A Mathematics Pipeline to Student Success in Data Analytics through Course-Based Undergraduate Research

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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Data for Students: The Potential of Data and Analytics for Student Success

10.3768/rtipress.2020.rb.0023.2003 ◽

2020 ◽

Author(s):

Katherine Leu

Keyword(s):

Student Success ◽

Financial Aid ◽

Postsecondary Education ◽

Data Analytics ◽

Data Science ◽

Postsecondary Institutions ◽

College Completion ◽

Ethical Challenges ◽

Education Data ◽

Data Linkages

Postsecondary education is awash in data. Postsecondary institutions track data on students’ demographics, academic performance, course-taking, and financial aid, and have put these data to use, applying data analytics and data science to issues in college completion. Meanwhile, an extensive amount of higher education data are being collected outside of institutions, opening possibilities for data linkages. Newer sources of postsecondary education data could provide an even richer view of student success and improve equity. To explore this potential, this brief describes existing applications of analytics to student success, presents a framework to structure understanding of postsecondary data topics, suggests potential extensions of these data to student success, and describes practical and ethical challenges.

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QEP is HIP: A Case Study Implementing an Institution-wide Undergraduate Research Community of Practice for a Small, Private College Setting

Journal of the Scholarship of Teaching and Learning ◽

10.14434/josotl.v21i1.30359 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Melissa Tingle ◽

Julia Schmitz ◽

Perry Rettig

Keyword(s):

Student Success ◽

Professional Socialization ◽

Underserved Populations ◽

Undergraduate Research ◽

Retention Rates ◽

Grade Point ◽

Quality Enhancement Plan ◽

Faculty Mentorship ◽

College Setting

Piedmont College’s quality enhancement plan (QEP) emphasizes a developmental and progressive integration of high-impact practices (HIPs) into the academic and social fabric of the institution. The QEP is HIP initiative provides students with multiple opportunities to deepen learning and leadership skills, which leads to improvements in student success, persistence, and retention. However, the institution grappled with how to effectively engage students in effective, meaningful research-based experiences. During the 2nd year of its QEP implementation, a campus-wide undergraduate research symposium was launched to showcase students’ research and creative inquiry in an effort to (a) gain full institutional participation in this crucial HIP and (b) offer the underserved student population (defined as ethnic minority, Pell-eligible, and first-generation students) an opportunity to participate in professional socialization and experience faculty mentorship. This case study shows the initial influences of this HIP on student success (in terms of grade point average [GPA]), students’ perceptions of their own learning, students’ persistence (measured with the Grit Scale), and retention from the 2018–2019 to the 2019–2020 academic year. Specifically, this study compared students who presented their research at the undergraduate research symposium to students who did not. While the immediate influence of this HIP on student persistence/perseverance (grit scores) remains undetermined, the retention rates and GPA appear to have been higher for students who presented, in both the dominant and underserved populations. Furthermore, students reported an increase in perceptions of their own learning. These findings are significant and affirm that undergraduate research communities can be considered a HIP for students, including those of underserved populations.

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Using Prescriptive Data Analytics to Reduce Grading Bias and Foster Student Success

2019 IEEE Frontiers in Education Conference (FIE) ◽

10.1109/fie43999.2019.9028663 ◽

2019 ◽

Author(s):

Ashwin Satyanarayana ◽

Reneta Lansiquot ◽

Christine Rosalia

Keyword(s):

Student Success ◽

Data Analytics

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How Well Do Undergraduate Research Programs Promote Engagement and Success of Students?

CBE—Life Sciences Education ◽

10.1187/cbe.10-10-0130 ◽

2011 ◽

Vol 10 (2) ◽

pp. 156-163 ◽

Cited By ~ 34

Author(s):

Marcus Fechheimer ◽

Karen Webber ◽

Pamela B. Kleiber

Keyword(s):

Student Success ◽

Undergraduate Research ◽

Operational Definition ◽

Potential Contribution ◽

Research Experience ◽

Grade Point ◽

University Of Georgia ◽

Institutional Data ◽

The University ◽

The Impact

Assessment of undergraduate research (UR) programs using participant surveys has produced a wealth of information about design, implementation, and perceived benefits of UR programs. However, measurement of student participation university wide, and the potential contribution of research experience to student success, also require the study of extrinsic measures. In this essay, institutional data on student credit-hour generation and grade point average (GPA) from the University of Georgia are used to approach these questions. Institutional data provide a measure of annual enrollment in UR classes in diverse disciplines. This operational definition allows accurate and retrospective analysis, but does not measure all modes of engagement in UR. Cumulative GPA is proposed as a quantitative extrinsic measure of student success. Initial results show that extended participation in research for more than a single semester is correlated with an increase in GPA, even after using SAT to control for the initial ability level of the students. While the authors acknowledge that correlation does not prove causality, continued efforts to measure the impact of UR programs on student outcomes using GPA or an alternate extrinsic measure is needed for development of evidence-based programmatic recommendations.

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Impacting Big Data analytics in higher education through Six Sigma techniques

International Journal of Productivity and Performance Management ◽

10.1108/ijppm-09-2016-0194 ◽

2017 ◽

Vol 66 (5) ◽

pp. 662-679 ◽

Cited By ~ 8

Author(s):

Chad Laux ◽

Na Li ◽

Corey Seliger ◽

John Springer

Keyword(s):

Higher Education ◽

Big Data ◽

Student Success ◽

Research Design ◽

Six Sigma ◽

Data Analytics ◽

Design Methodology ◽

Big Data Analytics ◽

Content Type ◽

Conceptual Research

Purpose The purpose of this paper is to develop a framework for utilizing Six Sigma (SS) principles and Big Data analytics at a US public university for the improvement of student success. This research utilizes findings from the Gallup index to identify performance factors of higher education. The goal is to offer a reimagined SS DMAIC methodology that incorporates Big Data principles. Design/methodology/approach The authors utilize a conceptual research design methodology based upon theory building consisting of discovery, description, explanation of the disciplines of SS and Big Data. Findings The authors have found that the interdisciplinary approach to SS and Big Data may be grounded in a framework that reimagines the define, measure, analyze, improve and control (DMAIC) methodology that incorporates Big Data principles. The authors offer propositions of SS DMAIC to be theory tested in subsequent study and offer the practitioner managing the performance of higher education institutions (HEIs) indicators and examples for managing the student success mission of the organization. Research limitations/implications The study is limited to conceptual research design with regard to the SS and Big Data interdisciplinary research. For performance management, this study is limited to HEIs and non-FERPA student data. Implications of this study include a detailed framework for conducting SS Big Data projects. Practical implications Devising a more effective management approach for higher education needs to be based upon student success and performance indicators that accurately measure and support the higher education mission. A proactive approach should utilize the data rich environment being generated. The individual that is most successful in engaging and managing this effort will have the knowledge and skills that are found in both SS and Big Data. Social implications HEIs have historically been significant contributors to the development of meritocracy in democratic societies. Due to a variety of factors, HEIs, especially publicly funded institutions, have been under stress due to a reduction of public funding, resulting in more limited access to the public in which they serve. Originality/value This paper examines Big Data and SS in interdisciplinary effort, an important contribution to SS but lacking a conceptual foundation in the literature. Higher education, as an industry, lacks penetration and adoption of continuous improvement efforts, despite being under tremendous cost pressures and ripe for disruption.

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