Explaining Student Retention: The Case of the University of Aberdeen

2004 ◽  
Author(s):  
W. David McCausland ◽  
Kostas G. G. Mavromaras ◽  
Ioannis Theodossiou
Keyword(s):  
Student Retention ◽  
The University

scholarly journals Using Data Mining for Improving Education of University Students – A Survey

2018 ◽  
Vol 6 (9) ◽  
pp. 78-83 ◽  
Author(s):  
K. P. S. D. Kumarapathirana
Keyword(s):  
Data Mining ◽  
Academic Success ◽  
Student Retention ◽  
Student Performance ◽  
Poor Performance ◽  
Academic Behavior ◽  
System Logs ◽  
Using Data ◽  
Visualization Techniques ◽  
The University

Data mining combines machine learning, statistical and visualization techniques to discover and extract knowledge. Student retention is an indicator of academic performance and enrolment management of the university. Poor student retention could reflect badly on the university. Universities are facing the immense and quick growth of the volume of educational data stored in different types of databases and system logs. Moreover, the academic success of students is another major issue for the management in all professional institutes. So the early prediction to improve the student performance through counseling and extra coaching will help the management to take timely action for decrease the percentage of poor performance by the students. Data mining can be used to find relationships and patterns that exist but are hidden among the vast amount of educational data. This survey conducts a literature survey to identify data mining technologies to monitor student, analyze student academic behavior and provide a basis for efficient intervention strategies. The results can be used to develop a decision support system and help the authorities to timely actions on weak students.

One size does not fit all: Tailoring Peer Support Programmes for optimal student engagement

2017 ◽  
Vol 3 (1) ◽  
pp. 274
Author(s):  
Rosey Davies ◽  
Amanda Pocklington ◽  
Simon Allington
Keyword(s):  
Student Engagement ◽  
Student Retention ◽  
Peer Support ◽  
Short Paper ◽  
Student Group ◽  
Local Contexts ◽  
Support Programmes ◽  
The University ◽  
Insight Into

Peer Support Programmes (PSPs) have long been a feature of student engagement activity at the University of Exeter.  Peer Support at Exeter started over eight years ago, with just two discipline-specific programmes. Since then, the number of programmes has increased significantly, but throughout this period of growth the bespoke nature of the programmes has been retained.  All PSPs are student-led and, crucially, they are tailored to meet the specific needs of the student group involved.  We, the authors, are firmly of the opinion that ‘one size does not fit all’ and that tailoring every programme to the specific needs of each student group is vital if it is to be successful.  It is now recognised that engagement in interventions such as PSPs can play an important role in improving student retention and success (Thomas, 2012) and that an understanding of ‘local contexts’ can enhance the efficacy of such programmes (Thomas et al, 2017).  However, this was not as well understood when PSPs were first introduced at Exeter. This short paper seeks to present an overview of the evolution of PSPs at the University of Exeter, which may provide others with a useful insight into the development of tailored PSPs for optimal student engagement.

scholarly journals Does Latinx Representation Matter for Latinx Student Retention in STEM?

2018 ◽  
Vol 19 (4) ◽  
pp. 437-451 ◽  
Author(s):  
Blanca E. Rincón
Keyword(s):  
Graduate Students ◽  
Student Retention ◽  
Science Technology ◽  
Level Data ◽  
Stem Students ◽  
And Mathematics ◽  
The University ◽  
The Relationship

Using student-level data, this study explores the relationship between Latinx student representation in science, technology, engineering and mathematics (STEM) and student retention. Results revealed that a 1% increase in cohort-level Latinx student representation in STEM subfields is associated with a decrease in student departures from the university, but not STEM. Furthermore, Latinx STEM students may be more responsive to changes in the representation of their cohorts compared with increases in diversity for upper-division undergraduate or graduate students.

scholarly journals ONLINE MENTORING FOR BIOTECHNOLOGY GRADUATE STUDENTS: AN INDUSTRY ACADEMIA PARTNERSHIP

2013 ◽  
Vol 17 (1) ◽  
Author(s):  
Rana Khan ◽  
Arhonda Gogos
Keyword(s):  
Student Retention ◽  
Student Satisfaction ◽  
Comparison Group ◽  
Mentoring Program ◽  
Online Mentoring ◽  
University Of Maryland ◽  
First Semester ◽  
Professional Science ◽  
The University ◽  
Masters Students

The Professional Science Master’s Biotechnology program at the University of Maryland University College implemented a novel online mentoring program that pairs masters’ students with mentors from the biotechnology industry. The program has grown from 19 mentor-mentee pairs in fall 2009 to 46 pairs in fall 2011. The student retention in the program is currently 80%. Data indicate that the students who continued participating after the first semester had completed more courses before joining the program than the students who dropped. The end-of semester questionnaires show general student satisfaction and provide specific examples of gains in the students’ ability to identify and pursue their career goals. The number of courses completed by the mentees was significantly higher than the number of courses completed by the comparison group, indicating a possible effect of their participation in the mentoring program. So far, more mentees graduated than the comparison group of non-participants, and in less overall time. As the program progresses, possible effects on students’ academic achievement and time-to-graduation will be reevaluated. The effectiveness of the mentoring program on improving the participants’ career prospects after graduation will also be examined.

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.

Recreate and Retain: How Entrance Into a Campus Recreation Facility Impacts Retention

2020 ◽  
Vol 44 (2) ◽  
pp. 89-98
Author(s):  
Paul R. Milton ◽  
Lisa M. Williamson ◽  
Kenneth Brubaker ◽  
Michael Papania
Keyword(s):  
Student Retention ◽  
Positive Relationship ◽  
Statistical Significance ◽  
Student Participation ◽  
Campus Recreation ◽  
Fall Semester ◽  
Number Of Visits ◽  
Recreation Programs ◽  
The University ◽  
Term Data

This study explored the rates of retention among students who entered a campus recreation facility. The purpose of the study was to determine the statistical significance of the rates of retention within several commonly studied demographic categories. Recent research in the field reported that student participation in campus recreation programs contributes to student retention. The researchers in this study also sought to determine whether there was a significant impact on campus recreation participants to return not only to the campus recreation center but to the university during the following fall term. Data were collected during a specific spring and subsequent fall semester to determine whether those who entered the recreation center during the spring enrolled in the fall term. Student entries were initially categorized by the number of visits 1–30, 31–60, 61–90, and 91–120 and were compared to students who did not enter. A χ2 analysis was conducted to compare the data collected from spring to fall terms and the results were separated by gender and overall participation. This suggests a positive relationship between entry into the recreation center and student retention from the spring to fall terms.

Retention Through Curriculum Development

2019 ◽  
pp. 128-148
Author(s):  
Charles B. W. Prince
Keyword(s):  
Literature Review ◽  
Student Retention ◽  
Curriculum Design ◽  
Student Experience ◽  
Document Analysis ◽  
Strategic Vision ◽  
Student Progression ◽  
University Curriculum ◽  
Retention And Graduation Rates ◽  
The University

Designing university curriculum is an important component to improve student retention and engagement. At the University of East London, the institution took to the curriculum as an opportunity to improve student progression and retention. In a time where UEL needed to make significant changes to the student experience, satisfaction, progression, retention, and graduation rates, bold steps were taken to ensure that their academic offering would support their strategic vision. The chapter provides the context of UEL's curriculum design journey and the results from those changes. A brief literature review is on the correlation between curriculum and retention. Next, the document analysis will show the experience of administrators during this process. The chapter closes by providing replicable and scalable opportunities that can be learned from their experience.

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.

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