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Journal articles on the topic 'Graduation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

Muliani, Sonia Sri, Volvo Sihombing, and Ibnu Rasyid Munthe. "Implementation of Exploratory Data Analysis and Artificial Neural Networks to Predict Student Graduation on-Time." sinkron 8, no. 2 (April 29, 2024): 1188–99. http://dx.doi.org/10.33395/sinkron.v8i2.13658.

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Almost all universities in Indonesia face the problem of a low number of students graduating on time. This will affect higher education accreditation. For this reason, universities must pay attention to the timely graduation of their students. The way that can be taken is to predict students' graduation on time. This research aims to predict students' timely graduations using a combination of exploratory data analysis and artificial neural networks. Exploratory data analysis is used to study the relationship between features that influence students' on-time graduation, while artificial neural networks are used to predict on-time graduation. This research goes through method stages, starting with determining the dataset, exploratory data analysis, data preprocessing, dividing training and test data, and applying artificial neural networks. From the research, it was found that Work features and GPS features greatly influence graduation on time. Students who study while working are less likely to graduate on time compared to students who do not work. Students who have an average GPS above 3.00 for eight consecutive semesters will find it easier to graduate on time. Meanwhile, Age and Gender features have no effect on graduating on time. With a percentage of 50% training data and 50% test data, epoch 100, and learning rate 0.001, the best network model was obtained to predict graduation on time with an accuracy rate of 69.84%. The research results also show that the amount of test data and the learning rate can influence the level of accuracy. Meanwhile, the number of epochs does not affect the level of accuracy.

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2

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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3

Wang, Ph.D., Ying, Chukwuma Ahanonu, Ph.D., and Kalanya Moore, Ph.D. "Understanding the Challenges of Graduation Rate Faced by Two Education Preparatory Programs in the State of Mississippi." World Journal of Educational Research 8, no. 4 (July 10, 2021): p9. http://dx.doi.org/10.22158/wjer.v8n4p9.

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In this study, the authors described the contribution of student’s academic performance indicators as predictors of graduation rate in two Historically Black Colleges and Universities (HBCUs) Education Preparatory Program (EPP) in the State of Mississippi. The authors interviewed two EPP chairs in summer 2019 and used qualitative inquiry to code and look for themes to provide meaning and add additional explanation to the students’ graduation rate. The main findings of the study suggest that teacher candidates’ ACT/SAT scores are predictive of graduation rates. Similarly, socioeconomic status showed a positive relationship with admission to the EPP and graduation rate. Each EPP faces the challenge of graduating a sufficient number of certified teachers to ensure its continuity. The EPP needs to ensure that students are capable of passing the state certification exams and graduating to be successful. Graduation rate is an indicator of EPP performance and its likelihood of continuity and longevity.

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4

Kostaki, Anastasia, Javier M. Moguerza, Alberto Olivares, and Stelios Psarakis. "Support Vector Machines as tools for mortality graduation." Canadian Studies in Population 38, no. 3-4 (July 5, 2012): 37. http://dx.doi.org/10.25336/p6vs46.

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A topic of interest in demographic and biostatistical analysis as well as in actuarial practice,is the graduation of the age-specific mortality pattern. A classical graduation technique is to fit parametric models. Recently, particular emphasis has been given to graduation using nonparametric techniques. Support Vector Machines (SVM) is an innovative methodology that could be utilized for mortality graduation purposes. This paper evaluates SVM techniques as tools for graduating mortality rates. We apply SVM to empirical death rates from a variety of populations and time periods. For comparison, we also apply standard graduation techniques to the same data.

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5

S, Lilis Suryati, Kordiyana K. Rangga, Yaktiworo Indriyani, Wuryaningsih Dwi Sayekti, Yuniar Aviati Syarief, and Tubagus Hasanuddin. "Strategies to Increase Independent Prosperous Graduation Family Hope Program Recipients in Central Lampung Regency." Journal of Social Research 2, no. 7 (June 30, 2023): 2443–55. http://dx.doi.org/10.55324/josr.v2i7.1206.

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Independent prosperous graduation is a benchmark for the performance of social companions and an indication of the success of the Family Hope Program (PKH), so a strategy is needed to increase independent prosperous graduation. This study aims to analyze strategies to increase independent prosperous graduation of PKH recipients. Data collection was carried out from September 2022 to February 2023 with informants as many as 12 PKH social assistants in Central Lampung Regency. The data analysis method used is SWOT analysis. The results showed that the strategies for increasing independent prosperous graduations in Central Lampung Regency include: holding regular meetings of all PKH Central Lampung human resources to unify perceptions about independent prosperous graduates, maximize the abilities and skills possessed by PKH social assistants to become facilitators, educators, motivators and advocates for PKH KPM; optimizing the role of PKH district coordinators in encouraging the success of independent prosperous graduates; maximizing the role of PKH social assistants to identify KPM business potential and empower according to existing potential to increase KPM prosperous graduation; ; utilizing support from various resource systems to develop the potential of KPM PKH both in terms of business and education of KPM PKH children; conduct training to improve the skills of PKH social assistants on KPM graduating techniques; utilizing the Family Improvement Meeting (P2K2) as a forum to change the mindset and behavior of KPM; and coordinate with village and sub-district officials.

