scholarly journals Embedding Logistic Regression Model in Decision Support Software for Student Graduation Prediction

2015 ◽  
Vol 2 ◽  
pp. 104-110
Author(s):  
Ace C. Lagman
Keyword(s):  
Logistic Regression ◽  
Undergraduate Students ◽  
First Year ◽  
Full Time ◽  
Students First ◽  
Student Graduation ◽  
Education Statistics ◽  
Categorical Response ◽  
Support Software ◽  
First Time

Logistic regression is a predictive modeling technique that finds an association between the independent variables and the logarithm of the odds of a categorical response variable. This is one of the techniques used in analyzing a categorical dependent variable. The study focused on the application of logistic regression in predicting student graduation by generating data models that could early predict and identify students who are prone to not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions. The student graduation rate is the percentage of a school’s first-time, first-year undergraduate students who complete their program successfully. Most students’ first-year freshmen enrolled at the tertiary level failed to graduate. According to the National Center for Education Statistics, almost half of the first time freshmen full-time students who began seeking a bachelor’s degree do not graduate. The colleges and universities consisting of high leaver rates go through a loss of fees and potential alumni contributors.

scholarly journals Classification Algorithm Accuracy Improvement for Student Graduation Prediction Using Ensemble Model

2020 ◽  
Vol 10 (10) ◽  
pp. 723-727
Author(s):  
Ace C. Lagman ◽  
◽  
Lourwel P. Alfonso ◽  
Marie Luvett I. Goh ◽  
Jay-ar P. Lalata ◽  
...  
Keyword(s):  
Classification Algorithm ◽  
Bachelor's Degree ◽  
Ensemble Model ◽  
Full Time ◽  
Accuracy Improvement ◽  
Student Graduation ◽  
Education Statistics ◽  
First Time

According to National Center for Education Statistics, almost half of the first-time freshmen full time students who began seeking a bachelor’s degree do not graduate. The imbalance between

scholarly journals Predictive Analysis of Higher Education Graduation and Retention in Saudi Arabia u sing Multinomial Logistic Regression

2021 ◽  
Vol 3 (6) ◽  
pp. 1-8
Author(s):  
Ahmed Bagabir ◽  
◽  
Mohammad Zaino ◽  
Ahmed Abutaleb ◽  
Ahmed Fagehi ◽  
...  
Keyword(s):  
Higher Education ◽  
Logistic Regression ◽  
Undergraduate Students ◽  
Degree Completion ◽  
Multinomial Logistic Regression ◽  
At Risk Students ◽  
Retention Rates ◽  
First Year ◽  
Important Indicator ◽  
Admissions Policy

It is suggested that this study contributes by establishing a robust methodology for analyzing the longitudinal outcomes of higher education. The current research uses multinomial logistic regression. To the knowledge of the authors, this is the first logistic regression analysis performed at Saudi higher education institutions. The study can help decision-makers take action to improve the academic performance of at-risk students. The analyses are based on enrollment and completion data of 5,203 undergraduate students in the colleges of engineering and medicine. The observation period was extended for ten academic years from 2010 to 2020. Four outcomes were identified for students: (i) degree completion on time, (ii) degree completion with delay, (iii) dropout, and (iv) still enrolled in programs. The objectives are twofold: (i) to study the present situation by measuring graduation and retention rates with benchmarking, and (ii) to determine the effect of twelve continuous and dummy predictors (covariates) on outcomes. The present results show that the pre-admission covariates slightly affect performance in higher education programs. The results indicate that the most important indicator of graduation is the student's achievement in the first year of the program. Finally, it is highly suggested that initiatives be taken to increase graduation and retention rates and to review the admissions policy currently in place.

Comparing Data Mining Models in Academic Analytics

2016 ◽  
pp. 970-987
Author(s):  
Dheeraj Raju ◽  
Randall Schumacker
Keyword(s):  
Data Mining ◽  
Full Time ◽  
Students At Risk ◽  
Academic Analytics ◽  
Institutional Researchers ◽  
Forest Models ◽  
Random Forest Models ◽  
Student Graduation ◽  
Misclassification Rates ◽  
First Time

The goal of this research study was to compare data mining techniques in predicting student graduation. The data included demographics, high school, ACT profile, and college indicators from 1995-2005 for first-time, full-time freshman students with a six year graduation timeline for a flagship university in the south east United States. The results indicated no difference in misclassification rates between logistic regression, decision tree, neural network, and random forest models. The results from the study suggest that institutional researchers should build and compare different data mining models and choose the best one based on its advantages. The results can be used to predict students at risk and help these students graduate.

scholarly journals The Relationship Between Campus Recreation Facility Use and Retention for First-Time Undergraduate Students

2020 ◽  
pp. 152102512092134 ◽  
Author(s):  
Sera J. Zegre ◽  
Rodney P. Hughes ◽  
Andrew M. Darling ◽  
Craig R. Decker
Keyword(s):  
Undergraduate Students ◽  
Family Characteristics ◽  
Campus Recreation ◽  
Full Time ◽  
Academic Goals ◽  
Student Records ◽  
Academic Preparedness ◽  
First Year Retention ◽  
First Time ◽  
The Relationship

