scholarly journals Predictive Model of Postgraduate Student’s Dropout and Delay Using Machine Learning Algorithms

2021 ◽  
Vol 10 (2) ◽  
pp. 894-900
Keyword(s):  
Decision Tree ◽  
Prediction Model ◽  
Student Performance ◽  
Machine Learning Algorithms ◽  
Drop Out ◽  
Postgraduate Students ◽  
Number Of Children ◽  
Logical Regression ◽  
Degree Outcomes ◽  
The University

Neural networks and Logical Regression algorithm provide the best ways to classify data, but they are outperformed continuously by the Decision Tree in analyzing student performance. Therefore, many scholars have used the Decision Tree to predict student performance with greater success. This research analyzed postgraduate student degree outcomes using socioeconomic data to develop a prediction model, where Decision Tree recorded the highest accuracy of 92.79%, better than Logical Regression and Neural Network. For brevity, the Decision Tree was used to produce the prediction model. Based on the study findings, postgraduate students who delay or drop out at the university mostly lack sponsors or had decreased income. Besides, male students delay or drop out if they had financial issues more than their female counterparts. Age, money management skills, number of children, and health expenses are the other factors that contribute to higher dropout or delay at the university. Therefore, this study provides a reliable prediction model for degree outcomes, allowing personalized follow-up to improve graduation rates.

Impact of Postgraduate Students Dropout and Delay in University: Analysis Using Machine Learning Algorithms

2021 ◽  
Vol 10 (3) ◽  
pp. 1821-1826
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Prediction Model ◽  
Learning Algorithms ◽  
Dropout Rate ◽  
Machine Learning Algorithms ◽  
Accuracy Class ◽  
Postgraduate Students ◽  
Student Dropout ◽  
Postgraduate Studies

Cost of education and economic background are some factors that influence student dropout from postgraduate studies. However, high dropouts do not affect the students only, but also impact university revenue. This research analyzes various literature on machine learning algorithms and applies suitable algorithm to produce a prediction model. This study indicates that decision tree and Random Forest algorithms have better accuracy, class recall, and class precision than Naïve Bayes. Therefore, the prediction model uses the Decision Tree algorithm to provide various approaches to maximize revenue in universities. The findings indicate high dropout rates negatively impact university revenue, while low rates influence revenue positively. Other aspects like grants received by students, the number of research publications, anddegree level also positively or negatively impact revenue if the dropout rate is medium. A complete understanding of this prediction model can identify and minimize the risk of early withdrawal or delayed graduation and improve revenue generation by universities.

scholarly journals KOMPARASI ALGORITMA DECISION TREE, NAIVE BAYES DAN K-NEAREST NEIGHBOR UNTUK MEMPREDIKSI MAHASISWA LULUS TEPAT WAKTU

2020 ◽  
Vol 5 (2) ◽  
pp. 265-270 ◽  
Author(s):  
Agus Budiyantara ◽  
Irwansyah Irwansyah ◽  
Egi Prengki ◽  
Pandi Ahmad Pratama ◽  
Ninuk Wiliani
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Drop Out ◽  
University Management ◽  
K Nearest Neighbor ◽  
Academic Factors ◽  
The University

Private Universities (PTS) compete so tight in providing performance in producing quality graduates. In addition, the number of universities in Indonesia which counts a lot both PTN and PTS makes the higher competition between universities as well. So the university strives to improve quality and provide the best education for service recipients, namely students, where one of the problems if there are some students who are late graduating or not on time so that it becomes an obstacle to the progress of the college. Prediction of students graduating on time is needed by university management in determining preventive policies related to early prevention of Drop Out (DO) cases. This prediction aims to determine the academic factors that influence the period of study and build the best prediction model with Data Mining techniques. There are 11 attributes used for Data Mining Classification, namely NPM, Gender, Age, Department, Class, Occupation, Semester 1 Achievement Index, Semester 2 Achievement Index, Semester 3 Achievement Index, Semester 4 Achievement Index and Information as result attributes. From the results of evaluations and validations that have been carried out using the RapidMiner tools the accuracy of the Decision Tree (C4.5) method is 98.04% in the 3rd test. The accuracy of the Naïve Bayes Method is 96.00% in the 4th test. And the accuracy of the K-Nearest Neighbor Method (K-NN) of 90.00% in the second test.

scholarly journals Classification of Student Performance Dataset using Machine Learning Algorithms

2019 ◽  
Vol 9 (2S2) ◽  
pp. 752-757
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Student Performance ◽  
Performance Metrics ◽  
Naive Bayes ◽  
Research Work ◽  
Naïve Bayes ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector