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6

Takahashi, Akira, Yuji Kokumai, and Yuichi Takigawa. "Lateral Shift Error due to Graduation Anomalies and Line-Detection Algorithm in Line Scale Measurement." International Journal of Automation Technology 6, no. 1 (January 5, 2012): 84–91. http://dx.doi.org/10.20965/ijat.2012.p0084.

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The measurement error resulting from graduation anomalies and the signal processing algorithm used for determining the positions of graduations on line scales was investigated by simulation and experiment. Optical image-forming simulations were carried out on models of 6-µm-wide graduations with three sizes of defects (0.5, 1.0 and 1.5 µm) at one edge. A digital filter was used in signal processing to obtain the first differential to determine the positions of the graduations. The minimum values of the lateral shift of the determined graduation positions were observed for the three defect sizes when using a 9-µm-wide differential filter. An experiment was also carried out on an ordinary line scale with 6-µm-wide graduations using a high-precision laser-interferometric line scale calibration system by measuring seven positions on the scale in the direction perpendicular to the measurement axis. The root mean square of the standard deviations from the linear fitting lines constructed using the measured positions over a 300-mm-long line scale was 2.8 nmwhen the differential filter width was 9 µm. It was demonstrated that a differential filter was effective in reducing the lateral error due to graduation anomalies.

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7

Boumi, Shahab, and Adan Ernesto Vela. "Improving Graduation Rate Estimates Using Regularly Updating Multi-Level Absorbing Markov Chains." Education Sciences 10, no. 12 (December 13, 2020): 377. http://dx.doi.org/10.3390/educsci10120377.

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American universities use a procedure based on a rolling six-year graduation rate to calculate statistics regarding their students’ final educational outcomes (graduating or not graduating). As an alternative to the six-year graduation rate method, many studies have applied absorbing Markov chains for estimating graduation rates. In both cases, a frequentist approach is used. For the standard six-year graduation rate method, the frequentist approach corresponds to counting the number of students who finished their program within six years and dividing by the number of students who entered that year. In the case of absorbing Markov chains, the frequentist approach is used to compute the underlying transition matrix, which is then used to estimate the graduation rate. In this paper, we apply a sensitivity analysis to compare the performance of the standard six-year graduation rate method with that of absorbing Markov chains. Through the analysis, we highlight significant limitations with regards to the estimation accuracy of both approaches when applied to small sample sizes or cohorts at a university. Additionally, we note that the Absorbing Markov chain method introduces a significant bias, which leads to an underestimation of the true graduation rate. To overcome both these challenges, we propose and evaluate the use of a regularly updating multi-level absorbing Markov chain (RUML-AMC) in which the transition matrix is updated year to year. We empirically demonstrate that the proposed RUML-AMC approach nearly eliminates estimation bias while reducing the estimation variation by more than 40%, especially for populations with small sample sizes.

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8

Martanto, Martanto, Irfan Ali, and Mulyawan Mulyawan. "Prediksi Tingkat Kelulusan Mahasiswa Menggunakan Machine Learning dengan Teknik Deep Learning." Jurnal Informatika: Jurnal Pengembangan IT 4, no. 2-2 (December 19, 2019): 191–94. http://dx.doi.org/10.30591/jpit.v4i2-2.1877.

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The graduation rate of students on time at the Informatics Engineering study program STMIK IKMI Cirebon greatly affects the accreditation assessment. Graduation prediction is difficult to do, but many have done predictions using a variety of methods. Graduation prediction is needed in order to determine preventive policies for students who graduate not on time. The method used in this research is Machine learning with deep learning techniques. The data set used as many as 405 data of students who graduated on time or who were not on time. The research attributes used are the Nim attribute, the GPA value of students who have graduated and the status of graduating or not graduating. The results of this study are the level of accuracy using Machine Learning by 72.84%.

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9

Siddig Mohamed Yagoub, Ibrahim. "بناء المعايير والاوزان لتقيم مشاريع التخرج لطلبة العمارة - (ج)." FES Journal of Engineering Sciences 9, no. 1 (February 22, 2021): 121–32. http://dx.doi.org/10.52981/fjes.v9i1.668.

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Evaluation criteria for graduation projects of architecture students have a major role in helping students to generate innovative graduation project, so the paper aims to build standards and criteria to evaluate graduation projects of architecture students. These criteria These will be used as a tool to assist professors and educators to fairly assess and analyze all types of graduation projects, and to achieve the research goal a methodology divided into three phases will be followed Firstly, to extract and find a draft of the standards by studying the theoretical aspect of architecture, and studying the criteria for arbitration of graduation projects in which members of the jury are interested in the academic community, secondly it deals with the foundations of building standards, and the philosophy of adding criteria to standards, then criteria of standards when evaluating each type of project Different graduations in jobs Third, testing the criteria for evaluating and applying proposed graduation projects, by selecting different samples of graduation projects for the previous year and evaluating them by arbitrators from the professors of Architecture and Planning, and comparing the results from them with previous evaluation results, in order to find out their suitability and suitability to evaluate graduation projects. It has been reached that building standards and criteria assist professors and educators to a fair evaluation of graduation projects, and help the students in the design process of innovative architectural designs that achieve the greatest benefit.

