Volume 1 Number 1 2023
Embracing Data-Driven Process Optimization to Highly Transform Instructional Administration in Higher Education Institutions
Nafiz Imtiaz1*, Md Zillul Karim2
Business and Social Sciences 1 (1) 1-8 https://doi.org/10.25163/business.1110399
Submitted: 13 December 2022 Revised: 18 February 2023 Accepted: 22 February 2023 Published: 24 February 2023
Abstract
Background: Higher education institutions (HEIs) in the United States face increasing pressure to improve instructional administration in response to complex academic demands and growing student expectations. The traditional manual approach generates operational delays which result in student service delivery delays and staff employment disparities.
Methods: The research study employed a mixed-methods approach which included five American universities that consisted of Harvard University and Stanford University and University of Michigan and University of Texas at Austin and Arizona State University. The researchers examined data which came from enrollment records and faculty workload reports and student satisfaction surveys that spanned from 2018 to 2023. The study gathered survey and interview data from 237 participants who included administrators and faculty members and staff personnel.
Results: Optimization frameworks brought major success to the organization through their execution. The administrative response speed improved by 34 to 36 percent while faculty work distribution became more balanced by 25 to 26 percent and student contentment rose by 14 percent. The predictive modeling approach achieved better course demand forecasts which improved prediction accuracy from 68% to 90-92%. The evaluation between different institutions showed that all institutions received advantages yet the level of impact differed according to their digital preparedness and available resources.
Conclusion: Data-driven process optimization brings substantial changes to educational management systems which operate throughout American universities. The use of analytics and predictive tools helps Higher Education Institutions achieve better operational efficiency and enhanced fairness and improved student services.
Keywords: Data-driven optimization, instructional administration, higher education, process mining, predictive analytics
References
Ayeni, A. J., & Amanekwe, A. P. (2018). Teachers’ instructional workload management and students’ academic performance in public and private secondary schools in Akoko North-East Local Government, Ondo State, Nigeria. American International Journal of Education and Linguistics Research, 9–23. https://doi.org/10.46545/aijelr.v1i1.135
Bodily, R., & Verbert, K. (2017). Review of research on Student-Facing Learning Analytics Dashboards and Educational Recommender Systems. IEEE Transactions on Learning Technologies, 10(4), 405–418. https://doi.org/10.1109/tlt.2017.2740172
Bogarín, A., Cerezo, R., & Romero, C. (2017). A survey on educational process mining. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery, 8(1). https://doi.org/10.1002/widm.1230
Bowles, D. E., & Gardiner, L. R. (2018). Supporting process improvements with process mapping and system dynamics. International Journal of Productivity and Performance Management, 67(8), 1255–1270. https://doi.org/10.1108/ijppm-03-2017-0067
Chung, J. Y., & Lee, S. (2018). Dropout early warning systems for high school students using machine learning. Children and Youth Services Review, 96, 346–353. https://doi.org/10.1016/j.childyouth.2018.11.030
Daniel, B. (2014). Big Data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904–920. https://doi.org/10.1111/bjet.12230
Deil-Amen, R., & Rosenbaum, J. E. (2003). The social Prerequisites of success: Can college structure reduce the need for Social Know-How? The Annals of the American Academy of Political and Social Science, 586(1), 120–143. https://doi.org/10.1177/0002716202250216
