will be predictive in producing grades for students' graduation rates in the future. So that the accuracy value will be obtained in the prediction. With the results of testing the accuracy of prediction student competency tests with patterns 5-4-1 reaching 85%, with patterns 5-6-1 reaching 95%, patterns 5-8-1 reaching 70%, patterns 5-10-1 reaching 85% % and with 5-12-1 patterns it reaches 85%. Of the five patterns, the best accuracy rate of 5-6-1 is 95%. The prediction results using the Bacpropagation method can become knowledge in the next year. So that the system parameters used in testing can be recognized properly.
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Suwardika, Gede Suwardika, and I. Ketut Putu Suniantara. "ANALISIS RANDOM FOREST PADA KLASIFIKASI CART KETIDAKTEPATAN WAKTU KELULUSAN MAHASISWA UNIVERSITAS TERBUKA." BAREKENG: Jurnal Ilmu Matematika dan Terapan 13, no. 3 (October 1, 2019): 177–84. http://dx.doi.org/10.30598/barekengvol13iss3pp177-184ar910.

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Classification and Regression Tree (CART) is one of the classification methods that are popularly used in various fields. The method is considered capable of dealing with various data conditions. However, the CART method has weaknesses in the classification tree prediction, which is less stable in changes in learning data which will cause major changes in the results of the classification tree prediction. Improving the predictions of the CART classification tree, an ensemble random forest method was developed that combines many classification trees to improve stability and determine classification predictions. This study aims to improve CART predictive stability and accuracy with Random Forest. The case used in this study is the classification of inaccuracies in Open University student graduation. The results of the analysis show that random forest is able to increase the accuracy of the classification of the inaccuracy of student graduation that reaches convergence with the prediction of classification reaching 93.23%.
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Suwardika, Gede, I. Ketut Putu Suniantara, and Ni Putu Nanik Hendayanti. "Ketidaktepatan Waktu Kelulusan Mahasiswa Universitas Terbuka dengan Metode Boosting Cart." Jurnal VARIAN 2, no. 2 (April 30, 2019): 37–46. http://dx.doi.org/10.30812/varian.v2i2.361.

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The classification tree method or better known as Classification and Regression Tree (CART) has capabilities in various data conditions, but CART is less stable in changing learning data which will cause major changes in the results of the classification tree prediction. Predictive accuracy of an unstable classifier can be corrected by a combination method of many single classifiers where the prediction results of each classifier are combined into the final prediction through the majority voting process for classification or average voting for regression cases. Boosting ensemble method is one method that combines many classification trees to improve stability and determine classification predictions. This research purpose to improve the stability and predictive accuracy of CART with boosting. The case used in this study is the classification of inaccuracies in the Open University student graduation. The results of the analysis show that boosting is able to improve the accuracy of the classification of the inaccuracy of student graduation which reaches a classification prediction of 75.94% which previously reached 65.41% in the classification tree.
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Santoso, Heri Bambang. "Fuzzy Decision Tree to Predict Student Success in Their Studies." International Journal of Quantitative Research and Modeling 1, no. 3 (September 3, 2020): 135–44. http://dx.doi.org/10.46336/ijqrm.v1i3.59.

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The number of students graduating on time is one of the important aspects in the assessment of accreditation of a university. But the problem is still a lot of students who exceed the target time of graduation. Therefore, the prediction of graduation on time can serve as an early warning for the university management to prepare strategies related to the prevention of cases of drop out. The purpose of this research is to build a model using fuzzy decision tree to form the classification rules are used to predict the success of a student's study using fuzzy inference system. Results of this study was generated model of the number of classification rules are 28 rules when the value θr is 98% and θn is 3%, with the level of accuracy is 95.85%. Accuracy of Fuzzy ID3 algorithm is higher than ID3 algorithms in predicting the timely graduation of students.
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Satria, Fiqih, Zamhariri Zamhariri, and M. Apun Syaripudin. "Prediksi Ketepatan Waktu Lulus Mahasiswa Menggunakan Algoritma C4.5 Pada Fakultas Dakwah Dan Ilmu Komunikasi UIN Raden Intan Lampung." Jurnal Ilmiah Matrik 22, no. 1 (March 30, 2020): 28–35. http://dx.doi.org/10.33557/jurnalmatrik.v22i1.836.

