Academic Success, Graduation Rates, Students, Teaching, Undergraduate Education
All students who enroll have success as their main goal. However, most institutions focus their resources on programs for students on honor roll, Dean’s list and those progressing academically. Little resources remain for those students who stumble. In 2015, 36.2% of white students, 22.5% of black students, and 15.5% of Hispanic students had completed four years of college. This shows a 13.7% gap between black and white students and a 20.7% gap between Hispanic and white students (Wellman, 2017). How do we close this gap in educational completion?
This study believes that all students can learn. Consequently, there needs to be educational equity and the development of a basis for instruction and assessment of all students’ learning outcomes. This paper represents an exploratory fundamental and qualitative research that aims to present a refocus on the role of faculty in teaching and learning to reach all students in classrooms. It examines a holistic and collaborative approach to increasing student success using evidence -based qualitative analysis of best practices. This approach has four component parts. Part 1 is the Holistic Component that involves engaging all students in the institution; communicating purposefully to them in a timely manner; and providing all-inclusive comprehensive support services (HC). This part develops and implements measurable benchmarks that motivate, encourage, and enable all students. Part 2 is the Collaborative Component which involves bringing six working teams together: faculty, industry, current majors, alumni, career services, and the community (CC). This part engages the team in maintaining a living curriculum that reflects the ever-changing global economy. Part 3 is Celebration of Student Success (CSS) which entails the collaborative team owning each milestone, reaffirming teamwork while building trust and persistence. Part 4 is the Assessment of Student Progress (ASP) using the holistic and collaborative approach.
The paper concludes that holistic and collaborative teamwork that includes, respects, and empowers all students is the key to reducing the college completion gaps that exist among blacks and Hispanic students.
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Adelman, C. (2006). The Toolbox revisited: Paths to degree completion from high school though college. U.S. Department of Education. Washington, D.C: Office of Vocational and Adult Education.
American College Testing Program (ACT) 2006.
Anoopkumar, M., & Rahman, A. M. J. M. Z. (2016). A Review on Data Mining techniques and factors used in Educational Data Mining to predict student amelioration. In 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), (pp. 122–133).
Asif, R., Merceron, A., Abbas, S., & Ghani, N. (2017). Analyzing undergraduate students’ performance using educational data mining. Computers in Education, 113, 177–194.
Asif, R., Merceron, A., & Pathan, M. K. (2015). Predicting student academic performance at degree level: A case study. International Journal of Intelligent Systems and Applications, 7(1), 49–61.
Astin, A.W. (1993). What matters in college: Four critical years revisited? San Francisco: Jossey-Bass
Bailey, T.R., & Leinbach, D.T. (2005). Is student success considered institutional failure?? The accountability debate at community colleges. New York, NY: Community College Research Center, Teachers College, Columbia University
Bailey, T., Jaggars, S., & Jenkins, D. (2015). Redesigning America’s community colleges: A clearer path to student success. Cambridge, MA: Harvard University Press.
Bauman, G. L., Bustillos, L.T., Bensimon, E.M., Brown, M.C., & Bartee, R.D. (2005). Achieving Equitable Educational Outcomes with All Students: The Institution’s Roles and Responsibilities. AAC&U/Ford Foundation.
Baum, S., & Payea, K. (2004). Education pays: The benefits of higher education for individuals and society. Washington, DC: The College Board.Bensimon 2004
Bensimon, E.M., Dowd, A.C., & Witham, K. (2016). Five principles for enacting equity by design. Diversity & Democracy, 19(1).
Brame, C. J. (2019). Inclusive teaching: Creating a welcoming, supportive classroom environment. In Science teaching essentials: Short guides to good practice (pp. 3–14). San Diego: Elsevier/Academic Press.
Calvet Liñán, L., & Juan Pérez, Á. A. (2015). Educational Data Mining and Learning Analytics: differences, similarities, and time evolution. International Journal of Educational Technology in Higher Education, 12(3), 98.
Center for Community College Student Engagement. (2013). A matter of degrees: Engaging practices, engaging students (High-impact practices for community college student engagement). Austin, TX: The University of Texas at Austin, Community College Leadership Program.
Chen, X. (2015). STEM attrition among high-performing college students: Scope and potential causes. Journal of Technology and Science Education, 5(1), 41–59.