Dibiase, D., & Rademacher, H. J. (2005). Scaling up: faculty workload, class size, and student satisfaction in a distance learning course on geographic Information science. Journal of Geography in Higher Education, 29(1), 139–158. https://doi.org/10.1080/03098260500030520
Domínguez, C., & Jaime, A. (2010). Database design learning: A project-based approach organized through a course management system. Computers & Education, 55(3), 1312–1320. https://doi.org/10.1016/j.compedu.2010.06.001
Edelstein, J. a. D. a. R. (2009, October 1). THE GLOBAL COMPETITION FOR TALENT the rapidly changing market for international students and the need for a strategic approach in the US. https://escholarship.org/uc/item/0qw462x1
Elia, G., Solazzo, G., Lorenzo, G., & Passiante, G. (2018). Assessing learners’ satisfaction in collaborative online courses through a big data approach. Computers in Human Behavior, 92, 589–599. https://doi.org/10.1016/j.chb.2018.04.033
Elmagarmid, A. K., Ipeirotis, P. G., & Verykios, V. S. (2006). Duplicate Record Detection: a survey. IEEE Transactions on Knowledge and Data Engineering, 19(1), 1–16. https://doi.org/10.1109/tkde.2007.250581
Fertier, A., Barthe-Delanoë, A., Montarnal, A., Truptil, S., & Bénaben, F. (2020). A new emergency decision support system: the automatic interpretation and contextualisation of events to model a crisis situation in real-time. Decision Support Systems, 133, 113260. https://doi.org/10.1016/j.dss.2020.113260
Glazer, J. L., & Peurach, D. J. (2012). School Improvement Networks as a Strategy for Large-Scale Education Reform. Educational Policy, 27(4), 676–710. https://doi.org/10.1177/0895904811429283
Hora, M. T., Bouwma-Gearhart, J., & Park, H. J. (2017). Data driven decision-making in the era of accountability: Fostering faculty data cultures for learning. Review of Higher Education/?the ?Review of Higher Education, 40(3), 391–426. https://doi.org/10.1353/rhe.2017.0013
Jacobson, S. (2011). Leadership effects on student achievement and sustained school success. International Journal of Educational Management, 25(1), 33–44. https://doi.org/10.1108/09513541111100107
Kardan, A. A., Sadeghi, H., Ghidary, S. S., & Sani, M. R. F. (2013). Prediction of student course selection in online higher education institutes using neural network. Computers & Education, 65, 1–11. https://doi.org/10.1016/j.compedu.2013.01.015
Kassen, M. (2018). Open data and its institutional ecosystems: A comparative cross-jurisdictional analysis of open data platforms. Canadian Public Administration, 61(1), 109–129. https://doi.org/10.1111/capa.12251
Kotsiantis, S. B. (2011). Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artificial Intelligence Review, 37(4), 331–344. https://doi.org/10.1007/s10462-011-9234-x
Kumar, V. O. S. S. O. C. a. I. (2018, July 9). Digital Thesis Room: Identifying student difficulty and frustration from discussion forum postings. http://hdl.handle.net/10791/267
Laux, C., Li, N., Seliger, C., & Springer, J. (2017). Impacting Big Data analytics in higher education through Six Sigma techniques. International Journal of Productivity and Performance Management, 66(5), 662–679. https://doi.org/10.1108/ijppm-09-2016-0194
Lee, C. (2005). Diagnostic, predictive and compositional modeling with data mining in integrated learning environments. Computers & Education, 49(3), 562–580. https://doi.org/10.1016/j.compedu.2005.10.010
Nagar, K. (2012). Organizational Commitment and Job Satisfaction among Teachers during Times of Burnout. Vikalpa the Journal for Decision Makers, 37(2), 43–60. https://doi.org/10.1177/0256090920120205
O’Meara, K., Lennartz, C. J., Kuvaeva, A., Jaeger, A., & Misra, J. (2019). Department Conditions and Practices Associated with Faculty Workload Satisfaction and Perceptions of Equity. The Journal of Higher Education, 90(5), 744–772. https://doi.org/10.1080/00221546.2019.1584025