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Student graduation data is very important for universities because it is used in the accreditation process. Data continues to grow and is ignored because it is rarely used. Data of graduating students can provide useful information if processed optimally. This study processes data using data mining to obtain information in the form of a prediction of student graduation punctuality. The method used is the C4.5 algorithm. The criteria used are gender, regional origin, type of school origin, ranking and entry point. In its application, the C4.5 algorithm can be used in predicting student graduation times with a precision value of 70.70%, 60.4% recall, and 58.2% accuracy. In measuring the performance of the algorithm in pattern recognition or information retrieval it is recommended to use a minimum of two parameters namely precission and recall to detect bias, therefore in this study the F-Measure calculation is used. From the calculation of the F-Measure obtained a value of 71% which means that the C4.5 algorithm is considered good in classifying and predicting students who graduate on time
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Kurniawan, Donny, Anthony Anggrawan, and Hairani Hairani. "Graduation Prediction System On Students Using C4.5 Algorithm." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 19, no. 2 (May 30, 2020): 358–65. http://dx.doi.org/10.30812/matrik.v19i2.685.

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Bumigora University College there are several things that are not balanced between the entry and exit of students who have completed their studies. Students who enter in large numbers, but students who graduate on time below the specified standards. As result, there was a huge accumulation of students in each graduation period. One solution to overcome the problem above needs a data mining based system in monitoring or utilizing student development in predicting graduation using the C4.5 algorithm. The stages of this research began with problem analysis, data collection, data requirement analysis, data design, coding, and testing. The results of this study are the implementation of the C4.5 algorithm for predicting student graduation on time or not. The data used is the data of students who have graduated from 2010 to 2012. The level of acceptance generated using the confusion matrix is ​​93,103% accuracy using 163 training data and 29 testing data or 85% training data and 15% testing data. The results of research and testing that has been done, C4.5 algorithm is very suitable to be used in student graduation prediction.
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Munawir, Munawir, and Taufiq Iqbal. "Prediksi Kelulusan Mahasiswa menggunakan Algoritma Naive Bayes (Studi Kasus 5 PTS di Banda Aceh)." Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) 3, no. 2 (September 30, 2019): 59. http://dx.doi.org/10.35870/jtik.v3i2.77.

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The e-questionnaire application that researchers built using CodeIgniter and React-Js This study aims to data mining by using rapidminer tools to collect student data from the Feeder application page from the class of 2010-2014 which is assumed that the student class has been declared graduated in 2018. The data was collected from 5 (five) Private Universities in the City Banda Aceh. then by observing the graduation level using data mining can bring a considerable contribution to educational institutions, in an effort to improve curriculum competency in Higher Education, it is expected that the results of data mining can make reference to curriculum standards as a form of graduate competency improvement. The research method uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) which is used as a standard data mining process as well as a research method with stages starting from Business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results showed that the data mining algorithm for graduation prediction based on the selected pass accuracy attribute revealed that the prediction level was uniform with the algorithm used, Naïve Bayes, prediction accuracy was 84%. The data attributes that were found to have significantly influenced the classification process were the GPA and Study Length. The results obtained that students who graduated by 60% are students who are educated in ASM Nusantara and AMIK Indonesia, while in Banda Aceh STIES and Serambi University Mecca the prediction of graduation is 52%. Another thing is different from STIA Iskandar Thani where the prediction of graduating is only 48% and not passing on time is 52%. The results of this prediction can reveal and become a recommendation for prospective students or academics to increase the quantity of graduates and increase student confidence in tertiary institutions.Keywords:Prediction, Student Graduation, Naive Bayes Algorithm.
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Tatar, Ahmet Emin, and Dilek Düştegör. "Prediction of Academic Performance at Undergraduate Graduation: Course Grades or Grade Point Average?" Applied Sciences 10, no. 14 (July 19, 2020): 4967. http://dx.doi.org/10.3390/app10144967.

