Unlocking Student Success: A Comparative Analysis of Business Intelligence and Analytics in Higher Education Institutions
Abstract views: 21
/ 
PDF downloads: 34
Keywords:
Business Intelligence, Analytics in Higher Education, Student Success, Comparative Analysis, Institutional EffectivenessAbstract
Business intelligence (BI) and analytics have emerged as critical tools for enhancing student
success and improving institutional effectiveness in higher education. This study presents a comparative
analysis of BI and analytics deployment and perceptions across different types of higher education
institutions, including publicly funded universities and private universities. In the study, data from the
Educause Core Data Services (CDS) questionnaire are utilized, supplemented by data from the National
Center for Education Statistics (IPEDS). The findings reveal significant variations in BI deployment and
perceptions among institutions, with private universities demonstrating higher levels of institution-wide
deployment compared to public universities. Despite widespread recognition of the importance of analytics
for strategic planning and decision-making, funding constraints and faculty acceptance emerge as key
challenges hindering analytics maturity. However, there is growing momentum toward leveraging BI and
analytics to drive transformative change in higher education, with strong leadership commitment observed
for developing institutional effectiveness through analytics. The study underscores the need for ongoing
research and collaboration to address funding constraints, improve faculty engagement, and foster a culture
of data-driven decision-making across the sector. By overcoming these challenges and seizing opportunities
for innovation, higher education institutions can harness the power of BI and analytics to improve student
success outcomes, enhance operational efficiency, and drive institutional excellence in the 21st century
academic landscape.
Downloads
References
E. Alyahyan and D. Düştegör, “Predicting academic success in higher education: literature review and best practices,” Int. J. Educ. Technol. High. Educ., vol. 17, no. 1, p. 3, 2020.
W. Greller and H. Drachsler, “Translating learning into numbers: A generic framework for learning analytics,” J. Educ. Technol. Soc., vol. 15, no. 3, pp. 42–57, 2012.
K. E. Arnold and M. D. Pistilli, “Course signals at Purdue: Using learning analytics to increase student success,” in Proceedings of the 2nd international conference on learning analytics and knowledge, 2012, pp. 267–270.
C. D. Jerald, “Identifying Potential Dropouts: Key Lessons for Building an Early Warning Data System. A Dual Agenda of High Standards and High Graduation Rates.,” Achieve Inc, 2006.
L. Pinkus, “Using early-warning data to improve graduation rates: Closing cracks in the education system,” Wash. DC Alliance Excell. Educ., 2008.
J. P. Campbell, P. B. DeBlois, and D. G. Oblinger, “Academic analytics: A new tool for a new era,” Educ. Rev., vol. 42, no. 4, p. 40, 2007.
P. Prinsloo and S. Slade, “Big data, higher education and learning analytics: Beyond justice, towards an ethics of care,” Big Data Learn. Anal. High. Educ. Curr. Theory Pract., pp. 109–124, 2017.
J. Salminen, W. Froneman, S. Jung, S. Chowdhury, and B. J. Jansen, “The ethics of data-driven personas,” in Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, 2020, pp. 1–9.
R. Chennells et al., “Ethics Dumping. Case Studies from North-South Research Collaborations,” Int. Genomic Res. Involv. San People Springer Int. Publ. EBook, pp. 15–22, 2018.
S. Mason, Turning data into knowledge: Lessons from six Milwaukee public schools. Madison, WI: Wisconsin Center for Education Research, 2002.
T. Mason, “Taking Stock: Academic Advising Leaders’ Perspectives on Predictive Analytics and Advising Underrepresented Minority Students,” PhD Thesis, University of Kansas, 2023.
G. Siemens and R. S. Baker, “Learning analytics and educational data mining: towards communication and collaboration,” in Proceedings of the 2nd international conference on learning analytics and knowledge, ACM, 2012, pp. 252–254.
G. D. Kuh et al., Using evidence of student learning to improve higher education. John Wiley & Sons, 2015.
E. B. Mandinach, “A perfect time for data use: Using data-driven decision making to inform practice,” Educ. Psychol., vol. 47, no. 2, pp. 71–85, 2012.
A. Van Barneveld, K. E. Arnold, and J. P. Campbell, “Analytics in higher education: Establishing a common language,” Educ. Learn. Initiat., vol. 1, no. 1, p. l–ll, 2012.
B. Abazi Chaushi, A. Chaushi, and Z. Dika, “Making impact with data in a University setting – the case of SEE-University,” in Proceedings of 12th International Conference on Informatics and Information Technologies, Bitola, Macedonia: Faculty of Computer Science and Engineering, UKIM, Apr. 2015, pp. 135–141.
J. Bichsel, Analytics in higher education: Benefits, barriers, progress, and recommendations. EDUCAUSE Center for Applied Research, 2012.
B. Abazi Chaushi, Dika, Zamir, A. Chaushi, and S. Ibraimi, “Information technology funding in Public and Private Universities: A comparative analysis and case study approach,” in Proceedings of the 3 International Scientific Conferece on Business and Economics, Skopje/Tetovo, R.N.Macedonia: South East European University, Faculty of Business and Economics, Jun. 2019.
S. Grajek, “Top 10 IT Issues, 2016: Divest, Reinvest, and Differentiate.” Accessed: Nov. 28, 2016. [Online]. Available: http://er.educause.edu/articles/2016/1/top-10-it-issues-2016