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10

Shin, Alexander Y. "Graduation." Techniques in Hand & Upper Extremity Surgery 25, no. 3 (July 22, 2021): 129. http://dx.doi.org/10.1097/bth.0000000000000364.

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11

Hunt, Jack. "Graduation." Postgraduate Medicine 83, no. 8 (June 1988): 221–25. http://dx.doi.org/10.1080/00325481.1988.11700318.

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12

Mateen, F. "GRADUATION." Medical Humanities 32, no. 2 (December 1, 2006): 106. http://dx.doi.org/10.1136/jmh.2006.000233.

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13

Bergevin, Rita C. "GRADUATION." American Journal of Nursing 99, no. 1 (January 1999): 25. http://dx.doi.org/10.1097/00000446-199901000-00030.

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14

Updike, Phyllis. "Graduation." Image: the Journal of Nursing Scholarship 21, no. 4 (December 1989): 258–59. http://dx.doi.org/10.1111/j.1547-5069.1989.tb00155.x.

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15

Bushman-Carlton, Marilyn. "Graduation." Dialogue: A Journal of Mormon Thought 40, no. 1 (April 1, 2007): 180–81. http://dx.doi.org/10.2307/45227168.

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16

Verrall, R. J. "Graduation by dynamic regression methods." Journal of the Institute of Actuaries 120, no. 1 (1993): 153–70. http://dx.doi.org/10.1017/s002026810003688x.

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AbstractThis paper extends the theory of graduation by parametric formulae to include dynamic estimation methods. This is an application of the Kalman filter and allows the parameters of the curve fitted to vary with age. The amount of variation is determined by the amount of smoothing required, and the method can be regarded as a combination of curve fitting and sequential smoothing, each of which has been used separately for performing graduations. In practice, a dynamic straight line can always be used for the graduation and the method has a sensible logical interpretation.

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17

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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18

Miyamoto, M., and S. Suzuki. "A Source of Systematic Error, Δδα, in Absolute Catalogs Compiled from Meridian Circle Observations." International Astronomical Union Colloquium 127 (1991): 314–17. http://dx.doi.org/10.1017/s0252921100064083.

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AbstractAutomated photoelectric meridian circles are able nowadays to have the full set of graduation errors of the decimation circle determined within a few days. Thus, the modern meridian circles can be monitored, and the annual and secular changes of the graduations can be easily detected to provide the graduation corrections at any date. In order to remove systematic declination errors in the form Δδα from absolute catalogs, these continuous changes should be taken into account.

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19

Umar, Zhahnaz Azizah. "Wisuda Online di Universitas Hasanuddin: Respon, Makna, dan Perayaan." Emik 5, no. 2 (December 20, 2022): 171–89. http://dx.doi.org/10.46918/emik.v5i2.1543.

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The graduation ceremony from year to year has not changed much and remains a solemn ceremonial activity that can bring its own euphoria to graduation participants, parents, and other attendees. However, since the Covid-19 pandemic at the beginning of 2020 various changes have occurred, including in the field of education, and one of them is related to the graduation ceremony. In Indonesia, many universities delay graduation, but not a few continue to do it online. This article will discuss online graduation as a substitute for face-to-face graduation due to the current epidemic of Covid-19, so that let alone crowds, physical distance is limited. This study was conducted at Hasanuddin University as one of the universities in Indonesia that carries out online graduation. The study focused on the implementation of the second online graduation, that is, the first period of 2020/2021, conducted between September and November 2021. Data was collected using observation and interviews. Those who participated in this study were graduates of Hasanuddin University (S1) who had attended online graduations, both period IV in 2019/2020 and period I in 2020/2021. There were ten informants who participated in this study. The are varied on the basis of age, place of origin, study program, faculty, and batch. The study indicates that despite the fact that Hasanuddin University is carrying out online graduation due to the Covid-19 outbreak, the graduates felt disappointed because the coveted graduation after more or less four years of studying finally had to be packaged in an online graduation. The meaning of graduation is very diverse, such as some who casually interpret graduation as a formality and euphoria. There are also those who interpret graduation as the result of the struggle for everything they went through while being students, as a transition status and as a space for appreciation. The implementation of online graduation does not have a significant effect on the completion of graduate thesis. Even though during the Covid-19 outbreak would be held online, the graduates are still preparing to take part. This preparation for graduation is divided into three, namely preparations related to media and internet networks, rehearsals, and the graduate appearance. Despite the Covid-19 outbreak, post-graduation celebrations are still carried out. It is argued in this article that Covid-19 is a pandemic, but an epidemic is an epidemic, graduates still intend to carry out various rituals that are carried out such as at offline graduation with various limitations and adjustments to the conditions of the Covid-19 outbreak.

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20

Garrison, Roger. "Graduation before Graduation: Social Involvement and English." English Journal 79, no. 6 (October 1990): 60. http://dx.doi.org/10.2307/819048.

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Garrison, Roger. "Graduation before Graduation: Social Involvement and English." English Journal 79, no. 6 (October 1, 1990): 60–63. http://dx.doi.org/10.58680/ej19908515.