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Predicting the academic standing of a student at the graduation time can be very useful, for example, in helping institutions select among candidates, or in helping potentially weak students in overcoming educational challenges. Most studies use individual course grades to represent college performance, with a recent trend towards using grade point average (GPA) per semester. It is unknown however which of these representations can yield the best predictive power, due to the lack of a comparative study. To answer this question, a case study is conducted that generates two sets of classification models, using respectively individual course grades and GPAs. Comprehensive sets of experiments are conducted, spanning different student data, using several well-known machine learning algorithms, and trying various prediction window sizes. Results show that using course grades yields better accuracy if the prediction is done before the third term, whereas using GPAs achieves better accuracy otherwise. Most importantly, variance analysis on the experiment results reveals interesting insights easily generalizable: individual course grades with short prediction window induces noise, and using GPAs with long prediction window causes over-simplification. The demonstrated analytical approach can be applied to any dataset to determine when to use which college performance representation for enhanced prediction.
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Dissertations / Theses on the topic "Graduation prediction"

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Tranchita, Anthony Phillip. "Predictors of Graduation and Rearrest in a Contemporary Juvenile Drug Court Program." DigitalCommons@USU, 2004. https://digitalcommons.usu.edu/etd/6210.

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Research on the efficacy of drug courts for substance-abusing criminal adult offenders has generally found reduced recidivism rates, and both actual and potential cost savings to the public. However , outcome research on juvenile drug courts has been limited. Furthermore , little research has examined variables that may be predictive of outcome in this population. This study reports graduation and rearrest rates for a sample of juvenile drug court participants in Salt Lake City, Utah. Also, this research assessed whether demographics, prior arrest history, attendance at drug education classes, serving detention time, or a preprogram measure of degree of substance abuse (SAS SI-A) help predict several important outcomes (i.e., graduation from the drug court program and number of rearrests per year after leaving drug court). The graduation rate in this sample was fairly high (84.2%). However, the rearrest rate was also relatively high, with slightly over 50% with an arrest for any offense, and 38. 7% with a drug-elated arrest during follow-up (average follow-up time 4.3 years). Serving detention and not attending prevention class predicted lower rates of program graduation, while younger age, male gender, not graduating drug court, non-Caucasian status, and past adjudication predicted higher rates of recidivism (rearrest).
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Crumrine, David A. "Effective graduation proficiency assessment parents' perception of high-stakes vs. multiple assessment as a predictor of future success /." Open access to IUP's electronic theses and dissertations, 2008. http://hdl.handle.net/2069/140.

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Misigaro, Edwin Nitunga Morreau Lanny E. "Factors influencing Tanzanian students to leave school prior to grade seven graduation." Normal, Ill. Illinois State University, 1993. http://wwwlib.umi.com/cr/ilstu/fullcit?p9323738.

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Thesis (Ed. D.)--Illinois State University, 1993.
Title from title page screen, viewed February 15, 2006. Dissertation Committee: Lanny Morreau (chair), Ming-Gon John Lian, Paul Baker, Keith Stearns. Includes bibliographical references (leaves 134-152) and abstract. Also available in print.
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Marshall, David T. "Testing the Ability of Two Series of Models to Predict High School Graduation Status." VCU Scholars Compass, 2017. http://scholarscompass.vcu.edu/etd/4756.

