Empathy First

2021 ◽  
pp. 12-30
Author(s):  
Randi Korn
Keyword(s):  
Student Success ◽  
Student Retention ◽  
Design Thinking ◽  
Academic Support ◽  
Education Institution ◽  
Team Cohesion ◽  
Wicked Problem ◽  
First Year ◽  
Team Members ◽  
Support Unit

This chapter describes how the promotion of empathy in an academic support unit at a higher education institution enabled the design thinking process to generate programs designed around student retention. Empathy empowered staff to understand retention as a complex construct called a “wicked problem,” which is a deep, structural, and pervasive issue. Priority was placed on the development of empathy among the team members to build cohesion and trust and develop deeper understanding of the complexity within the student experience in the first year of university. The results of the process were improved team cohesion, stronger commitment to student success, and reorganization of the academic support unit.

scholarly journals Analysis of Tinto’s student integration theory in first-year undergraduate computing students of a UK higher education institution

2017 ◽  
Vol 19 (2/3) ◽  
pp. 97-121 ◽  
Author(s):  
Alexandros Chrysikos ◽  
Ejaz Ahmed ◽  
Rupert Ward
Keyword(s):  
Higher Education ◽  
Student Retention ◽  
Education Institution ◽  
Integration Theory ◽  
First Year ◽  
Higher Education Institution ◽  
Content Type ◽  
Student Integration ◽  
Study Results ◽  
Uk Higher Education

Purpose Retention is one of the key performance indicators in university quality assurance processes. The purpose of this paper is to identify the causes leading to low retention rates for first-year undergraduate computing students in a UK higher education institution (HEI). Design/methodology/approach The study applies Tinto’s student integration theory, and connects it with the behavioural patterns of students. Data were collected from 901 students using Pascarella and Terenzini’s questionnaire (integration scales). This data were combined with student enrolment information and analysed using the structural equation modelling technique. Findings The study results indicate that Tinto’s student integration theory is useful in analysing student retention, but this accounts for only a modest amount of variance in retention. Nevertheless, important relationships amongst student’s initial and later academic goals and commitments have been identified through this new approach to analysing retention. The largest direct effect on retention was accounted for by initial goals and institutional commitments, followed by later goals and institutional commitments. In addition, the results show that academic and social integration constructs can have an influence on the student retention processes. When all, or some, of these relationships are operating towards students’ benefits, appropriate services or programmes, such as student support systems, can have their maximum benefits. Originality/value The authors mapped behavioural-related retention factors using a learning community lens. The study explored students’ social and learning experiences within the context of a UK HEI by employing Tinto’s model. This is the first time the model has been tested in this context.

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 Nourishing STEM Student Success via a TEAM-Based Advisement Model

2017 ◽  
Vol 6 (6) ◽  
pp. 31 ◽  
Author(s):  
Bernard Polnariev ◽  
Reem Jaafar ◽  
Tonya Hendrix ◽  
Holly Porter Morgan ◽  
Praveen Khethavath ◽  
...  
Keyword(s):  
Community College ◽  
Student Success ◽  
Student Persistence ◽  
Public Institution ◽  
First Year ◽  
Promising Practices ◽  
Team Members ◽  
Staff Members ◽  
Supportive Culture ◽  
Stem Students

LaGuardia Community College is an international leader recognized for developing and successfully implementing initiatives and educating underserved diverse students. LaGuardia’s STEM students are holistically advised by a team of dedicated faculty and staff members from different departments and divisions. As an innovative approach to advisement, students are first connected to an advising team member in their discipline-based first-year seminar and consequently guided by other cross-institutional advisement team members to ensure their continued success. In this article, we share our policies, processes, and promising practices in advising STEM student at an urban public institution. We present arguments that address and support five pillars for student success: 1) the student matters, 2) supportive culture matters, 3) effective communication matters, 4) data matters, and, 5) clear pathways and effective advisement matters. Finally, we present empirical evidence that show positive results in terms of students’ retention. Specifically, there was an improvement in the actual Fall 2015 to 2016 return rate of STEM students, from 62.9% to 64.6%. Our scaled practice demonstrates the value of collaborative team-based advisement efforts as supported through professional development can improve community college STEM student persistence when the above five pillars are fully espoused by the institution. 

scholarly journals Heads Or Tails (Success Or Failure)? Using Logit Modeling To Predict Student Retention And Progression

2010 ◽  
Vol 3 (5) ◽  
pp. 35 ◽  
Author(s):  
Michael C. Budden ◽  
Yu Hsing ◽  
Connie B. Budden ◽  
Michelle Hall
Keyword(s):  
High School ◽  
University Students ◽  
Student Success ◽  
Student Retention ◽  
Logit Model ◽  
Dominant Factor ◽  
First Year ◽  
Explanatory Variables ◽  
Core Requirements ◽  
Logit Modeling

Using a sample of 2,137 university students and applying the logit model, we find that the probability for students to return in fall 2008 is higher with a higher cumulative GPA, a higher grade for SE 101, and a returning status in the previous semester.  Several other explanatory variables are tested and have insignificant coefficients. A few variables such as the Board of Regent’s core requirements (CORE) and high school graduating GPA (HSGPA) have the expected signs and z-statistics closer to one, suggesting that the correlation coefficient may rise if the sample size were larger.  The findings suggest that the cumulative GPA is a dominant factor and that the large number of failures in SE 101 may need to be examined in order to fulfill its described purpose: “a course designed to ensure first-year student success.”

