Based on a mix of real world data and a simulated dataset for predicting the students’ academic performance, we study/compare various decision tree (DT) based algorithms (which include ID3, C4.5 and CART) with different choices of information entropy metrics (which include Shannon, Quadratic, Havrda and Charvát, Rényi, Taneja, Trigonometric and R-norm entropies) to build a decision tree in order to provide appropriate counseling/advise at an earlier stage. DT is one such important technique in educational data mining (EDM) which creates hierarchical structures of classification rules “If ⋯, Then ⋯” building a tree structure by incrementally breaking down the datasets in smaller subsets. The results suggest that basic training of the students has no significant predictive power on performance, while information about their abilities, diligence, motivation and activity in the learning process can predict their grades. As such, the resulting forecasts can be used by the instructor in optimizing the learning process and designing the course content and schedule.
<p>The classification of vertical displacements and the estimation of a local geometric geoid model and coordinate transformation were recently solved by the L<sup>2</sup> support vector machine and support vector regression. The L<sup>p</sup> quasi-norm SVM and SVR (0<p<1) is a non-convex and non-Lipschitz optimization problem that has been successfully formulated as an optimization model with a linear objective function and smooth constraints (LOSC) that can be solved by any black-box computing software, e.g., MATLAB, R and Python. The aim of this talk is to show that interior-point based algorithms, when applied correctly, can be effective for handling different LOSC-SVM and LOSC-SVR based models with different p values, in order to obtain better sparsity regularization and feature selection. As a comparative study, some artificial and real-life geoscience datasets are used to test the effectiveness of our proposed methods. Most importantly, the methods presented here can be used in geodetic classroom teaching to benefit our undergraduate students and further bridge the gap between the applications of geomatics and machine learning.</p>
ABSTRACT: INTRODUCTION: Both benefits and challenges are associated with training medical students in a community-based setting at a Regional Medical Campus (RMC). At the RMC, close relationships between learner and teaching faculty can truly be fostered. However, those volunteer teaching faculty are frequently conflicted due to time-constraints and practice productivity requirements that may run counter to maximizing learner involvement. Longitudinal integrated clerkships (LICs) have been studied and promoted as clinical clerkship structures that, through taking full advantage of the on-going relationship between learner, teacher, patients, and practices, optimize the learning environment for medical students on clinical rotations. In our resource-limited environment, we wished to create longitudinal educational relationships for all UPRC students with preceptors, practices and patients that would achieve the educational benefits of a true LIC yet not overwhelm the limited resources of this small community. METHODS: We created an amalgamative LIC clerkship model that provided a year-long Family Medicine experience integrated within OB-GYN, Surgery and Pediatrics ½-year longitudinal clerkships and three 1-week inpatient adult medicine mini-immersions spaced over the course of ½-year. Neurology, Psychiatry and Underserved/Rural Medicine (4-weeks each) and subspecialty/elective rotations (2-weeks each) remained in traditional self-contained blocks interspersed within longitudinal experiences. At 6 and 12 months, we administered a 5-point Likert-type survey to both medical students and teaching faculty asking their perceptions of the educational value and resource requirements for our clinical rotation structure. Descriptive averages of the ordinal values were reported. RESULTS: There were 11/12 students (92.7%) and 11/21 faculty (52.4%) who responded to the survey. Both students and faculty believed that some of the longitudinal benefits of the amalgamative structure were achieved. The students especially noted that attending feedback was beneficial due to the longer interaction and that they had a greater ability to interact with patients. All told, the faculty teachers found the Amalgamative LIC to be slightly less satisfying than the students. CONCLUSIONS: While logistical limitations necessitated our unique rotation design, some optimization of education was achieved. Faculty concerns toward adopting this new structure should be considered for other programs structuring LICs in a similar sparsely resourced environment such as a Regional Medical Campus.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.