Dadgar, M., Venezia, A., Nodine, T., & Bracco, K. R. (2013). Providing structured pathways to guide students toward completion. San Francisco: WestEd.
Dewsbury, B. M. (2017). Context determines strategies for “activating” the inclusive classroom. Journal of Microbiology & Biology Education, 18(3). 18.3.66.
Dewsbury, B. M. (2019). Deep teaching: A conceptual model for inclusive approaches to higher education STEM pedagogy. Cultural Studies in Science Education. doi.org/10.1007/s11422-018-9891-z. Accessed 3/30/2019
Dutt, A., Ismail, M. A., & Herawan, T. (2017). A systematic review on educational data mining. IEEE Access, 5, 15991–16005.
Estrada, M., Burnett, M., Campbell, A. G., Campbell, P. B., Denetclaw, W. F., Gutiérrez, C. G., ... Okpodu, C. M. (2016). Improving underrepresented minority student persistence in STEM. CBE—Life Sciences Education, 15(3), es5.
Finley, A. & McNair, T. (2013). Assessing underserved students' engagement in high-impact practices. Washington, DC: Association of American Colleges and Universities.
Finn, J. D., & Rock, D. A. (1997). Academic success among students at risk for school failure. The Journal of Applied Psychology, 82(2), 221–234.
Freeman, T. M., Anderman, L. H., & Jensen, J. M. (2007). Sense of belongingness of college freshmen at the classroom and campus levels. Journal of Experimental Education, 75, 203–220. Freire, P. (1970).
Goffee, R., & Jones, G. (2000, September/October). Why should anyone be led by you? Harvard Business Review. Retrieved from https://hbr.org/2000/09/why-should-anyone-be-led-by-you. Accessed 1/15/2019.
Haak DC, HilleRisLambers J, Pitre E, Freeman S (2011). Increased structure and active learning reduce the achievement gap in introductory biology. Science 332, 1213–1216.
Hamoud, A. K., Hashim, A. S., & Awadh, W. A. (2018). Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis. International Journal of Interactive Multimedia and Artificial Intelligence, in Press, 1.
Hearn, J.C. (2006). Student success: What research suggest for policy and practice. Paper presented at the National Symposium on Postsecondary Student Success. Available, https://nces.ed.gov/npec/pdf/synth_Hearn.pdf . Accessed 2/1/2019.
Hearn, J. C. (2015). Outcomes-based funding in historical and comparative contexts. The Lumina Foundation. Available, www.luminafoundation.org/files/resources/hearn-obf-full.pdf . Accessed 1/5/2019
Henderson, S.E. (2008). Admissions' evolving role: From gatekeeper to strategic partner. In B Lauren (Ed.), The College Admissions' Officer's Guide (pp. 1-22) Washington, D.C.: American Association of Collegiate Registrars and Admissions Officers.
Hurtado, S., Alvarez, C. L., Guillermo-Wann, C., Cuellar, M., & Arellano, L. (2012). A model for diverse learning environments: The scholarship on creating and assessing conditions for student success. In J. C. Smart & M. B. Paulsen (Eds.), Higher education: Handbook of theory and research (Vol. 27, pp. 41–122). New York, NY: Springer.
Kuh, G. D., Kinzie, J., Buckley, J. A., Bridges, B. K., & Hayek, J. C. (2006). What matters to student success: A review of the literature commissioned report for the National Symposium on postsecondary student success. University of California and F. Foundation for Open Access Statistics.
Kuh, G.D. (2016). Making learning meaningful: Engaging students in ways that matter to them. In M. Watts (Ed.), Finding the why: Personalizing learning in higher education. New Directions for Teaching and Learning, No.145. San Francisco: JosseyBass.
Kuh, G., O'Donnell, K., & Reed, S. (2013). Ensuring quality & taking high-impact practices to scale. Washington, D.C.: Association of American Colleges and Universities.
Moira Cachia, Siobhan Lynam & Rosemary Stock (2018) Academic success: Is it just about the grades? Higher Education Pedagogies, 3:1, 434-439, DOI: 10.1080/23752696.2018.1462096
Martins, M. P. G., Miguéis, V. L., Fonseca, D. S. B., & Alves, A. (2019). A data mining approach for predicting academic success – A case study, (pp. 45–56). Cham: Springer.