What do first-year students need? Digital badges for academic support to enhance student retention

2019 ◽  
Vol 12 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Dana-Kristin Mah ◽  
Dirk Ifenthaler
Keyword(s):  
Higher Education ◽  
Student Retention ◽  
Support Services ◽  
Academic Support ◽  
Institutional Support ◽  
First Year ◽  
Digital Badges ◽  
Academic Support Services ◽  
First Year Students ◽  
Content Type

Purpose The purpose of this paper is to analyse data on first-year students’ needs regarding academic support services and reasons for their intention to leave the institution prior to degree completion. On the basis of the findings, a digital badge outline is proposed which could contribute to improved communication of academic requirements in order to help students to better adapt to higher education demands. Digital badges might also serve as an indicator for students’ needing additional academic support services. Design/methodology/approach An online-questionnaire was conducted with 730 first-year students at a German university. Participants’ responses to open-ended questions were coded and categorised. On the basis on these findings, an outline for a digital badge programme is proposed. Findings Participants seek the most institutional support regarding research skills and organisational aspects. Main reasons for participants’ intention to withdraw from the institution include difficulties with their programme choice. Practical implications These findings may enable higher education institutions to provide targeted support services that meet first-year students’ needs. On the basis of the findings, higher education institutions can create digital badge programmes, which may improve communication of academic requirements and may also serve as a platform for a staff-student conversation about expectations and demands for a successful first-year experience. Besides, further research and discussion may address using digital badges for learning analytics algorithms to even better identify students’ strengths and needs for targeted academic support services and enhanced student success in higher education. Originality/value Little is known about first-year students’ needs for institutional support and reasons for thinking about dropout in Germany. Understanding the student perspective is crucial for enhancing student retention. Digital badges are an emerging educational technology in higher education and they have the potential to target academic requirements, which may guide first-year students and help them to better adjust to universities’ demands.

scholarly journals VALIDATING Pathways to Student Success and Retention

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Neva Lozada ◽  
Paul Croft
Keyword(s):  
Student Success ◽  
Student Retention ◽  
Return On Investment ◽  
College Preparation ◽  
Public University ◽  
Support Programs ◽  
First Year ◽  
Retention Model ◽  
First Year Students ◽  
Student College

Retention interventions focus on support programs, measures of success, and demographics. This study offers an alternate student-based narrative relative to student college preparation and self-diagnosed functionalities and compatibilities. We examine metrics that portray economic and environmental settings and social frameworks – as well as interactions, behaviors, and responses – that comprise first-year students’ experiences for the spectrum of engagement behaviors. We analyze the return on investment for student retention efforts at a four-year public university by applying a predictive and proactive retention model. We also discuss ensuring accessibility to services, diagnosing student intervention, and considering subsequent engagement behaviors.

Design Thinking as a Tool for the Analysis of Project Risks

2019 ◽  
Vol 16 (6) ◽  
pp. 60-77
Author(s):  
E. V. Vasilieva ◽  
T. V. Gaibova
Keyword(s):  
Risk Management ◽  
Risk Analysis ◽  
Design Thinking ◽  
Management Tools ◽  
Project Risk ◽  
Team Members ◽  
Investment Projects ◽  
Risk Management Process ◽  
Project Objectives ◽  
Project Risk Analysis

This paper describes the method of project risk analysis based on design thinking and explores the possibility of its application for industrial investment projects. Traditional and suggested approaches to project risk management have been compared. Several risk analysis artifacts have been added to the standard list of artifacts. An iterative procedure for the formation of risk analysis artifacts has been developed, with the purpose of integrating the risk management process into strategic and prompt decision-making during project management. A list of tools at each stage of design thinking for risk management within the framework of real investment projects has been proposed. The suggested technology helps to determine project objectives and content and adapt them in regards to possible; as well as to implement measures aimed at reducing these risks, to increase productivity of the existing risk assessment and risk management tools, to organize effective cooperation between project team members, and to promote accumulation of knowledge about the project during its development and implementation.The authors declare no conflict of interest.

10. Irish Students Turning First-Year Transition Obstacles into Successful Progression

2020 ◽  
pp. 225-244
Author(s):  
Vera Sheridan
Keyword(s):  
Student Retention ◽  
First Year ◽  
Comprehensive Overview

Vera Sheridan provides a comprehensive overview of the literature on student retention and progression before discussing the narrative accounts of four Irish students who successfully continued their studies after failing in the first year, seeking to understand how this was achieved.

Data Analytics and STEM Student Success: The Impact of Predictive Analytics-Informed Academic Advising Among Undeclared First-Year Engineering Students

2018 ◽  
Vol 22 (3) ◽  
pp. 497-521 ◽  
Author(s):  
Yu (April) Chen ◽  
Sylvester Upah
Keyword(s):  
Higher Education ◽  
Student Success ◽  
Academic Advising ◽  
Data Analytics ◽  
Predictive Analytics ◽  
First Year ◽  
Program Of Study ◽  
Science Technology ◽  
Mathematics Student ◽  
And Mathematics

Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success. This study took the first step to investigate the influence of using predictive analytics on academic advising in engineering majors. Specifically, we examined the effects of predictive analytics-informed academic advising among undeclared first-year engineering student with regard to changing a major and selecting a program of study. We utilized the propensity score matching technique to compare students who received predictive analytics-informed advising with those who did not. Results indicated that students who received predictive analytics-informed advising were more likely to change a major than their counterparts. No significant effects was detected regarding selecting a program of study. Implications of the findings for policy, practice, and future research were discussed.

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