This study examines the relationship between campus recreation facility access and first-year retention of full-time, first-time undergraduate students at a public university for 2014–2015 through 2016–2017. Authors examine differences between facility users and nonusers by pairing facility swipe card data with student records. Statistical analysis includes logistic regression and matching approaches, controlling for student demographics, academic preparedness, academic goals, family characteristics, and various environmental factors. Results show a positive and significant relationship between recreation facility use and retention, including 7.1 to 8.4 percentage points higher retention for users versus nonusers, holding other variables constant. Subsample analysis suggests the relationship between recreation facility use and retention differs across student subgroups. Key study contributions include linking card swipe data on facility usage with extensive student records, clearly defining facility users and nonusers, and introducing a new robustness check based on assignment of students to residence halls different distances from recreation facilities.

scholarly journals First-year students’ perceptions of team-based learning in a new medical genetics course

2019 ◽  
Vol 43 (3) ◽  
pp. 170-177
Author(s):  
Vinicius Canato Santana ◽  
Carlos Rocha Oliveira ◽  
Ramon Bossardi Ramos
Keyword(s):  
Medical Education ◽  
New Technologies ◽  
Genetic Diseases ◽  
Medical Genetics ◽  
First Year ◽  
First Year Students ◽  
Students First ◽  
First Year Medical Students ◽  
First Time ◽  
Large Groups

ABSTRACT Background Medical education has evolved considerably over the last few years, especially through adoption of new technologies and active methodologies. These methodologies aim to improve learning and engage students deeply in the process. TBL is a methodology widely used in health schools, including Medical Schools. We can use it to work with large groups, divided into small teams. The students first work individually, then within teams, and finally the groups cooperate to solve applied problems. Objectives To describe students’ perceptions and satisfaction about a Medical Genetics course organized into blocks of subject in which we used TBL sessions with first-year medical students. Methods A Medical Genetics course were organized into subject blocks in which a TBL session was conducted in each of these blocks to improve the learning process. At the end of the course, the students answered a questionnaire on satisfaction and perceptions. Results By the first time we described a Medical Genetics course organized into 5 blocks of subject matter on a total of 25 genetic diseases in which a TBL session was conducted in each of these blocks. We enrolled a total of 290 participants and 96% of the students were satisfied with TBL. Furthermore, 97% of students believe that TBL helped them to learn, and 87% approved of use of TBL in the future at other stages of their medical course. Conclusion Application of the TBL method during a medical genetics course was well-received by students and proved an important tool in the structures of curricula for medical education at this university.

scholarly journals An Application of the Probabilistic Model to the Prediction of Student Graduation Using Bayesian Belief Network

1970 ◽  
Vol 3 (1) ◽  
pp. 63-71 ◽  
Author(s):  
Jiraporn Yingkuachat ◽  
Prasong Praneetpolgrang ◽  
Boonserm Kijsirikul
Keyword(s):  
Undergraduate Students ◽  
Bayesian Belief Network ◽  
Experimental Result ◽  
First Year ◽  
Data Mining Technique ◽  
Grade Point ◽  
Belief Network ◽  
Bayes Net ◽  
Student Graduation ◽  
High Prediction

This paper proposes an alternative to the prediction of education accomplishment. It employs a data mining technique, the Bayesian belief network (Bayes net). The technique is used to analyze the independent variables that affect the education accomplishment result of vocational students, undergraduate students and graduate students. The machine learning tool, WEKA, is used to construct the prediction model that is accurate for the prediction based on k-fold cross-validation. The experimental result shows that the Bayes net technique is able to determine important variables for the prediction of the result of education accomplishment and this technique provides high prediction accuracy. From the models constructed by WEKA, we find that the important variables that affect the education accomplishment are the previous GPA., mother and or fathers career, the total income of the family and the grade point average when they enter the first year in bachelor study. The obtained result is consistent with the result analyzed by multiple regression analysis.

Testing Our Assumptions

2015 ◽  
Vol 19 (2) ◽  
pp. 161-175 ◽  
Author(s):  
Janet Callahan ◽  
Marcia Belcheir
Keyword(s):  
Student Persistence ◽  
Quantitative Research ◽  
First Year ◽  
Full Time ◽  
High Grade ◽  
Freshman English ◽  
Science Technology ◽  
First Time ◽  
Level Effect

Methods that provide an early indicator of factors that affect student persistence are important to colleges and universities. This quantitative research focused on the role of level of entry mathematics and English and also on grades earned in those classes, as they relate to persistence after 1 year. The research showed that by far, the variable most predictive of first-time, full-time students enrolling 1 year later was earning a grade of “A” in English. Compared with those who did not pass their first English course, students who earned an “A” were 3 times more likely to persist. The variables which at least doubled the likelihood of persistence included earning an “A” or a “B” in mathematics, a “B” in English, and taking an English course beyond freshman English. While course level taken was significant, the course level effect paled compared with grades earned as a predictor. This effect—of grade earned being more important than course level—included remedial coursework in mathematics and English. In addition, obtaining a high grade in English was equally important for both science, technology, engineering and math and non-science, technology, engineering and math majors. Finally, students who took both mathematics and English courses during their first year were more likely to persist than students who did not take both subjects.