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22

Fatunnisa, Arina, and Hendra Marcos. "Prediksi Kelulusan Tepat Waktu Siswa SMK Teknik Komputer Menggunakan Algoritma Random Forest." Jurnal Manajemen Informatika (JAMIKA) 14, no. 1 (April 5, 2024): 101–11. http://dx.doi.org/10.34010/jamika.v14i1.12114.

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School performance can be measured through student completion rates, which is a key indicator. Low graduation rates indicate problems in the education and learning system, which require timely intervention to prevent students from not completing their education. Therefore, predicting graduation rates is crucial for schools in order to determine students who are likely to not complete their education, so as to provide early assistance to improve their academic performance. This research is urgent because by understanding and predicting student completion, schools can allocate resources more effectively to support at-risk students, with the ultimate goal of improving graduation rates and overall school performance. This research uses random forest algorithm with graduation dataset. The distribution of training and test data selection uses stratified random sampling method to ensure a balanced representation of each class generated. The Random Forest model was successfully obtained through training and model evaluation using test data, showing an accuracy of 1.0 or equivalent to 100%. The use of the random forest algorithm on student graduation datasets can be an effective approach in supporting timely graduation prediction due to its high accuracy and the model's ability to recognize both graduating and non-passing students.

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23

Hussey, Andrew J., and Omari H. Swinton. "Estimating the ex ante Expected Returns to College." American Economic Review 101, no. 3 (May 1, 2011): 598–602. http://dx.doi.org/10.1257/aer.101.3.598.

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Rather than estimating the returns to obtaining a college degree, this paper treats the college education decision as an uncertain investment involving varying likelihoods of successful graduation. We predict earnings conditional on both graduating and not graduating from both selective and non-selective institutions, and incorporate estimated individual-specific graduation rates in calculating the ex ante expected returns from college attendance for individuals across the ability distribution. Our results suggest that, especially for lower ability students, ex ante returns may differ substantially from typical estimates of the returns to a college degree, and typical estimates of the selectivity premium may be underestimated.

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24

Dorn, Sherman. "High-Stakes Testing and the History of Graduation." education policy analysis archives 11 (January 3, 2003): 1. http://dx.doi.org/10.14507/epaa.v11n1.2003.

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An historical perspective on high-stakes testing suggests that tests required for high school graduation will have mixed results for the putative value of high school diplomas: (1) graduation requirements are likely to have indirect as well as direct effects on the likelihood of graduating; (2) the proliferation of different exit documents may dilute efforts to improve the education of all students; and (3) graduation requirements remain unlikely to disentangle the general cultural confusion in the U.S. about the purpose of secondary education and a high school diploma, especially confusion about whether the educational, exchange, or other value of a diploma is most important.

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Saleh, Jaya, Angelia Adrian, and Junaidy Sanger. "SISTEM KLASIFIKASI KELULUSAN MAHASISWA DENGAN ALGORITMA RANDOM FOREST." Jurnal Ilmiah Realtech 18, no. 1 (April 15, 2022): 10–14. http://dx.doi.org/10.52159/realtech.v18i1.4.

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Some of the causes of students not graduating on time are the Cumulative GPA, which is below standard, the contracted courses do not pass, and the Number of leave taken. If left unchecked, this student's graduation time affects the accreditation value of the study program. For that, we need an application that can classify students who do not graduate on time from an early age so that supervisors and teaching lecturers can pay special attention to extra lessons, provide motivation, and can provide encouragement for students who are classified as not graduating on time so that the student can graduate on time. In this study, a student graduation classification application was made using the Random Forest algorithm. The attributes used to classify student graduation are Grade Point Average, Grade Point Average 1 to 4, Number of courses not passed, age, and gender, with 2 class outputs classified as punctual and late. This application development uses Waterfall and Unified Modeling Language (UML) as modeling tools. Testing of student graduation classification applications using the Random Forest algorithm, which is carried out using 60% training data and 40% test data. The test was performed five times, and the highest accuracy obtained was 90.00% using 50 trees.

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26

Ariani, Yayuk, Masrizal Masrizal, and Rahma Muti’ah. "Prediction of Student Graduation Rates using the Artificial Neural Network Backpropagation Method." sinkron 8, no. 2 (April 25, 2024): 1169–77. http://dx.doi.org/10.33395/sinkron.v8i2.13659.

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This student graduation rate research focuses on analyzing academic performance with the main aim of identifying and distinguishing between students who graduate on time and those who graduate late. The application of data mining techniques in this research uses the neural network method, which is expected to offer deeper insight into the factors that influence students' graduation times. In this study, the neural network method was used to classify graduation data from 150 students. The results of this analysis were very encouraging, with 149 students identified as graduating on time and one student graduating late. The level of accuracy achieved in this classification is 98%, which shows the effectiveness of the neural network method in processing and analyzing academic data. These results confirm that neural networks are a powerful and reliable tool for predictive tasks like this. The successful use of neural networks in this study also proves their potential in broader educational applications, particularly in optimizing educational and intervention strategies. By understanding the characteristics of students who graduate on time versus those who graduate late, educators and administrators can design more effective programs to support student success. This is important not only to improve graduation statistics, but also to improve the overall educational experience for students.

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T, Thoyyibah. "Identifying Factors Affecting the Relationship between Department and Graduation Level of Informatics Engineering Students using Apriori Algorithm: A Case Study at Pamulang University." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 11, no. 1 (March 31, 2023): 159–70. http://dx.doi.org/10.33558/piksel.v11i1.6933.