The scope of this research work is to identify the efficient machine learning algorithm for predicting the behavior of a student from the student performance dataset. We applied Support Vector Machines, K-Nearest Neighbor, Decision Tree and Naïve Bayes algorithms to predict the grade of a student and compared their prediction results in terms of various performance metrics. The students who visited many resources for reference, made academic related discussions and interactions in the class room, absent for minimum days, cared by parents care have shown great improvement in the final grade. Among the machine learning techniques we have used, SVM has shown more accuracy in terms of four important attribute. The accuracy rate of SVM after tuning is 0.80. The KNN and decision tree achieves the accuracy of 0.64, 0.65 respectively whereas the Naïve Bayes achieves 0.77.

scholarly journals Classification and Prediction of Student Academic Performance using Gray Wolf Optimization Based Relief-F Budget Random Forest

2019 ◽  
Vol 8 (3) ◽  
pp. 4411-4418
Keyword(s):  
Random Forest ◽  
Prediction Model ◽  
Student Performance ◽  
Nearest Neighbor ◽  
Gray Wolf ◽  
K Nearest Neighbor ◽  
Efficient Prediction ◽  
Ranking Order ◽  
Artificial Neural Network Ann ◽  
The University

The student academic prediction model helps to predict the student performance that helps the university to provide necessary care to the particular students. Efficient prediction model helps to encourage the student for better performance in the academic. In this research, the Relief-F Budget Tree Random Forest with Gray Wolf Optimization (RFBTRF-GWO) method is proposed for the feature selection. The Gray Wolf Optimization (GWO) helps to scale the relevant feature with ranking order from the features selected by the Relief-F Budget Tree Random Forest (RFBTRF). The selected features are given as input to the classifier for the effective prediction. The k-Nearest Neighbor (kNN) and Artificial Neural Network (ANN) are used for the classification. The proposed RFBTRF-GWO method is evaluated on the three datasets such as two UCI datasets and one collected dataset. The RFBTRF-GWO has a higher performance accuracy of 96.2 % while the existing method RFBTRF has an accuracy of 70.88 %.

scholarly journals The Investigation of Student Dropout Prediction Model in Thai Higher Education Using Educational Data Mining: A Case Study of Faculty of Science, Prince of Songkla Uni-versity

2019 ◽  
Vol 27 (1) ◽  
pp. 356-367 ◽  
Author(s):  
Jarutas Pattanaphanchai ◽  
Koranat Leelertpanyakul ◽  
Napa Theppalak
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Prediction Model ◽  
Retention Rate ◽  
Dropout Rate ◽  
Drop Out ◽  
Data Mining Technique ◽  
Student Dropout ◽  
The University

The student’s retention rate is one of the challenging issues that representing the quality of the university. A high dropout rate of students affects not only the reputation of the university but also the students’ career in the future. Therefore, there is a need of student dropout analysis in order to improve the academic plan and management to reduce students drop out from the university as well as to  enhance the quality of the higher education system. Data mining technique provides powerful methods for analysis and the prediction the dropout. This paper proposes a model for predicting students’ dropout using the dataset from the representative of the largest public university in the Southen part of Thailand. In this study, data from Faculty of Science, Prince of Songkla University was collected from academic year of 2013 to 2017. The experiment result shows that JRip rule induction is the best technique to generate a prediction model receiving the highest accuracy value of 77.30%. The results highlight the potential prediction model that can be used to detect the early state of dropping out of the student which the university can provide supporting program to improve the student retention rate

Student Preparation for Dysphagia Practice: Universities and Practicum Sites Supporting a Common Outcome

2016 ◽  
Vol 1 (13) ◽  
pp. 122-129 ◽  
Author(s):  
Wendy Chase ◽  
Lucinda Soares Gonzales
Keyword(s):  
Health Care ◽  
Student Performance ◽  
Classroom Setting ◽  
Medical Settings ◽  
Student Preparation ◽  
Health Care Settings ◽  
The University ◽  
University Of Connecticut

This article will describe the approach to dysphagia education in a classroom setting at the University of Connecticut (UCONN), explore the disparity between student performance in schools vs. health care settings that was discovered at UCONN, and offer suggestions for practicum supervisors in medical settings to enhance student acquisition of competence.

The Student Room Survey: Changes in the Post Graduate Study for Cardiothoracic Surgery

2020 ◽  
Vol 23 (2) ◽  
pp. 71-74
Author(s):  
Md Faizus Sazzad ◽  
Mohammed Moniruzzaman ◽  
Dewan Iftakher Raza Choudhury ◽  
Arif Ahmed Mohiuddin ◽  
Raafi Rahman ◽  
...  
Keyword(s):  
Thoracic Surgery ◽  
Retrospective Analysis ◽  
Primary Objective ◽  
Graduate Study ◽  
Postgraduate Students ◽  
Depth Interview ◽  
Secondary Objective ◽  
Cardio Vascular ◽  
The University ◽  
Day By Day