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The purpose of this study was to create and test two series of predictive models aimed at projecting high school graduation status. Secondary data were obtained in partnership with an urban school district. All of the predictor variables included in the models tested in this study were academic and nonacademic variables that were found to be significant predictors of high school graduation in previous empirical work. In the first series of models tested, individual academic and nonacademic variables were tested together along with school-level variables. Eighth and ninth grade variables were tested separately to avoid multicollinearity issues. The second series of models tested included similar individual-level academic and nonacademic variables, along with community-level predictors to analyze their ability to predict high school graduation status. Logistic regression and multilevel logistic regression analyses were conducted to analyze the data. The model including community-level predictors yielded a pseudo R-squared value of .40, approximating that 40% of the variance was explained by the predictors in the model. Most of the individual predictors included in the models yielded findings similar to those found in previous literature on high school graduation status projection; however, this was not true for all of the predictor variables included. These differences highlight the tension that can exist between generalizability and local specificity. Significant findings from studies utilizing large nationally-representative longitudinal datasets and other large data sources do not always generalize to settings with samples that differ demographically. This study represents a first step in a line of research aimed at developing a better understanding of high school graduation status, particularly in challenging school contexts.
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Campos, Lisa D. "An investigation of cognitive and non-cognitive variables that affect student-athlete graduation and retention." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Kotzè, M., and L. Griessel. "The prediction of the academic performance of MBA students by means of specific aptitudes And competencies." Journal for New Generation Sciences, Vol 6, Issue 2: Central University of Technology, Free State, Bloemfontein, 2008. http://hdl.handle.net/11462/505.

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The Council on Higher Education (CHE) (2004) states that graduation rates across all provider types of MBA qualifications in South Africa are not very high. Various studies have reported that, in order to address poor throughput rates, one of the important aspects that needs to be addressed, is the criteria used to select students. The purpose of this study was to identify valid predictors and measures of the academic performance of MBA students. Multiple regression analysis was used to determine the significance of different competencies and aptitudes in predicting academic success. The sample consisted of 135 MBA students from a South African School of Management. The results show that certain aptitudes and competencies, namely numerical aptitude, personal motivation, verbal aptitude, and resilience, contributed statistically significant to academic success.
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Mills, Bradley Scott. "Predicting Graduation| An Examination of the Variables that Predict Graduation for Students with Emotional Disabilities." Thesis, North Carolina State University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10708320.

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Students with Emotional Disabilities (ED) graduate from high school at rates far below their peers. The completed study utilized archival data from former students’ special education folders and from a nondisabled comparison group to examine variables that had previously been studied in relation to graduation (e.g., repeating ninth grade, extracurricular participation) along with variables identified from the folders of the former students. The descriptive quantitative study identified variables that predicted graduation for individuals with ED and the differences between the variables for individuals with ED and the nondisabled group. The results indicated that GPA and extracurricular participation positively predicted graduation while the number of years spent in 9th grade negatively predicts graduation for both groups. Specifically for students with ED, student attendance at special education meetings was statistically significant for predicting graduation.

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Sims, Michael S. "Predicting Four-Year Graduation| A Sequential Modeling Approach." Thesis, California State University, Long Beach, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10841337.

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As a result of the California State Universities having four-year graduation rates among freshman students below 20% over the last few years, the Graduation Initiative 2025 has been deployed. This initiative aims to increase the graduation rates to 40%, while eliminating opportunity and achievement gaps. A signicant impact of this is looking at the success of rst-time-freshmen (FTF) and the prediction of whether or not they will graduate in a timely fashion. To this end, a natural classication problem is identied: amongst the FTF cohort who will graduate in four years or less(class instance = 1), or more than four years (class instance = 0) including students who did not graduate. In this paper, using Area Under the Curve (AUC) as our models performance metric, we construct classication models that quickly identify students at risk of not graduating in a timely fashion. Furthermore, we will construct models cumulatively—term by term—where each successive model includes student data from matriculation to the end of a given term. Using this approach allows a University to nd an optimal time to deploy possible intervention programs. It should be noted that optimal in this paper means, having a model with high AUC as early into the students academic career as possible. This way, an at-risk student is identied early, and the value of the University intervening is optimized. In this paper we will compare a variety of classication algorithms such as Logistic Regression, Random Forest, and XGBoost to see which model yields the highest AUC. Also we provide insight on interpretation specically identifying the eect each covariate has on the response. This approach will be unique because not only will it be a means for identifying the problem, but also serve as part of the solution.

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Sandusky, Sue Ann. "Predicting Student Veteran Persistence." Bowling Green State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1585070424571773.

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McNeill, Donald B. "An analysis of factors predicting graduation at United States Marine Corps Officer Candidates School." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FMcNeill.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, September 2002.
Thesis advisor(s): Samuel E. Buttrey, Lyn R. Whitaker. Includes bibliographical references (p. 69-72). Also available online.
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Books on the topic "Graduation prediction"

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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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Fine, Kerry Kinney. Retention of Minnesota college students: Skimming the surface of graduation. St. Paul, MN: Research Dept., Minnesota House of Representatives, 1992.

