Examples of Academic Support Systems to Improve Student Success

2006 ◽  
Author(s):  
Amir Karimi
Keyword(s):  
Student Success ◽  
Student Retention ◽  
Support Systems ◽  
San Antonio ◽  
Academic Support ◽  
Student Attrition ◽  
University Of Texas ◽  
Student Demographics ◽  
Underlying Causes ◽  
The University

The University of Texas at San Antonio (UTSA) has implemented a number of academic support systems to address obstacles to student success and to improve student retention. This paper describes the student demographics at UTSA, provides tracking data on student enrollment and retention, and includes discussion of the underlying causes of student attrition. It will describe some of the programs that are implemented to improve student success. Data is provided to measure the level of success of some of the programs that have implemented for the student success.

scholarly journals Engineering Student Retention and Attrition Literature Review

2015 ◽  
Author(s):  
Elizabeth A. Kuley ◽  
Sean Maw ◽  
Terry Fonstad
Keyword(s):  
Literature Review ◽  
Student Success ◽  
Student Retention ◽  
Educational Experience ◽  
Student Characteristics ◽  
Student Attrition ◽  
Engineering Student ◽  
Retention And Attrition ◽  
The University ◽  
Engineering Schools

The University of Saskatchewan, similar tomany engineering colleges, would like to improve studentretention. With that in mind, a literature review wasundertaken to summarize current peer reviewed literaturerelated to engineering student retention and attrition inan attempt to better understand the potential structuralcauses, processes, and student characteristics that maycontribute to student success or attrition. Through asystematic search of several major databases using thekeywords “engineering and attrition or retention,” andafter narrowing the scope to peer reviewed articleswritten between 2005 and the present, each article’sabstract was read and evaluated. Forty-five papers weredeemed to be highly relevant, and were thus included inthe literature review. Preliminary trends that haveemerged in this review are: the potential causes of highattrition rates in engineering schools, various methodsthat have been used to determine the causes of attrition,interventions that have been implemented and stories oftheir success/failure, and attributes that have been foundto correlate with student attrition or success. This paperis an attempt to organize this body of research into asingular source that can be referenced by engineeringeducators or researchers who wish to increase studentretention and improve the educational experience of theirstudents.

Community Through Constructive Learning

2011 ◽  
pp. 447-459
Author(s):  
Patricia McGee ◽  
Misty Sailors ◽  
Lucretia Fraga
Keyword(s):  
Student Engagement ◽  
San Antonio ◽  
Faculty Engagement ◽  
Learning Approach ◽  
Community Based ◽  
Constructive Learning ◽  
University Of Texas ◽  
Initial Implementation ◽  
The University

This case study illustrates a community-based constructive learning approach to ePortfolio development, and the subsequent phenomena and outcomes that came from the initial implementation. The authors discuss why and how an ePortfolio system was chosen, as well as faculty engagement, student engagement, and recommendations to others based on the University of Texas at San Antonio experience.

Organocatalyzed C–C Ring Construction: The Bradshaw/Bonjoch Synthesis of (−)-Cermizine B

2017 ◽  
Author(s):  
Douglass F. Taber
Keyword(s):  
San Antonio ◽  
Bond Formation ◽  
Bryn Mawr ◽  
National Chemical Laboratory ◽  
Chemical Laboratory ◽  
Enantiomerically Pure ◽  
University Of Texas ◽  
National University ◽  
Institute Of Technology ◽  
The University

In a continuation of his studies (OHL20141229, OHL20140811) of organocatalyzed 2+2 photocycloaddition, Thorsten Bach of the Technische Universität München assembled (Angew. Chem. Int. Ed. 2014, 53, 7661) 3 by adding 2 to 1. Li-Xin Wang of the Chengdu Institute of Organic Chemistry also used (Org. Lett. 2014, 16, 6436) an organocatalyst to effect the addition of 5 to 4 to give 6. Shuichi Nakamura of the Nagoya Institute of Technology devised (Org. Lett. 2014, 16, 4452) an organocatalyst that mediated the enantioselective opening of the aziridine 7 to 8. Zhi Li of the National University of Singapore cloned (Chem. Commun. 2014, 50, 9729) an enzyme from Acinetobacter sp. RS1 that reduced 9 to 10. Gregory C. Fu of Caltech developed (Angew. Chem. Int. Ed. 2014, 53, 13183) a phosphine catalyst that directed the addition of 12 to 11 to give 13. Armido Studer of the Westfälische Wilhelms-Universität Münster showed (Angew. Chem. Int. Ed. 2014, 53, 9622) that 15 could be added to 14 to give 16 in high ee. Akkattu T. Biju of CSIR-National Chemical Laboratory described (Chem. Commun. 2014, 50, 14539) related results. The photostimulated enantioselective ketone alkylation developed (Chem. Sci. 2014, 5, 2438) by Paolo Melchiorre of ICIQ was powerful enough to enable the alkyl­ation of 17 with 18 to give 19, overcoming the stereoelectronic preference for axial bond formation. David W. Lupton of Monash University established (J. Am. Chem. Soc. 2014, 136, 14397) the organocatalyzed transformation of the dienyl ester 20 to 21. James McNulty of McMaster University added (Angew. Chem. Int. Ed. 2014, 53, 8450) azido acetone 23 to 22 to give 24 in high ee. There are sixteen enantiomerically-pure diastereomers of the product 27. John C.-G. Zhao of the University of Texas at San Antonio showed (Angew. Chem. Int. Ed. 2014, 53, 7619) that with the proper choice of organocatalyst, with or without subsequent epimerization, it was possible to selectively prepare any one of eight of those diastereomers by the addition of 26 to 25. William P. Malachowski of Bryn Mawr College showed (Tetrahedron Lett. 2014, 55, 4616) that 28, readily prepared by a Birch reduction protocol, was converted by heating followed by exposure to catalytic Me3P to the angularly-substituted octalone 29.

Prediction of student attrition risk using machine learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mauricio Barramuño ◽  
Claudia Meza-Narváez ◽  
Germán Gálvez-García
Keyword(s):  
Machine Learning ◽  
Student Retention ◽  
Quantitative Research ◽  
Student Attrition ◽  
Supervised Machine Learning ◽  
Computer Tool ◽  
Content Type ◽  
Academic Management ◽  
Qualitative Variables ◽  
The University

PurposeThe prediction of student attrition is critical to facilitate retention mechanisms. This study aims to focus on implementing a method to predict student attrition in the upper years of a physiotherapy program.Design/methodology/approachMachine learning is a computer tool that can recognize patterns and generate predictive models. Using a quantitative research methodology, a database of 336 university students in their upper-year courses was accessed. The participant's data were collected from the Financial Academic Management and Administration System and a platform of Universidad Autónoma de Chile. Five quantitative and 11 qualitative variables were chosen, associated with university student attrition. With this database, 23 classifiers were tested based on supervised machine learning.FindingsAbout 23.58% of males and 17.39% of females were among the attrition student group. The mean accuracy of the classifiers increased based on the number of variables used for the training. The best accuracy level was obtained using the “Subspace KNN” algorithm (86.3%). The classifier “RUSboosted trees” yielded the lowest number of false negatives and the higher sensitivity of the algorithms used (78%) as well as a specificity of 86%.Practical implicationsThis predictive method identifies attrition students in the university program and could be used to improve student retention in higher grades.Originality/valueThe study has developed a novel predictive model of student attrition from upper-year courses, useful for unbalanced databases with a lower number of attrition students.

Sign in / Sign up

Export Citation Format

Share Document