scholarly journals Perbandingan Algoritma C4.5 dan CART dalam Memprediksi Kategori Indeks Prestasi Mahasiswa

2018 ◽  
Vol 6 (2) ◽  
pp. 76-83
Author(s):  
Dea Alverina ◽  
Antonius Rachmat Chrismanto ◽  
R. Gunawan Santosa
Keyword(s):  
Information Technology ◽  
Grade Point Average ◽  
Data Availability ◽  
Training Data ◽  
Grade Point ◽  
First Semester ◽  
Testing Data ◽  
Accuracy Result ◽  
The Difference ◽  
Accuracy Of Prediction

This research compared the accuracy of prediction of Grade Point Average (GPA) of the first semester students using C4.5 and CART algorithms in Faculty of Information Technology (FTI), Universitas Kristen Duta Wacana (UKDW). This research also explored various parameters such as numeric attribute categorization, data balance, GPA categories number, and different attributes availability due to the difference of data availability between Achievement Admission (AA) and Regular Admission (RA). The training data used to create decision tree were FTI students, 2008-2015 batch, while the testing data were FTI students, 2016 batch. The accuracy of prediction was measured by using crosstab table. In AA, the accuracy of both algorithms can be achieved about 86.86%. Meanwhile, in RA the accuracy of C4.5 is about 61.54% and CART is about 63.16%. From these accuracy result, both algorithms are better to predict AA rather than RA.

scholarly journals Comparison of National Board of Chiropractic Examiners part I examination scores between tutors and tutees at a chiropractic college

2016 ◽  
Vol 30 (2) ◽  
pp. 104-107 ◽  
Author(s):  
Amilliah W. Kenya ◽  
John F. Hart ◽  
Charles K. Vuyiya
Keyword(s):  
Grade Point Average ◽  
Test Scores ◽  
Effect Sizes ◽  
Grade Point ◽  
Board Examination ◽  
National Board ◽  
The Subject ◽  
The Mean ◽  
The Difference ◽  
Tutor Group

Objective: This study compared National Board of Chiropractic Examiners part I test scores between students who did and did not serve as tutors on the subject matter. Methods: Students who had a prior grade point average of 3.45 or above on a 4.0 scale just before taking part I of the board exams were eligible to participate. A 2-sample t-test was used to ascertain the difference in the mean scores on part I between the tutor group (n = 28) and nontutor (n = 29) group. Results: Scores were higher in all subjects for the tutor group compared to the nontutor group and the differences were statistically significant (p < .01) with large effect sizes. Conclusion: The tutors in this study performed better on part I of the board examination compared to nontutors, suggesting that tutoring results in an academic benefit for tutors themselves.

scholarly journals Implementation of Fuzzy Tsukamoto Algorithm in Determining Work Feasibility

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Grade Point Average ◽  
Fuzzy System ◽  
Membership Functions ◽  
Grade Point ◽  
The Difference ◽  
Parameter Values ◽  
Academic Potential ◽  
Potential Test

The process of determining eligibility for someone is usually given with the same value. The determination of eligibility is determined based on several criteria. These criteria are the ability to Academic Potential Test (APT) and Grade Point Average (GPA). The Tsukamoto fuzzy system is the model used in this paper. Each input variable is divided into two membership functions. There are nine rules of Tsukamoto's model applied. The system also provides following changes of parameters if the parameter values are to be changed. The result of the difference of the employability is given to them. The greater the APT score, the more the value of the feasibility of the work obtained. The more GPA values gained, the greater the value of the workplace eligibility.

scholarly journals Impact of the English Advanced Placement (AP) Program on College Grade Point Average among Rural Appalachian Students

2018 ◽  
Vol 35 (3) ◽  
Author(s):  
Shannon Deaton
Keyword(s):  
Grade Point Average ◽  
Advanced Placement ◽  
Eastern Kentucky ◽  
Grade Point ◽  
First Semester ◽  
College And University ◽  
Study Results ◽  
Significant Difference ◽  
Act Score ◽  
The Impact