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To cultivate the next generation of leaders, it is essential for teenagers to receive a high level of education. Typically, this education is acquired through attending lectures that produce a high GPA, which is considered a valuable achievement for students. The level of graduation achieved within the appropriate timeframe can also impact campus accreditation, especially for engineering students, particularly those pursuing informatics engineering. To improve graduation rates, it is necessary to use data mining to identify patterns and trends among graduating students. The a priori algorithm was used in this study to analyze school majors, the length of study, and student graduation rates. Through this algorithm, it was possible to identify one or more rules that can be used as benchmarks for predicting graduation rates. Based on the results and discussions of 30 students, the most effective rule for predicting graduation is a combination of the student's previous school major, a study period of 4 years or less, a GPA of 2.51-3.00, and passing all courses on time. Using the a priori algorithm, the rule was found to have a confidence value of 16 and a support value of 71.4%. This indicates that the rule is a reliable predictor of student graduation rates.

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Kolbe, Laura. "Graduation Poem." Annals of Internal Medicine 174, no. 6 (June 2021): 870. http://dx.doi.org/10.7326/m20-2101.

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29

McNett, Lorraine. "GED Graduation." Adult Learning 6, no. 2 (November 1994): 19–30. http://dx.doi.org/10.1177/104515959400600212.

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30

Alexander, Meena. "Graduation 1949." Callaloo 36, no. 1 (2013): 29–30. http://dx.doi.org/10.1353/cal.2013.0050.

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31

Gannon, Frank. "Graduation day." EMBO reports 7, no. 9 (August 11, 2006): 845. http://dx.doi.org/10.1038/sj.embor.7400792.

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32

Timpson, Erik. "Doctoral graduation!" IEEE Instrumentation & Measurement Magazine 18, no. 2 (April 2015): 38–40. http://dx.doi.org/10.1109/mim.2015.7066683.

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33

Andersson, Andreas. "Graduation joy." Nature 441, no. 7095 (June 2006): 904. http://dx.doi.org/10.1038/nj7095-904c.

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34

Larson, Lisa M., Kathryn M. Pesch, Spurty Surapaneni, Verena S. Bonitz, Tsui-Feng Wu, and James D. Werbel. "Predicting Graduation." Journal of Career Assessment 23, no. 3 (August 19, 2014): 399–409. http://dx.doi.org/10.1177/1069072714547322.

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35

Fisk, Candace, Vicki Dunlop, and Toni Sills-briegel. "Graduation Exhibitions." Clearing House: A Journal of Educational Strategies, Issues and Ideas 71, no. 1 (September 1997): 4–5. http://dx.doi.org/10.1080/00098659709599312.

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36

Raab, Lawrence. "Graduation address." Academic Questions 14, no. 4 (December 2001): 43. http://dx.doi.org/10.1007/s12129-001-1035-2.

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37

Kovatch, Kevin J. "Graduation Gift." JAMA 312, no. 13 (October 1, 2014): 1301. http://dx.doi.org/10.1001/jama.2014.10544.

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38

Coskey, Samuel. "May Graduation." Journal of Humanistic Mathematics 13, no. 2 (July 2023): 458–68. http://dx.doi.org/10.5642/jhummath.evzk5964.

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Carlisle, Jay C. "Graduation Remarks." Pace Law Review 22, no. 2 (April 1, 2002): 395. http://dx.doi.org/10.58948/2331-3528.1245.

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Anwar, Muhammad. "Prediction of the graduation rate of engineering education students using Artificial Neural Network Algorithms." International Journal of Research in Counseling and Education 5, no. 1 (June 10, 2021): 15. http://dx.doi.org/10.24036/00411za0002.

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The graduation rate of engineering education students on time dramatically affects the quality of learning. The purpose of this study is to predict the graduation rate of engineering education students. The method uses an artificial neural network algorithm combined with particle swarm optimization and forward selection, with 234 samples. The test results with Artificial Neural Network obtained 82.61% accuracy with predictions on time 149 and not on time 62. Artificial Neural Network with Particle Swarm Optimization obtained 91.30% accuracy with predictions on time 165, not on time 69. Furthermore, Artificial Neural Network with Particle Swarm Optimization and reduced by forwarding selection obtained 95.65% accuracy with predictions of the number of graduations on time 165 and not on time 69. Thus, the combination of the three algorithms can predict the graduation rate of engineering education students with high accuracy.

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Tu, Duong Thanh. "Improving Students' Employability in Ho Chi Minh City Today." Advances 5, no. 1 (February 27, 2024): 5–12. http://dx.doi.org/10.11648/j.advances.20240501.12.

