An Examination of Organization Learning and Data Driven Decision Making within Two University Enrollment Management Programs

2021 ◽  
Vol 8 (4) ◽  
pp. 150-168
Author(s):  
Barbara N Martin ◽  
J. D. Gragg
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Organizational Learning ◽  
Best Practices ◽  
Enrollment Management ◽  
Effective Means ◽  
Data Driven ◽  
Organization Learning ◽  
Data Driven Decision Making ◽  
Effective Strategies

This paper investigated the declining number of students in higher education institutions, and the effective strategies universities can use to recruit students who will graduate. From this research, it appeared that data-driven decision-making can be an effective means to create a successful enrollment management office. Through a practice of data-driven decision-making, organizational learning can happen, which will allow an organization to maintain success and build a culture that sustains that success. Three key themes emerged from the research that will help to inform best practices for other colleges and universities to implement ideas from this research. The three themes are: an organization must have good, usable data; an organization should strive to create a culture of teamwork to sustain success, and an organization needs a successful enrollment leader.

scholarly journals An examination of organizational learning and data driven decision making within two university enrollment management programs

2019 ◽  
Author(s):  
◽  
J. D. Gragg
Keyword(s):  
United States ◽  
Decision Making ◽  
Organizational Learning ◽  
Enrollment Management ◽  
Effective Means ◽  
The United States ◽  
Data Driven ◽  
United States Department ◽  
Data Driven Decision Making ◽  
Feasible Option

As the number of United States high school graduates continue to decline, research into the most effective ways to recruit and retain college students becomes increasingly valuable. According to the United States Department of Education (Hussar and Bailey, 2016), students graduating between now and 2028 will decrease by approximately 4.5 %. Historically, when focusing on enrollment numbers, college admission offices have recruited students in the door and then let other departments at the university worry about retaining them. In today's competitive market, this is no longer a feasible option. The focus of this research was to address the declining number of students, and the effective strategies universities can use to recruit students who will progress and graduate. Researchers Kretchmar and Memory (2010) and Seeman and O'Hara, (2006) concurred that to retain students is to attempt to understand students beyond the numbers, looking at students as more than a GPA or test score, and understand what the students are desiring in a college and be prepared to sell the student on that experience. From this research, it appeared that data-driven decision-making can be an effective means to create a successful enrollment management office. Through a practice of data-driven decision-making, organizational learning can happen, which will allow an organization to maintain success and build a culture that sustains that success. Three key themes emerged from the research that will help to inform best practices for other colleges and universities to implement ideas from this research. The three themes are: an organization must have good, usable data and the resources needed to analyze that data; an organization should strive to create a culture of openness, caring, communication, and teamwork to sustain success, and enrollment management is a mix of art and science; an organization needs a successful enrollment manager that is a person who can blend the use of data with creating a well-rounded, meaningful, and impactful student recruitment experience.

Lip Service or Actionable Insights? Linking Student Experiences to Institutional Assessment and Data-Driven Decision Making in Higher Education

2017 ◽  
Vol 88 (6) ◽  
pp. 835-862 ◽  
Author(s):  
Bradley E. Cox ◽  
Robert D. Reason ◽  
Barbara F. Tobolowsky ◽  
Rebecca L. Brower ◽  
Shawna Patterson ◽  
...  
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Data Driven ◽  
Student Experiences ◽  
Data Driven Decision Making ◽  
Institutional Assessment

Data-Driven Decision Making in Higher Education

2012 ◽  
Vol 7 (1) ◽  
pp. 78-97 ◽  
Author(s):  
Liz Hollingworth ◽  
Anne M. Sullivan ◽  
Chris Condon ◽  
Monica Bhatt ◽  
W. Christopher Brandt
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Data Driven ◽  
Data Driven Decision Making

scholarly journals Assuring Academic Integrity of Supervisor Appointment in Post-Graduate Program with Data-Driven Decision Making Strategy: A Proposal

2021 ◽  
Vol 5 (1) ◽  
pp. 141-154
Author(s):  
Aziman Abdullah ◽  
Asar A.K
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Academic Integrity ◽  
Graduate Program ◽  
Data Driven ◽  
Research Projects ◽  
Data Driven Decision Making ◽  
Post Graduate ◽  
Research Supervision ◽  
Research Profile

Research supervision is one of the important aspect in academic quality assurance and the sustainbility of the science itself. However, there is lack of attention based on research literature and evidence of good practice on research supervision from the context of academic integrity in higher education. This study aims to develop a data-driven decision making strategy in supervisor selection for post-graduate program based using research projects data. Apart of that, the researchers reviewed the indicator of academic integrity in research supervisory from program standards in masters and doctoral degree by Malaysia Qualification Agency (MQA), international recommendation by UNESCO and Islamic principles according to the roles of the supervisor, administrator and student in the context of research supervisory. This study adopted data analytics and visualization technique using cloud-based collaborative platform as a research method for data acqusition, processing and analyzing the data. The researchers acquired the research projects profile data registered in the institutional database in Universiti Malaysia Pahang from Department of Research and Innovation as a case study. We categorized and mapped the research profile according to Malaysian Research and Development Classification System (MRDCS) code. The combined data was been analyzed and visualized to specific online dashboard to indicate the research experience in fraction of years as a metric. The researchers evaluate the characteristics of the dashboard based on the academic integrity indicators from MQA, UNESCO and Islamic principles as our measures. The result shows that there is a potential usefulness of the proposed strategy in assuring academic integrity for supervisor selection in post-graduate programmes. This novel approach has a potential impact on academic integrity in higher education which can be adopted at larger scale by higher education institution in Malaysia.

The Evolution of Big Data and Learning Analytics in American Higher Education

2012 ◽  
Vol 16 (3) ◽  
Author(s):  
Anthony G Picciano
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Big Data ◽  
Learning Analytics ◽  
Data Driven ◽  
Higher Education Administrators ◽  
Data Driven Decision Making ◽  
American Higher Education ◽  
Education Administrators

Data-driven decision making, popularized in the 1980s and 1990s, is evolving into a vastly more sophisticated concept known as big data that relies on software approaches generally referred to as analytics. Big data and analytics for instructional applications are in their infancy and will take a few years to mature, although their presence is already being felt and should not be ignored. While big data and analytics are not panaceas for addressing all of the issues and decisions faced by higher education administrators, they can become part of the solutions integrated into administrative and instructional functions. The purpose of this article is to examine the evolving world of big data and analytics in American higher education. Specifically, it will look at the nature of these concepts, provide basic definitions, consider possible applications, and last but not least, identify concerns about their implementation and growth.

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