Background: The number of postgraduate students in Cardiac surgical discipline is increasing day by day with incremental proportion are measurably suffering from the unnecessary lingering of the present course curriculum. The primary objective of this study was to find out the last 5 years’ of results of Masters in Surgery course under the University of Dhaka from a student room survey. A secondary objective was to find out positive changes that could show us the way of a step toward up-gradation. Methods: It is a retrospective analysis of all examination results of Cardio-vascular & Thoracic Surgery published since January 2008 to January 2013 from the University of Dhaka with in depth interview of 11 participants. Results: 85.24% students failed to pass part-I of Masters in Surgery for Cardio-vascular & Thoracic Surgery course while, 82.18% in part-II and 71.28% failed to pass the final part. Average 2.51 attempts needed to complete each part of the designed course resulted into lingering of course duration for 42.18 months/student. In the thoracic surgery discipline the number of students alarmingly reduced up to 0% in the recent academic sessions. Conclusions: Masters in Surgery is resulting in unnecessary prolongation of the course. We should step forward to meet the next generation challenge. Journal of Surgical Sciences (2019) Vol. 23(2): 71-74

Selection of children with ultra-severe traumatic brain injury for neurosurgical intervention

2019 ◽  
Vol 23 (6) ◽  
pp. 670-679
Author(s):  
Krista Greenan ◽  
Sandra L. Taylor ◽  
Daniel Fulkerson ◽  
Kiarash Shahlaie ◽  
Clayton Gerndt ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Decision Tree ◽  
Prediction Model ◽  
Severe Traumatic Brain Injury ◽  
Pupillary Response ◽  
Data Set ◽  
Robust Model ◽  
Gcs Score ◽  
Combined Data

OBJECTIVEA recent retrospective study of severe traumatic brain injury (TBI) in pediatric patients showed similar outcomes in those with a Glasgow Coma Scale (GCS) score of 3 and those with a score of 4 and reported a favorable long-term outcome in 11.9% of patients. Using decision tree analysis, authors of that study provided criteria to identify patients with a potentially favorable outcome. The authors of the present study sought to validate the previously described decision tree and further inform understanding of the outcomes of children with a GCS score 3 or 4 by using data from multiple institutions and machine learning methods to identify important predictors of outcome.METHODSClinical, radiographic, and outcome data on pediatric TBI patients (age < 18 years) were prospectively collected as part of an institutional TBI registry. Patients with a GCS score of 3 or 4 were selected, and the previously published prediction model was evaluated using this data set. Next, a combined data set that included data from two institutions was used to create a new, more statistically robust model using binomial recursive partitioning to create a decision tree.RESULTSForty-five patients from the institutional TBI registry were included in the present study, as were 67 patients from the previously published data set, for a total of 112 patients in the combined analysis. The previously published prediction model for survival was externally validated and performed only modestly (AUC 0.68, 95% CI 0.47, 0.89). In the combined data set, pupillary response and age were the only predictors retained in the decision tree. Ninety-six percent of patients with bilaterally nonreactive pupils had a poor outcome. If the pupillary response was normal in at least one eye, the outcome subsequently depended on age: 72% of children between 5 months and 6 years old had a favorable outcome, whereas 100% of children younger than 5 months old and 77% of those older than 6 years had poor outcomes. The overall accuracy of the combined prediction model was 90.2% with a sensitivity of 68.4% and specificity of 93.6%.CONCLUSIONSA previously published survival model for severe TBI in children with a low GCS score was externally validated. With a larger data set, however, a simplified and more robust model was developed, and the variables most predictive of outcome were age and pupillary response.

Use of reference management software among postgraduate students in Greece

2021 ◽  
pp. 096100062199641
Author(s):  
Ilias Nitsos ◽  
Afrodite Malliari ◽  
Rodopi Chamouroudi
Keyword(s):  
Student Discipline ◽  
Bibliographic Databases ◽  
Background Characteristics ◽  
Key Factors ◽  
Online Questionnaire ◽  
Management Software ◽  
Postgraduate Students ◽  
Significant Relationships ◽  
The University ◽  
Degree Level

The use of reference management software in the context of academic work and research is the main subject of this study. The study focuses on the extent to which postgraduate students at the Aristotle University of Thessaloniki, one of the largest Greek universities, make use of – or avoid using – reference management software tools to organize their bibliographic databases and to automate the process of creating references and citations. The study also tries to find out which are the key factors for their choices and whether certain background characteristics affect their behavior. It should be mentioned that no previous studies have been conducted in Greece regarding the use of reference management software in the academic environment. An online questionnaire was sent to the postgraduate students at the University and a result set of 545 responses was collected and analyzed. The majority (almost two-thirds) of the respondents identified themselves as non-users and one-third identified themselves as reference management software users. Among the latter, Mendeley was found to be the software used by more than two-thirds of the users and was followed by EndNote and Zotero. It is worth mentioning that Mendeley is the software officially recommended by the University’s central library to its users but most of the students (more than 60%) were not aware of this fact. In terms of background characteristics, the analysis revealed, among other things, statistically significant relationships between degree level, student discipline and preferences, reference management software features, and potential future use of reference management software.

Sign in / Sign up

Export Citation Format

Share Document