This study explored the impact of the English Advanced Placement (AP) program on college success among rural Appalachian students attending four private colleges in central and eastern Kentucky: Alice Lloyd College, Georgetown College, Lindsey Wilson College, and University of the Cumberlands. A Pearson Product-Moment Correlation r and an independent-samples t-test were conducted. With respect to rural Appalachian students, statistical analyses revealed that the English ACT score is a better predictor of first-semester college grade point average than the English AP score. Analyses also revealed no statistically significant difference between first-semester college GPAs of rural Appalachian students with English AP credit and rural Appalachian students without AP credit. The study results are helpful for students, parents, administrators, and policymakers evaluating the English AP program at local high schools and colleges.  

Can We Predict Podiatric Medical School Grade Point Average Using an Admission Screen?

2012 ◽  
Vol 102 (6) ◽  
pp. 499-504 ◽  
Author(s):  
Graham P. Shaw ◽  
Evelio Velis ◽  
David Molnar
Keyword(s):  
Medical School ◽  
Grade Point Average ◽  
Test Score ◽  
College Admission ◽  
Medical College Admission Test ◽  
Data Set ◽  
Grade Point ◽  
First Semester ◽  
Medical College ◽  
Admission Test

Background: Most medical school admission committees use cognitive and noncognitive measures to inform their final admission decisions. We evaluated using admission data to predict academic success for podiatric medical students using first-semester grade point average (GPA) and cumulative GPA at graduation as outcome measures. Methods: In this study, we used linear multiple regression to examine the predictive power of an admission screen. A cross-validation technique was used to assess how the results of the regression model would generalize to an independent data set. Results: Undergraduate GPA and Medical College Admission Test score accounted for only 22% of the variance in cumulative GPA at graduation. Undergraduate GPA, Medical College Admission Test score, and a time trend variable accounted for only 24% of the variance in first-semester GPA. Conclusions: Seventy-five percent of the individual variation in cumulative GPA at graduation and first-semester GPA remains unaccounted for by admission screens that rely on only cognitive measures, such as undergraduate GPA and Medical College Admission Test score. A reevaluation of admission screens is warranted, and medical educators should consider broadening the criteria used to select the podiatric physicians of the future. (J Am Podiatr Med Assoc 102(6): 499–504, 2012)

scholarly journals Winning Models for Grade Point Average, Grit, and Layoff in the Fragile Families Challenge

2019 ◽  
Vol 5 ◽  
pp. 237802311882041 ◽  
Author(s):  
Daniel E. Rigobon ◽  
Eaman Jahani ◽  
Yoshihiko Suhara ◽  
Khaled AlGhoneim ◽  
Abdulaziz Alghunaim ◽  
...  
Keyword(s):  
Grade Point Average ◽  
Data Science ◽  
Mean Value ◽  
Training Data ◽  
Fragile Families ◽  
Grade Point ◽  
Out Of Sample ◽  
The Mean ◽  
Selection Operator ◽  
Better Than

In this article, the authors discuss and analyze their approach to the Fragile Families Challenge. The data consisted of more than 12,000 features (covariates) about the children and their parents, schools, and overall environments from birth to age 9. The authors’ modular and collaborative approach parallelized prediction tasks and relied primarily on existing data science techniques, including (1) data preprocessing: elimination of low variance features, imputation of missing data, and construction of composite features; (2) feature selection through univariate mutual information and extraction of nonzero least absolute shrinkage and selection operator coefficients; (3) three machine learning models: random forest, elastic net, and gradient-boosted trees; and finally (4) prediction aggregation according to performance. The top-performing submissions produced winning out-of-sample predictions for three outcomes: grade point average, grit, and layoff. However, predictions were at most 20 percent better than a baseline that predicted the mean value of the training data for each outcome.