Obaid, H. S., Dheyab, S. A., & Sabry, S. S. (2019). The Impact of Data Pre-Processing Techniques and Dimensionality Reduction on the Accuracy of Machine Learning. Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON), 279–283. https://doi.org/10.1109/iemeconx.2019.8877011
Rabovsky, T. M. (2014). Using data to manage for performance at public universities. Public Administration Review, 74(2), 260–272. https://doi.org/10.1111/puar.12185
Ray, S., & Saeed, M. (2018). Applications of educational data mining and learning analytics tools in handling big data in higher education. In Springer eBooks (pp. 135–160). https://doi.org/10.1007/978-3-319-76472-6_7
Schertzer, C. B., & Schertzer, S. M. B. (2004). Student Satisfaction and Retention: a Conceptual model. Journal of Marketing for HIGHER EDUCATION, 14(1), 79–91. https://doi.org/10.1300/j050v14n01_05
Sellani, R. J., & Harrington, W. (2002). Addressing administrator/faculty conflict in an academic online environment. The Internet and Higher Education, 5(2), 131–145. https://doi.org/10.1016/s1096-7516(02)00090-8
Seyedan, M., & Mafakheri, F. (2020). Predictive big data analytics for supply chain demand forecasting: methods, applications, and research opportunities. Journal of Big Data, 7(1). https://doi.org/10.1186/s40537-020-00329-2
Shobrys, D. E., & White, D. C. (2002). Planning, scheduling and control systems: why cannot they work together. Computers & Chemical Engineering, 26(2), 149–160. https://doi.org/10.1016/s0098-1354(01)00737-2
Skrbinjek, V., & Dermol, V. (2019). Predicting students’ satisfaction using a decision tree. Tertiary Education and Management, 25(2), 101–113. https://doi.org/10.1007/s11233-018-09018-5
Strausser, J. (2015). EXAMINING THE APPLICATION OF BIG DATA ANALYTICS TO INCREASE UNIVERSITY RETENTION AND PROMOTE STUDENT SUCCESS. EBSCOhost. https://openurl.ebsco.com/EPDB%3Agcd%3A7%3A5822055/detailv2?sid=ebsco%3Aplink%3Ascholar&id=ebsco%3Agcd%3A110321416&crl=c&link_origin=scholar.google.com
Tavoletti, E. (2009). Governance Shifts in Higher Education: Across National Comparison. Higher Education and Local Economic Development, 1000–1039. https://doi.org/10.1400/112588
Tonelli, F., Demartini, M., Loleo, A., & Testa, C. (2016). A novel methodology for manufacturing firms value modeling and mapping to improve operational performance in the industry 4.0 era. Procedia CIRP, 57, 122–127. https://doi.org/10.1016/j.procir.2016.11.022
Vialardi, C., Chue, J., Peche, J. P., Alvarado, G., Vinatea, B., Estrella, J., & Ortigosa, Á. (2011). A data mining approach to guide students through the enrollment process based on academic performance. User Modeling and User-Adapted Interaction, 21(1–2), 217–248. https://doi.org/10.1007/s11257-011-9098-4
Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626–639. https://doi.org/10.1016/j.ejor.2017.02.023
Wanderer, J. P., De Oliveira Filho, G. R., Rothman, B. S., Sandberg, W. S., & McEvoy, M. D. (2017). Implementation and evaluation of the Z-Score system for normalizing residency evaluations. Anesthesiology, 128(1), 144–158. https://doi.org/10.1097/aln.0000000000001919
Williamson, B. (2015). Digital education governance: data visualization, predictive analytics, and ‘real-time’ policy instruments. Journal of Education Policy, 31(2), 123–141. https://doi.org/10.1080/02680939.2015.1035758
Xu, S., Lu, B., Baldea, M., Edgar, T. F., Wojsznis, W., Blevins, T., & Nixon, M. (2015). Data cleaning in the process industries. Reviews in Chemical Engineering, 31(5). https://doi.org/10.1515/revce-2015-0022
Zhao, E. Y., Fisher, G., Lounsbury, M., & Miller, D. (2016). Optimal distinctiveness: Broadening the interface between institutional theory and strategic management. Strategic Management Journal, 38(1), 93–113. https://doi.org/10.1002/smj.2589