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Our country's economy is on the rise, besides the advantages, there are also many difficulties and negatives that arise, such as unemployment among students after graduating. Increasing in the current market mechanism. The more the country develops, in addition to modern technology serving production and business, one of the factors determining the country's development is the labor force, in today's market economy the workforce Workers are students trained in universities, colleges..., the country's young force is very dynamic and capable at work. Therefore, students are a very important human resource that we need to know how to use in the most reasonable and effective way. But the current unemployment situation of students after graduating has greatly affected the economic and social development of the country. So the question for managers and the country is what causes that situation? Is it because the training process at universities still lacks many aspects? or because the State's policies are not reasonable in using labor. Employment after graduation is always a pressing issue not only for students themselves but also for families, schools and society. Having a job that matches the field of training is always the dream of not only students graduating from school but even for those still sitting in university lecture halls. Starting from the importance of employment for students after graduation, the topic "Improving the ability of students to find jobs in Ho Chi Minh City today" researches, surveys and evaluates. Reality, employability of students after graduation and research on factors affecting students' job search results. With that goal, the author uses scientific research methods, typically data investigation methods, to propose solutions to practical problems related to student employment. After graduation and advise on some solutions. With the hope that those solutions will be applied in practice to improve the employability of Ho Chi Minh City students after graduation in the current period.

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Nihcolson, Aleksander, Dali Santun Naga, and Viny Christanti Mawardi. "PENERAPAN METODE K-NEAREST NEIGHBOR DALAM MEMPREDIKSI WAKTU KELULUSAN MAHASISWA SARJANA YANG BERMAIN GAME." Jurnal Ilmu Komputer dan Sistem Informasi 9, no. 2 (August 25, 2021): 55. http://dx.doi.org/10.24912/jiksi.v9i2.13107.

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Graduating from college is something that students really want. By graduating from college, it has become a sign for a student to become a worthy scholar to continue and enter the next level. Graduation time is influenced by the academic value obtained from a student. If a student gets a high score, the student's graduation will be faster or on time. On the other hand, if a student gets a score below the average, the student's graduation time will be longer. At this time, one of the causes of students getting low grades is because students who are so busy playing games neglect their lectures and lose concentration while studying. So this can affect the time of their graduation. Students should be able to control themselves to manage their time playing online games and lectures in order to complete their obligations as a student, and students who get low grades for playing games should also be aware that getting low grades continuously will result in the student being threatened with dropping out (DO).Therefore, an information system program was designed that can be used by students who like to play games to be able to predict their graduation time so that they can find out their graduation time. The design of this program applies the K-Nearest Neighbor method which is a classification technique for objects based on learning data that is closest to the object.The final result of the application of the K-Nearest Neighbor method in the program has its advantages and disadvantages. The classification process is strongly influenced by the large amount of training data, and the determined value of 'K' (neighbors). The more the amount of training data, the level of accuracy can be reduced. The level of accuracy in testing using training data is 230 data, and using test data as much as 30 data with several specified 'K' values, namely, 2, 3, 4, 10. Accuracy results with 4 K values used can reach an accuracy rate of 90% .

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Satrio Junaidi, Rani Valicia Anggela, and Delsi Kariman. "Klasifikasi Metode Data Mining untuk Prediksi Kelulusan Tepat Waktu Mahasiswa dengan Algoritma Naïve Bayes, Random Forest, Support Vector Machine (SVM) dan Artificial Neural Nerwork (ANN)." Journal of Applied Computer Science and Technology 5, no. 1 (June 30, 2024): 109–19. http://dx.doi.org/10.52158/jacost.v5i1.489.

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Timely graduation of students is essential for determining the quality of college. Universities must know the percentage of students' ability to complete their studies on time. So, to deal with this problem, data mining classification is carried out to predict student graduation on time to find patterns for student on-time graduation predictions. This research can yield new information to help colleges anticipate student graduations that are not on time. The method used is a classification data mining method with 4 algorithms: naïve Bayes, random forest, support vector machine (SVM), and artificial neural network (ANN). The attributes used are gender, parental income, length of guidance, working student status or not, semester 1 to semester 8 grades, and GPA. This study used Python 3 programming language on jupyter notebooks in Anaconda to process datasets. The distribution of datasets is divided by 70% for training data and 30% for testing data. The results of this study were obtained with the best algorithm accuracy in the support vector machine (SVM) algorithm is 0.94. Based on the results of this study, the accuracy is good for predicting student graduation on time.

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Sharun, Sara. "Enrollment in a Library Credit Course is Positively Related to the College Graduation Rates of Full Time Students." Evidence Based Library and Information Practice 10, no. 2 (June 14, 2015): 156. http://dx.doi.org/10.18438/b85w21.

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A Review of: Cook, J.M. (2014). A Library credit course and student success rates: A longitudinal study. College & Research Libraries 75(3), 272-283. doi:10.5860/crl12-424 Abstract Objective – To determine the impact of a credit-bearing information literacy skills course on student success rates. Design – Observational Study. Setting – An academic library at a mid-sized university in Georgia, United States of America. Subjects – Nine cohorts of students (n=15,012) who entered the institution for the first time, on a full-time basis, each year between 1999 and 2007. Methods – Aggregate data on each student cohort was gathered from the Department of Institutional Research and Planning. Data included high school ACT and SAT scores, high school graduating GPAs, college graduating GPAs, and college graduation dates. The nine cohorts were each divided into two groups: students who took a credit library course (LIBR 1101) at some point during their student career, and students who did not. For each cohort, a Pearson Chi-Square test was used to determine statistical correlation between library course enrollment and four-, five-, and six-year graduation rates. Z-tests were used to determine a difference in the average graduation GPA of students who did and did not take the course, as well as a difference in the average high school graduation GPA, ACT, and SAT scores of the two groups in each cohort. Main Results – Graduation rates were positively associated with students who took the library course at some point during their studies. Students who took the library course graduated at higher rates than students who did not: 56% of those students who took the library course graduated within the study’s time frame, compared to 30% of those who did not take the course. On average, there was no significant difference in college graduation GPAs between students who did and did not take LIBR 1101. During the time period of the study, more students who took the course graduated than those who did not, but those students who took the course did not have higher graduating GPAs. Conclusion – Students who enrolled in LIBR 1101 at some point in their studies graduated at a significantly higher rate than students who did not.