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An Analysis of Factors Predicting Graduation at United States Marine Corps Officer Candidates School. Storming Media, 2002.

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An Analysis of Factors Predicting Graduation of Students at Defense Language Institute Foreign Language Center. Storming Media, 2004.

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Trussell, Jessica W., and M. Christina Rivera. Word Identification and Adolescent Deaf and Hard-of-Hearing Readers. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190880545.003.0011.

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Many deaf and hard-of-hearing (DHH) high school students graduate with reading abilities that leave them poorly prepared for postsecondary settings. In college, reading ability is an important predictor of graduation rates and level of degree attained, and the postsecondary degree a DHH student completes will affect his or her future earnings, upward mobility, and job satisfaction. Considering how important reading is to a DHH student’s future, this chapter will review the evidence base surrounding the foundational building block of reading, decoding. Researchers suggest that decoding instruction for adolescents should occur not only during language arts classes but also in the content areas (i.e., math, science, and social studies). This chapter reviews successful decoding strategies and suggests decoding strategies that teachers can use to support adolescents in various content-area disciplines. The authors discuss how teachers and parents can make strategic decisions when implementing decoding interventions that have no available evidence base.
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Book chapters on the topic "Graduation prediction"

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Tampakas, Vassilis, Ioannis E. Livieris, Emmanuel Pintelas, Nikos Karacapilidis, and Panagiotis Pintelas. "Prediction of Students’ Graduation Time Using a Two-Level Classification Algorithm." In Communications in Computer and Information Science, 553–65. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20954-4_42.

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Plajner, Martin, and Jiří Vomlel. "Bayesian Networks for the Test Score Prediction: A Case Study on a Math Graduation Exam." In Lecture Notes in Computer Science, 255–67. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86772-0_19.

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Polatajko, Mark M., and Catherine H. Monaghan. "Performance Funding of United States' Public Higher Education." In Handbook of Research on Administration, Policy, and Leadership in Higher Education, 496–517. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0672-0.ch019.

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Policy makers around the globe are responsible for decision regarding the funding of higher education and the benchmarks of success. This chapter is geared toward higher education administration and leadership, especially those who shape policy in this arena. This quantitative study examined the effectiveness in the United States of allocating state resources to state public institutions of higher education by investigating the rate of change in the current benchmarks of success, which are graduation and retention rates. The findings revealed that the method of funding was not a statistically significant predictor of either the initial status or the rate of change of graduation rate or retention rate over the eight-year period, although institution type and enrollment were. The study recommends further research of performance funding outcomes, state funding levels, and other environmental factors as a means of helping administrators and policy makers in their quest to facilitate economic progress through an educated citizenry.
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Shah, Tanveer H. "Big Data Analytics in Higher Education." In Maximizing Social Science Research Through Publicly Accessible Data Sets, 38–61. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3616-1.ch003.

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This chapter reviews the literature on the use of business analytics in higher education. Universities have large datasets available to predict future direction and generate actionable information. An important type of analytics used to improve management processes and to make informed decisions is big data business analytics. State university executive leaders may improve the effectiveness of their decisions by integrating business analytics in the decision-making models. However, there is a need to examine the use of big data business analytics in the decision-making process at the executive leadership level of the selected state universities. Especially in the context of how descriptive, predictive, prescriptive, decisive and basic analytics, and data collection influence the decision-making process at the executive leadership level of the state universities in terms of student retention and graduation rates.
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Chemosit, Caroline C., John K. Rugutt, Viviline Ngeno, and Dorothy Soi. "Active Learning Strategies in Enhancing Learning among College Students." In Handbook of Research on Educational Technology Integration and Active Learning, 202–14. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8363-1.ch010.

