The Impact of Online Shopping on Higher Education
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Student enrollment and retention are paramount to the ongoing operation of a college or university. Recognition of achievement is a motivational factor for college students to remain enrolled in their courses and persist in their coursework to graduate, as past research has indicated. This investigation measures the impact of student recognition of excellence on student persistence in an online, competency-based higher education program. The study includes a retrospective longitudinal study of all excellence award recipients from August 2016 to August 2019, involving the correlation of student’s course progression after receiving the award and its impact on three key student success indicators: On Time Progress (OTP), Student Academic Progress (SAP) and Course Completion Rate (CCR).
Using a mixed methodology, student success analytics foundational to this student success ensued based on the qualitative content analysis of grateful student responses and the quantitative data analysis tool. Initial findings indicated that students struggling and on the verge of dis-enrolling exhibited greater motivation to perform well in a competency-based education model following student recognition of excellence (14 days: 11-20% persistence lift, p<0.01, n=30,440). Subsequent findings validated initial findings (10 weeks: 5.9% persistence lift, p<0.01, n=27,510; 20 weeks: 3.6% persistence lift, p<0.01, n=27,490). This study’s results are significant and have shown that student recognition positively impacts student retention and academic success. Future research in this realm is encouraged.
Extant research indicated student recognition affects student’s self-efficacy, motivation and level of effort put forth. Students with low academic performance, have higher academic performance after recognition. Students react to receiving recognition by expending more significant effort toward higher academic achievement. However, these findings were not from studies conducted in a competency-based education model.
The study is based on an online university operating in a competency-based education model. The students demonstrate competency (showing what they know and can do) in their courses to pass their performance and objective assessments. There is a considerable amount of self-regulated learning on students’ part in this education model, while the learning is facilitated and mentored by faculty. Therefore, in this learning environment, students must have a higher level of motivation that fosters persistence than in another educational model.
Based on the findings of extant research regarding college student motivation and persistence, the following research questions emerge: (1) What effect is the online competency-based university awards program having on their students? (2) How does student recognition of excellence support and enhance indicators of student success: On-Time Progress (OTP), Satisfactory Academic Progress (SAP) and Course Completion Record (CCR)?
This research is a retrospective longitudinal study over three years with a mixed-method investigation in a competency-based education model. The qualitative method was a content analysis using a content analysis software tool. This tool is a software program designed for computer-assisted qualitative data, text and multi-media analysis of content. The quantitative investigation commenced using a quasi-experimental data analysis tool.
The qualitative analysis measured the frequency of trending words and phrases in a student’s comment written after receiving an excellence award. The content analysis tool analyzed 11,644 student grateful responses, identifyingwords and phrases expressing gratitude such as: “…thank you, …,” “thanks for the award.” Another type of trending phrase emerged: “I was on the verge of quitting…,” “…was about to give up…,”…was about to quit school.” 15% of the phrases written were from students about giving up, leaving and quitting school.
The quantitative method measured student’s persistence lift. Thestatistical analysis tool uses a two-dimensional method known as a prediction-based propensity score (PPSM). This method matches one group of students receiving an award with the group of students not receiving an award. Similar students are matched to identical persistence probabilities that they boths show. These persistence probabilities match several categories, including OTP, SAP, CCR and demography, with behavioral indicators. The action of matching one student to another helps control the amount of bias in any analyzed initiative.
The quantitative statistical tool uses a quasi-experimental statistical method to determine the persistence lift for the specified student group. Persistence liftis defined as the student’s ability to meet on-time progression (8/12 CU’s) and be retained to the following term for a minimum of 45 days. Persistence lift is measured by comparing the propensity score and predicted outcomes of the students who participated in the initiative to those who did not. This methodology is an extension of the Propensity Score Matching methodology developed by Rosenbaum and Rubin in 1983 to calculate propensity (Rosenbaum & Rubin (1983). Using this patented methodology provided a way to analyze several initiatives by removing bias and effectively limiting other confounding factors that might differ participant students from those in the comparison group.
There are key data process steps integral to establishing a high level of confidence in this cutting-edge student success data analytics tool. In this initiative, the participant group was classified as students who received an excellence award, and the comparison group was every student who did not receive it. There are five high-level process steps to this method; the first identifies the eligible students (known as the participant group). The completed method includes nine steps to explore and interpret the results, ensuring the research team understands the initiative and context details and that the overall persistence lift is intuitive and not too good to be true. The research team also makes sure that the results are statistically significant or that the match rate is greater than 85%.