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Kim, Juhee. "Student Perspectives on Barriers to Timely Graduation." International Research in Education 10, no. 1 (May 20, 2022): 35. http://dx.doi.org/10.5296/ire.v10i1.19876.

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This study examines the barriers preventing students from graduating college in four years and proposes strategies for enhancing student academic completion and retention in higher education. To explore contemporary perspectives, Appreciative Inquiry was employed. The findings revealed the personal and institutional level of challenges as well as the need for a support system to ensure timely graduation. Addressing students’ biggest barriers to timely graduation require campus-wide engagement and deep collaboration across institutional functions. Specifically, higher education institutions need to provide adequate academic, social, and cultural assistance to embrace international, minority, low-income, and first-generation college students.

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Iradhia, Shara, Husaini Husaini, and Laila Qadriah. "PENERAPAN DATA MINING UNTUK PREDIKSI KELULUSAN MAHASISWA FAKULTAS TEKNIK INFORMATIKA UNIVERSITAS JABAL GHAFUR MENGGUNAKAN METODE K-NEAREST NEIGHBOR BERBASIS WEB." Jurnal Real Riset 5, no. 3 (November 23, 2023): 452–58. http://dx.doi.org/10.47647/jrr.v5i3.1516.

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One of the instruments in campus accreditation, especially the faculty of informatics engineering at Jabal Ghafur University in order to get a good grade score is the satisfaction of graduate students getting decent jobs according to their fields, and is also a consideration during the study period for completing long lectures in a study program or faculty. The prediction of student graduation graduating on time or late does not only look at student data based on the highest scores as has been done in conventional grading systems so far. With advances in computer technology, especially in the field of data mining, it has brought many changes in the process of analyzing data patterns with data mining techniques, for example, in determining student graduation, a K-Nearest Neighbor method is used which processes data mining based on test data and sample data, especially on students. The results of the process for predicting student graduation are used sample data and training data. The sample data are students who are currently undergoing lectures and training data, namely students who have become alumni based on their graduation parameters. The final result obtained in the thesis research is that the system can input alternative data, criterion data, and process graduation predictions using the K-nearest neighbor method so that it can make a decision on whether the student who is being tested is "Passed" or "Graduated Late". The system can also display the K-nearest neighbor manual calculation flow and can display results reports.Key Words : Datamining, K-nearest Neighbor, Student Graduation Prediction, , PHPMySQL, Jabal Ghafur University

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Seilo, Noora, Susanna Paldanius, Reija Autio, Kristina Kunttu, and Minna Kaila. "Associations between e-health questionnaire responses, health checks and graduation: Finnish register-based study of 2011–2012 university entrants." BMJ Open 10, no. 12 (December 2020): e041551. http://dx.doi.org/10.1136/bmjopen-2020-041551.

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ObjectiveTo evaluate the association between health and study-related factors measured by an Electronic Health Questionnaire (eHQ), participation in a health examination process and graduation in a university student population.DesignNationwide, retrospective, register-based cohort study with a 6-year follow-up.SettingStudent health care in Finland. Finnish Student Health Service (FSHS) provides statutory student health services to university students in Finland. The health examination process of FSHS includes the eHQ provided annually to university entrants and a subsequent health check when necessary based on students’ eHQ response.ParticipantsA national cohort of university entrants from the 2011–2012 academic year (n=14 329, n (female)=8075, n (male)=6254).Outcome measuresThe primary outcome measure was graduation, measured based on whether a student had completed a bachelor’s, licentiate or master’s degree during the 6-year follow-up.ResultsSome 72% of the women and 60% of the men had graduated during the follow-up. The predictors in the eHQ associated with non-graduation differed by sex. Among the women’s low enthusiasm about studies (OR 2.6, 95% CI 1.9 to 3.6), low engagement with studies (OR 2.5, 95% CI 1.8 to 3.4) and daily smoking (OR 1.9, 95% CI 1.4 to 2.6) were the strongest predictors to non-graduation. Among the men, low engagement with studies (OR 3.7, 95% CI 2.5 to 5.5) and obesity (body mass index≥35) (OR 4.0, 95% CI 1.9 to 8.8) were the strongest predictors to non-graduation. Not attending the health check when referred was associated with non-graduation in both sexes: the OR for not graduating was 1.6 (95% CI 1.3 to 1.9) in women and 1.3 (95% CI 1.0 to 1.6) in men.ConclusionsEngagement and enthusiasm about studying in the first year are important predictors of graduation and therefore a potential intervention target. Health promotion initiatives conducted early in the studies may have a positive effect on students’ academic achievement.