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This chapter explores the relationship between active learning strategies and skills and attributes that enhance learning (SAEL) among college students. Developing skills and attributes that enhance learning (SAEL) among college students is critical to student success and persistence in college. Additionally, SAEL help the students develop a sustained learning commitment while in college and after graduation. However, little evidence is there to show how higher education institutions are equipping students with SAEL. This study seeks to investigate if there is a relationship between active learning strategies (ALS) and SAEL. Secondary data from the 2007 National Survey of Student Engagement (NSSE) at a Midwestern state university in the USA were employed to examine the relationship between ALS and SAEL. The results of the analysis showed positive significant correlations between ALS and SAEL components, (p < 0.001). Multiple regression model showed that ALS predictor variables significantly predict SAEL, R2 = .196, R2adj = .188, F (7, 731) = 25.38, p < .001. The regression model accounts for 19.6% of variance in SAEL.
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Conference papers on the topic "Graduation prediction"

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Hartatik, Kusrini Kusrini, and Agung Budi Prasetio. "Prediction of Student Graduation with Naive Bayes Algorithm." In 2020 Fifth International Conference on Informatics and Computing (ICIC). IEEE, 2020. http://dx.doi.org/10.1109/icic50835.2020.9288625.

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Ojha, Tushar, Gregory L. Heileman, Manel Martinez-Ramon, and Ahmad Slim. "Prediction of graduation delay based on student performance." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966290.

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Salim, Alfin Pratama, Kusuma Ayu Laksitowening, and Ibnu Asror. "Time Series Prediction on College Graduation Using KNN Algorithm." In 2020 8th International Conference on Information and Communication Technology (ICoICT). IEEE, 2020. http://dx.doi.org/10.1109/icoict49345.2020.9166238.

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Cahaya, Leonardo, Lely Hiryanto, and Teny Handhayani. "Student graduation time prediction using intelligent K-Medoids Algorithm." In 2017 3rd International Conference on Science in Information Technology (ICSITech). IEEE, 2017. http://dx.doi.org/10.1109/icsitech.2017.8257122.

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Prachuabsupakij, Wanthanee, and Pafan Doungpaisan. "Matching preprocessing methods for improving the prediction of student's graduation." In 2016 2nd IEEE International Conference on Computer and Communications (ICCC). IEEE, 2016. http://dx.doi.org/10.1109/compcomm.2016.7924659.

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Suwitno, Suwitno, and Arief Wibowo. "On-Time Graduation Prediction System Using Data Mining Classification Method." In Proceedings of the 1st Workshop on Multidisciplinary and Its Applications Part 1, WMA-01 2018, 19-20 January 2018, Aceh, Indonesia. EAI, 2019. http://dx.doi.org/10.4108/eai.20-1-2018.2281900.

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Peralta, B., T. Poblete, and L. Caro. "Automatic feature selection for desertion and graduation prediction: A chilean case." In 2016 35th International Conference of the Chilean Computer Science Society (SCCC). IEEE, 2016. http://dx.doi.org/10.1109/sccc.2016.7836055.

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Andreswari, Rachmadita, Muhammad Azani Hasibuan, Dela Youlina Putri, and Qalbinuril Setyani. "Analysis Comparison of Data Mining Algorithm for Prediction Student Graduation Target." In Proceedings of the 2018 International Conference on Industrial Enterprise and System Engineering (IcoIESE 2018). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/icoiese-18.2019.58.

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Wirawan, Chandra, Eva Khudzaeva, Tuhfatul Habibah Hasibuan, Karjono, and Yeni Hilmi Khairani Lubis. "Application of Data mining to Prediction of Timeliness Graduation of Students (A Case Study)." In 2019 7th International Conference on Cyber and IT Service Management (CITSM). IEEE, 2019. http://dx.doi.org/10.1109/citsm47753.2019.8965425.

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Peralta, Billy, Jorge Salazar, Marcos Levano, and Orietta Nicolis. "A causal modelling for desertion and graduation prediction using Bayesian networks: a Chilean case." In 2021 IEEE International Conference on Automation/XXIV Congress of the Chilean Association of Automatic Control (ICA-ACCA). IEEE, 2021. http://dx.doi.org/10.1109/icaacca51523.2021.9465333.

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