scholarly journals First semester academic performance: The importance of early indicators of non-engagement

2018 ◽  
Vol 9 (4) ◽  
pp. 1-12 ◽  
Author(s):  
Jacques Van der Meer ◽  
Stephen Scott ◽  
Keryn Pratt
Keyword(s):  
Academic Performance ◽  
Student Success ◽  
Grade Point Average ◽  
First Year ◽  
First Year Students ◽  
Grade Point ◽  
Retention Strategy ◽  
First Semester ◽  
Efficiency And Effectiveness ◽  
The University

Success, progression and retention of students are goals of many university strategic directions and policies. For many decades it has been recognised that the greatest focus in any retention strategy should be on first-year students. University of Otago too has goals around student success. The Strategic Plan of the institution also identified that in the context of a fiscally constrained environment, all of our activities and processes need to be assessed for efficiency and effectiveness.  To this end, a pilot was undertaken in one area of the university to identify possible indicators of first-year students’ non-engagement in the first semester and their possible impact on the first semester academic performance. The findings suggest that there are indeed some indicators that predict Grade Point Average at the end of the first semester.

Interventions Using Scales Measuring Expected and Actual Adjustment to College

1993 ◽  
Vol 13 (1) ◽  
pp. 9-17
Author(s):  
Robert W. Baker ◽  
Kim L. Schultz
Keyword(s):  
At Risk ◽  
Grade Point Average ◽  
College Freshmen ◽  
Score Change ◽  
Grade Point ◽  
Freshman Year ◽  
First Semester ◽  
Adjustment To College

This study evaluates the consequences of intervention by interview with college freshmen identified by questionnaires as being at risk in three respects: (a) low prematriculation expectations regarding capacity for dealing with the transition into college, (b) significant postmatriculation disillusionment regarding adjustive capacity, and (c) low self-assessed adjustment in the first semester. Effects were analyzed in terms of score change on scales of adjustment, freshman year grade point average (GPA), number of credits earned in the freshman year, and continuance of enrollment.

scholarly journals A Model for Rapid Selection and COVID-19 Prediction with Dynamic and Imbalanced Data

2021 ◽  
Vol 13 (6) ◽  
pp. 3099 ◽  
Author(s):  
Jeonghun Kim ◽  
Ohbyung Kwon
Keyword(s):  
Imbalanced Data ◽  
The United States ◽  
Data Availability ◽  
Training Data ◽  
Timely Manner ◽  
Greedy Method ◽  
Imbalanced Dataset ◽  
Rapid Spread ◽  
Accuracy Of Prediction

The COVID-19 pandemic is threatening our quality of life and economic sustainability. The rapid spread of COVID-19 around the world requires each country or region to establish appropriate anti-proliferation policies in a timely manner. It is important, in making COVID-19-related health policy decisions, to predict the number of confirmed COVID-19 patients as accurately and quickly as possible. Predictions are already being made using several traditional models such as the susceptible, infected, and recovered (SIR) and susceptible, exposed, infected, and resistant (SEIR) frameworks, but these predictions may not be accurate due to the simplicity of the models, so a prediction model with more diverse input features is needed. However, it is difficult to propose a universal predictive model globally because there are differences in data availability by country and region. Moreover, the training data for predicting confirmed patients is typically an imbalanced dataset consisting mostly of normal data; this imbalance negatively affects the accuracy of prediction. Hence, the purposes of this study are to extract rules for selecting appropriate prediction algorithms and data imbalance resolution methods according to the characteristics of the datasets available for each country or region, and to predict the number of COVID-19 patients based on these algorithms. To this end, a decision tree-type rule was extracted to identify 13 data characteristics and a discrimination algorithm was selected based on those characteristics. With this system, we predicted the COVID-19 situation in four regions: Africa, China, Korea, and the United States. The proposed method has higher prediction accuracy than the random selection method, the ensemble method, or the greedy method of discriminant analysis, and prediction takes very little time.

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