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Pangesti, Witriana Endah, Indah Ariyati, Priyono Priyono, Sugiono Sugiono, and Rachmat Suryadithia. "Utilizing Genetic Algorithms To Enhance Student Graduation Prediction With Neural Networks." Sinkron 9, no. 1 (January 1, 2024): 276–84. http://dx.doi.org/10.33395/sinkron.v9i1.13161.

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The prediction of student graduation plays a crucial role in improving higher education efficiency and as-sisting students in graduating on time. Neural networks have been used for predicting student graduation; however, the performance of neural network models can still be enhanced to make predictions more accurate. Genetic algorithms are optimization methods used to improve the performance of neural network models by optimizing their parameters. The problem at hand is the suboptimal performance of neural networks in predict-ing student graduation. Thus, the objective is to leverage genetic algorithms to improve the accuracy of stu-dent graduation predictions, measure the improvements obtained, and compare the accuracy results between the genetic algorithm-optimized neural network model and the neural network model without optimization. The training process of the neural network model is conducted using training data obtained through experiments, and the accuracy results of the neural network model with and without genetic algorithm optimization are compared. The research findings indicate that by harnessing genetic algorithms to optimize the parameters of the neural network model, the accuracy of student graduation predictions increased by 2.78%. Furthermore, the Area Under the Curve (AUC) also improved by 0.037%. These results demonstrate that integrating genetic algorithms into the neural network model can significantly enhance prediction performance. Thus, this study successfully utilized genetic algorithms to improve student graduation predictions using a neural network model. Experimental results show that prediction accuracy and AUC values significantly increased after opti-mizing the neural network model's parameters with genetic algorithms. Therefore, the use of genetic algorithms can be considered an effective approach to improving student graduation predictions, thereby assisting educa-tional institutions in improving efficiency and helping students graduate on tim.

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Wibisono, David Leandro, and Zaenal Abidin. "Prediction of Student Graduation Predicts using Hybrid 2D Convolutional Neural Network and Synthetic Minority Over-Sampling Technique." Recursive Journal of Informatics 1, no. 1 (March 20, 2023): 27–34. http://dx.doi.org/10.15294/rji.v1i1.65646.

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Abstract. With the rapid growth of technology, educational institutions are constantly looking for ways to improve their services and enhance student performance. One of the significant challenges in higher education is predicting the graduation outcome of students. Predicting student graduation can help educators and academic advisors to provide early intervention and support to students who may be at risk of not graduating on time. In this paper, we propose a hybrid 2D convolutional neural network (CNN) and synthetic minority over-sampling technique (SMOTE) to predict the graduation outcome of students. Purpose: Knowing the results and how the Hybrid 2D Convolutional Neural Network (CNN) and Synthetic Minority Over-sampling Technique (SMOTE) algorithms work in predicting student graduation predicates. This algorithm uses a dataset based on family background variables and academic data. Methods/Study design/approach: This study uses the Hybrid 2D CNN algorithm for the classification process and SMOTE for the minority class over-sampling. Result/Findings: The prediction accuracy of the model using SMOTE is 96.31%. Meanwhile, the model that does not use SMOTE obtains an accuracy of 95.32%. Novelty/Originality/Value: This research shows that the use of a Hybrid 2D CNN algorithm with SMOTE gives better accuracy than without using SMOTE. The dataset used also proves that family background and student academic data can be used as a reference for predicting student graduation predicates.

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Acharya, Bala Ram. "Efforts of Nepal's Least Developed Countries (LDC) Graduation and Its Challenges." Humanities and Social Sciences Journal 15, no. 1-2 (December 31, 2023): 169–83. http://dx.doi.org/10.3126/hssj.v15i1-2.63790.

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The primary objectives of this study are to examine the initiatives undertaken by the government of Nepal for LDC graduation, identify challenges faced during process, and discuss the potential impact of graduation. This study is based on qualitative analysis of secondary data obtained from diverse domestic and international sources. Major sources include literature reviews, government policies, reports, and international declarations. Comprehensive desk review on the issue shows that Nepal has undertaken a strategic plan for LDC graduation, prioritizing investments in human capital, infrastructure, and healthcare, while addressing income disparities. Nepal has already achieved two requirements; HAI and EVI, and Nepal fell significantly short of the LDC graduation threshold of a per capita GNI. The plan includes achieving Sustainable Development Goals (SDGs) and progressing from a lower-middle income to an upper-middle-income nation by 2030.Despite progress, Nepal faces challenges in achieving graduation, including political instability, governance issues, and environmental vulnerabilities. The country lags in good governance rankings, which could hinder its development efforts. Nepal's commitment to SDGs is seen as a potential driver for LDC graduation, focusing on sustainability, gender equality, clean energy, job creation, and economic development. Nepal aims to become a high-income country by 2043, emphasizing poverty reduction, trade balance improvement, and overall quality of life enhancement. Leaving the LDC category will bring opportunities but also challenges, including the loss of special market access that was provided to the least developed countries like Nepal. Other challenges may involve growing trade and market capacity and strengthening the global competitiveness of internal production. Internally, intersectional variations of inequalities and poverty among and between regions, castes and ethnic groups in Nepal may generate challenges meeting LDC graduations and its sustainability.

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