Acknowledgments vii Preface xiii Chapter 1. Educational Attainment: Overall Trends, Disparities, and the Public Universities We Study 1 Chapter 2. Bachelor's Degree Attainment on a National Level 20 Chapter 3. Finishing College at Public Universities 32 Chapter 4. Fields of Study, Time-to-Degree, and College Grades 57 Chapter 5. High Schools and "Undermatching" 87 Chapter 6. Test Scores and High School Grades as Predictors 112 Chapter 7. Transfer Students and the Path from Two-Year to Four-Year Colleges 134 Chapter 8. Financial Aid and Pricing on a National Level 149 Chapter 9. Financial Aid at Public Universities 166 Chapter 10. Institutional Selectivity and Institutional Effects 192 Chapter 11. Target Populations 207 Chapter 12. Looking Ahead 223 Appendix A. The Modern Evolution of America's Flagship Universities by Eugene M. Tobin 239 Notes 265 List of Figures 337 List of Tables 347 List of Appendix Tables 349 References 357 Index 377

Crossing the Finish Line:: Completing College at America’s Public Universities (Association for Institutional Research, May 30, 2010)

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Boosting: Foundations and Algorithms.

Mindset reading of a student towards technology is a challenging task. The student's demographic features prediction has a significant aspect for the learning activities in educational institutions. The current studies predicted the student's native place based on technological awareness having various features such as development, availability, usability, educational benefits, etc. However,these studies have not explored the identification of sentiment identification about the technology through ML,optimization,etc.Motivated from these facts,in this paper, we propose a machine learning (ML) model with optimizing techniques to tune the hyper-parameters. In the proposed model, a primary dataset gathered from Indian and Hungarian universities, which analyzed with a Multi-Layer-Perceptron (MLP) with three popular optimization algorithms, such as Adaptive Moment Estimation (Adam), Stochastic Gradient Descent (SGD), Limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS). The optimized MLP has compared with the Support Vector Machine (SVM). Besides, numerous testing methods and to select the most prominent features, Principal Component Analysis (PCA) trained both models. Association of the Adam optimizer with the ReLu activation function in the MLP proved significant play in prediction with regularization. The PCA components covering most of the variance improved the optimized MLP accuracy with 2.3% and boosted the accuracy of the SVM with 2.9%. The Gain-ratio and the Info-gain suggested 11 features with significant weights. Both predictive models are found not only competitive but also outperformed with an identical prediction accuracy of 94% to identify the native place of the student. The Statistical t-test supported the equal predictive strength of both models and proved the significant enhancement in the SVM performance using the PCA components. Further, a considerable reduction is also achieved in the prediction error and prediction time to support the institute's web-based real-time system. Based on deep experiments, we recommend the optimistic native identification models for the higher educational institutions to analyze the attitude and technical awareness among students based on their native place.

/pdf/machine-learning-based-student-s-native-place-identification-4m15ec5jcx.pdf

Machine Learning-Based Student’s Native Place Identification for Real-Time

The recent diffusion of online education (both MOOCs and e-courses) has led to an increased economic and scientific interest in e-learning environments. As widely documented, online students have a much higher chance of dropping out than those attending conventional classrooms. It is of paramount interest for institutions, students, and faculty members to find more efficient methodologies to mitigate withdrawals. Following the rise of attention on the Student Dropout Prediction (SDP) problem, the literature has witnessed a significant increase in contributions to this subject. In this survey, we present an in-depth analysis of the state-of-the-art literature in the field of SDP, under the central perspective, but not exclusive, of machine learning predictive algorithms. Our main contributions are the following: (i) we propose a comprehensive hierarchical classification of existing literature that follows the workflow of design choices in the SDP; (ii) to facilitate the comparative analysis, we introduce a formal notation to describe in a uniform way the alternative dropout models investigated by the researchers in the field; (iii) we analyse some other relevant aspects to which the literature has given less attention, such as evaluation metrics, gathered data, and privacy concerns; (iv) we pay specific attention to deep sequential machine learning methods—recently proposed by some contributors—which represent one of the most effective solutions in this area. Overall, our survey provides novice readers who address these topics with practical guidance on design choices, as well as directs researchers to the most promising approaches, highlighting current limitations and open challenges in the field.

A Survey of Machine Learning Approaches for Student Dropout Prediction in Online Courses

Early identification of college dropouts can provide tremendous value for improving student success and institutional effectiveness, and predictive analytics are increasingly used for this purpose. However, ethical concerns have emerged about whether including protected attributes in these prediction models discriminates against underrepresented student groups and exacerbates existing inequities. We examine this issue in the context of a large U.S. research university with both residential and fully online degree-seeking students. Based on comprehensive institutional records for the entire student population across multiple years (N = 93,457), we build machine learning models to predict student dropout after one academic year of study and compare the overall performance and fairness of model predictions with or without four protected attributes (gender, URM, first-generation student, and high financial need). We find that including protected attributes does not impact the overall prediction performance and it only marginally improves the algorithmic fairness of predictions. These findings suggest that including protected attributes is preferable. We offer guidance on how to evaluate the impact of including protected attributes in a local context, where institutional stakeholders seek to leverage predictive analytics to support student success.

https://dl.acm.org/doi/pdf/10.1145/3430895.3460139

Should College Dropout Prediction Models Include Protected Attributes

To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and a private university to predict student success as a basis for a targeted intervention. The EDS uses regression analysis, neural networks, decision trees, and the AdaBoost algorithm to identify student characteristics which distinguish potential dropouts from graduates. Prediction accuracy at the end of the first semester is 79% for the state university and 85% for the private university of applied sciences. After the fourth semester, the accuracy improves to 90% for the state university and 95% for the private university of applied sciences.

Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods

To successfully reduce student attrition, it is imperative to understand which students are at risk of dropping
out. We develop an early detection system (EDS) to predict student success in tertiary education as a basis
for a targeted intervention. The EDS uses regression analysis, neural networks, decision trees and the
AdaBoost algorithm to identify student characteristics which distinguish potential dropouts from graduates.
The developed method can be implemented in every German university, as it uses student performance and
demographic data collected and maintained by legal mandate. Therefore the EDS self-adjusts to the university
where it is employed. The EDS is tested and applied on a state university and a private university of applied
sciences. Both institutes of higher education differ considerably in their organization, tuition fees and studentteacher
ratios. Our results indicate a prediction accuracy at the end of the first semester of 79% for the state
university and 85% for the private university of applied sciences. After the fourth semester, the accuracy
improves to 90% for the state university and 95% for the private university of applied sciences.

 Dateigrose: 1,52 MB

https://www.econstor.eu/bitstream/10419/181293/1/sdp18006.pdf

Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods

To successfully reduce student attrition, it is imperative to understand what the underlying determinants of attrition are and which students are at risk of dropping out. We develop an early detection system (EDS) using administrative student data from a state and private university to predict student dropout as a basis for a targeted intervention. To create an EDS that can be used in any German university, we use the AdaBoost Algorithm to combine regression analysis, neural networks, and decision trees - instead of relying on only one specific method. Prediction accuracy at the end of the first semester is 79% for the state university and 85% for the private university of applied sciences. After the fourth semester, the accuracy improves to 90% for the state university and 95% for the private university of applied sciences.

/pdf/early-detection-of-students-at-risk-predicting-student-1nz2xa3bem.pdf

Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods

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Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods

Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods

Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods

Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data from German Universities and Machine Learning Methods