Comparing Data Mining Models in Academic Analytics

2016 ◽  
Vol 6 (2) ◽  
pp. 38-54 ◽  
Author(s):  
Dheeraj Raju ◽  
Randall Schumacker
Keyword(s):  
Data Mining ◽  
Full Time ◽  
Students At Risk ◽  
Institutional Researchers ◽  
Forest Models ◽  
Random Forest Models ◽  
Student Graduation ◽  
Flagship University ◽  
Misclassification Rates ◽  
First Time

The goal of this research study was to compare data mining techniques in predicting student graduation. The data included demographics, high school, ACT profile, and college indicators from 1995-2005 for first-time, full-time freshman students with a six year graduation timeline for a flagship university in the south east United States. The results indicated no difference in misclassification rates between logistic regression, decision tree, neural network, and random forest models. The results from the study suggest that institutional researchers should build and compare different data mining models and choose the best one based on its advantages. The results can be used to predict students at risk and help these students graduate.

scholarly journals Predictive Decision Support System using Logistic Regression and Decision Tree Model Combination for Student Graduation Success Determination

2015 ◽  
Vol 2 ◽  
pp. 144-153
Author(s):  
Ace C. Lagman
Keyword(s):  
Data Mining ◽  
Logistic Regression ◽  
Decision Tree ◽  
Educational Data Mining ◽  
The Philippines ◽  
Full Time ◽  
Model Combination ◽  
Process Data ◽  
Data Set ◽  
Student Graduation

More recently, researchers and higher education institutions are also beginning to explore the potential of data mining in analyzing academic data. The goal of such an endeavor is to find means to improve the services that these institutions provide and to enhance instruction. This type of data mining application is more popularly known as educational data mining or EDM. At present, EDM is more particularly focused on developing tools that can be used to discover patterns in academic data. It is more concerned about exploring a huge amount of data in order to identify patterns about the microconcepts involved in learning. This area of EDM is often referred to as Learning Analytics – at least as it is commonly compared to more prominent data mining approaches that process data from large repository for better decision-making. One main topic under educational data mining is student graduation. In the Philippines According to the National Statistics Office, there is an imbalance between student enrolment and student graduation. Almost half of the first time freshmen full-time students who began seeking a bachelor’s degree do not graduate on time. This scenario indicates the need to conduct research in this area in order to build models that can help improve the situation. The study focused to extract hidden patterns from the data set using logistic regression and decision tree algorithms that can be used to predict too early identification of students who are vulnerable to not having graduation on time so proper retention policies and measures be implemented by the administration.

Postprandial Somnolence and its awareness among the Medical Undergraduate Students: A crosssectional study

2021 ◽  
Vol 20 (4) ◽  
pp. 826-832
Author(s):  
Abhishek Chaturvedi ◽  
Anitha Guru ◽  
Naveen Kumar ◽  
Ling Yi Lin ◽  
Daniel YeapTze Wei ◽  
...  
Keyword(s):  
Medical Students ◽  
Undergraduate Students ◽  
Pearson Correlation ◽  
Statistical Significance ◽  
Medical Science ◽  
First Year ◽  
Lunch Break ◽  
Medical Undergraduate ◽  
Students First

Introduction: Postprandial somnolence or commonly referred to as food coma is generally experienced after the ingestion of afternoon meals. The performance of an individual gets affected after the ingestion of a heavy meal and this is more pertinent in a college setup where students have to attend a lecture right after the meal. The objective of this study was to assess the awareness of medical students about the factors responsible for postprandial somnolence, to identify the methods used to counteract it and to ascertain lecturers’ perception on responsiveness and participation of the students in a post lunch lecture. Methods: Total 330 students (first year to third year MBBS students) aged between 18-21 years and 40 lecturers teaching first and second year MBBS students were involved in this study. Two separate questionnaires (Part A: students’ perception, and B: lecturers’ perception) were prepared, peer-reviewed, validated and administered to the respective participants. All the responses were compiled and expressed in frequency percentage. Statistical analysis was performed using Statistical Package for Social Sciences, version 15.0 for a level of statistical significance of 5%. Pearson correlation was used to get the association between the variables. Results: About 55.75% students were aware about the role of serotonin and melatonin in drowsiness but 45.75% students did not know that food rich in tryptophan relaxes the brain and results in sleepiness. Students agree that heaviness of their meal might cause drowsiness and indigestion or bloating, which can also result in lethargy and can affect their performance. Majority of the lecturers opined that students disturb the harmony of the class and are less responsive and participative in post lunch break lectures. Conclusion: Thus, the present study provided scope for conducting awareness talks regarding the strategies to counteract postprandial somnolence among medical students which can help improve their concentration during post-lunch lectures. Bangladesh Journal of Medical Science Vol.20(4) 2021 p.826-832

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