Data Analytics and Visualization to Support the Adult Learner in Higher Education

2020 ◽  
Author(s):  
Sylvia Chong ◽  
Yew Haur Lee ◽  
Yoke Wah Tang
Keyword(s):  
Higher Education ◽  
Data Analytics ◽  
Adult Learner

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.

Adult Learners

2019 ◽  
pp. 114-132
Author(s):  
David Deggs
Keyword(s):  
Higher Education ◽  
Adult Learners ◽  
Real World ◽  
Adult Learner ◽  
Student Activism ◽  
Upper Class ◽  
Traditional Student ◽  
Societal Issues ◽  
American Higher Education ◽  
The 1960S

Student activism is mostly thought of as an activity that engages and motivates the traditional-aged students in American higher education to action. The emergence of student activism in the 1960s occurred when enrollment in American higher education was still primarily limited to youth from middle- and upper-class families. The demographics of American higher education have shifted, and the adult learner or non-traditional student now represents a significant amount, if not the majority, of most campus populations. The adult learner brings unique perspective to the higher education classroom based upon their real-world experiences that directly impacts their values, beliefs, and ideas about societal issues. Adult learners in American higher education have the potential to change the ways, means, and longstanding outcomes related to activism in American higher education.

Introducing the Black Male Adult Learner Success Theory

2020 ◽  
pp. 074171362095960
Author(s):  
Ramon B. Goings
Keyword(s):  
Higher Education ◽  
Academic Success ◽  
Adult Learners ◽  
Black Male ◽  
Adult Learner ◽  
Theoretical Frameworks ◽  
Male Adult ◽  
Scholar Identity ◽  
The Impact ◽  
Learner Success

Given the preponderance of deficit-oriented discourse about Black men and adult learners in higher education, there have not been theoretical frameworks put forth to explain the success of Black male adult learners in higher education. Thus, this article describes the Black male adult learner success theory, which builds on Gilman Whiting’s scholar identity model and Urie Bronfenbrenner’s bioecological system theory and was developed as a lens to examine the unique experiences of Black male adult learners in higher education and the impact of their various environments on their academic success. In response to the call for action from adult education scholars, this article introduces an asset-based theoretical approach for researchers to use when studying Black male adult learners. The article provides implications for using the Black male adult learner success theory for policy and practitioners. The article ends with providing future recommendations for research based on the theory.

THE IMPACT OF ADMINISTRATIVE SUPPORT AND INSTITUTIONAL TYPE ON ADULT LEARNER SERVICES

1988 ◽  
Vol 8 (2) ◽  
pp. 7-16 ◽  
Author(s):  
Cheryl J. Polson ◽  
Jan P. Eriksen
Keyword(s):  
Higher Education ◽  
Adult Learners ◽  
Administrative Support ◽  
Adult Learner ◽  
Student Population ◽  
Academic Advisors ◽  
Institutional Type ◽  
Education Today ◽  
The Impact

The study described examined the scope of existing services for adult learners enrolled in higher education today. Two analyses were performed, one to review the effect of institutional type on services provided and the second to understand the impact of perceived administrative support on efforts to serve this student population. The implications of the findings for academic advisors are addressed.

scholarly journals Decision support model to adopt big data analytics in higher education systems

2021 ◽  
Vol 8 (6) ◽  
pp. 67-78
Author(s):  
Adel Alkhalil ◽  
Keyword(s):  
Higher Education ◽  
Big Data ◽  
Data Analytics ◽  
Structural Equation ◽  
Data Science ◽  
Big Data Analytics ◽  
Equation Modeling ◽  
Systematic Research ◽  
Education Systems ◽  
Higher Education Systems

Data science or specifically data analytics systems have become an emerging trend in information technology and have attracted many organizations, including higher education. Higher Education Systems (HES) involve very active entities (students, faculty members, researchers, employers) who generate and require large volumes of data that go beyond the structured data stored in the house. The collection, analysis, and visualization of such big data present a huge challenge for HES. Big data analysis could be the solution to this challenge. However, the rationale and decision process for the adoption of big data analytics can be difficult. Such a knowledge-driven process requires a multitude of technical and organizational aspects that must be accounted for to ensure informed decisions are made. Existing research and development indicates that the decision to adopt, although systematic research with a theoretical background is rare and none of the existing studies have considered diffusion of innovation (DOI) theory. This paper aims to support HES, by providing a systematic analysis of the determinants for the decision to adopt big data analytics. An integrated framework referred to as the Technology Organization Environment (TOE) framework is proposed. The proposed framework is validated using structural equation modeling. Eleven determinants are confirmed that influence the TOE-driven framework for data analytics in HES. The result is expected to contribute to on-going research that attempts to address the complex and multidimensional challenge that relates to data science and analytics implementation in HES.

scholarly journals A case study of an adult learner with ASD and ADHD in an undergraduate online learning environment

2015 ◽  
Vol 31 (2) ◽  
Author(s):  
Christopher A Meyers ◽  
Richard G Bagnall
Keyword(s):  
Higher Education ◽  
Online Learning ◽  
Learning Environment ◽  
Older Workers ◽  
Adult Learner ◽  
Autism Spectrum ◽  
Online Learning Environment ◽  
Learning Needs ◽  
Hyperactivity Disorder

<p>The contemporary need for older workers to participate in education and training programs to increase their employability has exposed many of them to learning opportunities involving online learning in higher education. This paper reports research into the issues and experiences of an adult learner with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) engaging in undergraduate online learning. The issues and experiences were identified through the use of inductive, in-depth interpretive phenomenological analysis (IPA), as part of a larger study. The profile of the target student was very different from the other nine participants in the study, and was interpreted as principally related to disorientation within his online learning environment. Three types of disorientation were identified – navigational, contextual, and procedural – each of which presented strategies for its mitigation. The research revealed a significant disjunction between the characteristics of the learner’s online learning environment and his learning needs and preferences, which has implications for the design and development of inclusive online learning environments in higher education.</p>

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