Maton, K. I., Beason, T. S., Godsay, S., Sto. Domingo, M. R., Bailey, T. C., Sun, S., & Hrabowski, F. A. (2016). Outcomes and processes in the Meyerhoff Scholars program: STEM PhD completion, sense of community, perceived program benefit, science identity, and research-self-efficacy. CBE—Life Sciences Education, 15, ar48.
Mohamed, M. H., & Waguih, H. M. (2017). Early prediction of student success using a data mining classification technique. International Journal of Science and Research, 6(10), 126–131.
Muijs, D., & Reynolds, D. (2011). Effective teaching. Evidence and practice. London: Sage. National Center for Public Policy and High Education 2005
Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students (Volume 2): A third decade of research. San Francisco, CA: Jossey-Bass.
Perez, T., Cromley, J. G., & Kaplan, A. (2014). The role of identity development, values, and costs in college STEM retention. Journal of Educational Psychology, 106(1), 315–329.
Pérez, B., Castellanos, C., & Correal, D. (2018). Predicting student drop-out rates using data mining techniques: A case study, (pp. 111–125). Cham: Springer.
Purdie, J. R., & Rosser, V. J. (2011). Examining the academic performance and retention of first-year students in living-learning communities and first year experience courses. College Student Affairs Journal, 29(2), 95.
Reardon, S. F. (2011). The widening academic achievement gap between rich and poor: New evidence and possible explanations. In G. J. Duncan & R. J. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children's life chances (pp. 91–115). New York: Russell Sage Foundation.
Reardon, S.F. (2013). The widening income achievement gap. Educational Leadership, 70(8), 10-16.
Reason, R. D., Terenzini, P. T., & Domingo, R. J. (2006). First things first: Developing academic competence in the first year of college. Research in Higher Education, 47(2), in press.
Schinske, J. N., Perkins, H., Snyder, A., & Wyer, M. (2016). Scientist Spotlight homework assignments shift students’ stereotypes of scientists and enhance science identity in a diverse introductory science class. CBE—Life Sciences Education, 15(3), ar47.
Strayhorn, T. L. (2015). Cultural navigation and college student success: What works for ensuring academic excellence (CHEE #What Works Report Series 2015-001). Columbus, OH: Center for Higher Education Enterprise, The Ohio State University.
Terenzini, P. T., and Reason, R. D. (2005, November). Parsing the first year of college: A conceptual framework for studying college impact. Paper presented at the annual meeting of the Association for the Study of Higher Education, Philadelphia
Thelin, J. R. (2011). A history of American higher education (2nd ed.). Baltimore, MD: Johns Hopkins University Press.
Valant, J., & Newark, D. A. (2016). The Politics of Achievement Gaps: U.S. Public Opinion on Race-Based and Wealth-Based Differences in Test Scores. Educational Researcher, 45, 331-346. https://doi.org/10.3102/0013189X16658447. Accessed 1/16/2019
Wellman, M. (2017). Report: The Race Gap in Higher Education is Very Real. University of Virginia. https://www.usatoday.com/story/college/2017/03/07/report-the-race-gap-in-higher-education-is-very-real/37428635/. Accessed 1/16/2019
Willems, J., Coertjens, L., Tambuyzer, B., & Donche, V. (2019). Identifying science students at risk in the first year of higher education: the incremental value of non-cognitive variables in predicting early academic achievement. European Journal of Psychology of Education, 34(4), 847–872.
Wilson, D., Jones, D., Bocell, F., Crawford, J., Kim, M. J., Veilleux, N., ... Plett, M. (2015). Belonging and academic engagement among undergraduate STEM students: A multi-institutional study. Research in Higher Education, 56(7), 750–776.
Witham, K, Malcom-Piqueux, L., Dowd, A.C., & Bensimon, E.M. (2015). America’s Unmet Promise: The Imperative for Equity in Higher Education. Washington, DC: Association of American Colleges and Universities.
York, T.T., Gibson, C., & Rankin, S. (2015). Defining and measuring academic success. Practical assessment, research, and evaluation, 20 (5), 1–20. [Google Scholar]
Zumbrunn, S., McKim, C., Buhs, E., & Hawley, L. R. (2014). Support, belonging, motivation, and engagement in the college classroom: A mixed method study. Instructional Science, 42, 661–684.