The qualitative analysis showed a significant effect taking place by the student’s comments. The content analysis of the student’s comments (n= 11,644) shows 15% of the students receiving the excellence award were on the verge of giving up and dropping their enrollment. Another trend identified 16% of the comments as expressing a boost of confidence, which affected the level of motivation confirmed with the measurement of persistence lift.
Using PPSM, and after comparing students in the same three-year time period, the statistical analysis (n=30,340) shows a significant overall lift of 10.9% in persistence (p<0.01) after 14 days, with students in the lower quartile being the ones with the most lift in persistence (20.23%, p<0.01). Subsequent analysis validated initial findings using the more targeted time parameters of 10 and 20 weeks after receiving an excellence award, comparing students receiving the award versus students not receiving the award: 10 weeks—5.9% persistence lift, p<0.01, n=27,510; 20 weeks—3.6% persistence lift, p<0.01, n=27,490. In both analyses, students in the lower quartile (struggling with their coursework) showed the most persistence lift—10 weeks: 13.95%, p<0.01; 20 weeks: 11.95%, p<0.01. From all indications provided in the quantitative data analysis, these students decided to continue their studies, remain enrolled and persisted in their coursework after receiving an excellence award.
Compared to the persistence lift of students who received an award and were in term 1-3 (8.44%, p<0.01) and those who were in term 4+ (7.36%, p<0.01), students in their first term had a more significant persistence lift than students who did not receive an award (14.96%, p<0.01). These findings indicate that “early cheering” versus “early warning” positively affected the student’s outcome measures of OTP, SAP, and CCR. Additional results include the measurable positive impact of receiving an excellence award on a diverse student population and students attending college for the first time.
The research investigation confirmed the findings of past research when applied to other education models. The recognition of students’ effort as being deemed excellent has been quantifiably shown to impact their student outcomes and achievements measured by course completion, on-time-performance and satisfactory academic progress. This research investigation is the first study of its nature in a competency-based education model (as known at this time).
This study’s findings indicate that a positive impact occurs after a student receives recognition for their efforts. They become motivated, have greater intention and put forth a more significant effort and energy to successfully complete their courses and graduate on time. Recognizing students take on the form of “cheering” has shown a significance in positive persistence lift. The positive nudge in receiving an excellence award fostering motivation, self-efficacy and self-regulated learning offers improved academic performance, as indicated in past studies in educational models not competency-based and in this study. Momentum work versus intervention work is brought to light with the results of this research investigation.
Students on the verge of leaving school who receive recognition remain enrolled and engaged in their degree pursuit. The cheer to continue doing well is what motivates and encourages them to continue to work with excellence. Lower-performing students perform better after recognition of excellence.
Given what we learned from this research study, we can adapt this insight to our current practices and are motivated to explore additional opportunities to recognize students. Also, we are inspired to continue this research to gain further insights into the effect the excellence award is having on our students with regression, correlation and nearest neighbor analysis.
Future research in this domain is advised, including using a student survey measuring motivation, self-efficacy and self-regulated learning levels to triangulate more of the evidence brought forth in this study investigation. Future studies investigating the effect student recognition has on student success factors may alter the cheering versus warning policy. It may also serve as encouragement to continue putting forth an excellent effort and graduate with skills, then re-skill and upskill to be even more employable.
This piece is an excerpt from a research report, co-authored by Annamaria Bliven, Michelle Jungbauer (Senior Manager of Student Experience at Western Governors Universty), and Issac Martin (Business Process Optimization Engineer at ABP).
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Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Students’ learning strategies and motivation processes. Journal of Educational Psychology, 80, 260-267.
Angrist, J., & Oreopoulos, P. (2009). Incentives and services for college achievement: Evidence from a randomized trial. American Economic Journal: Applied Economics, 1 (1): 136-163
Angrist, J., Oreopoulos, P., & Williams, T. (2013). When opportunity knocks, who answers? Journal of Human Resources, 49 (3), 572-610.
Atieno, L. (2018). Extrinsic motivation: Why it’s the best approach. The New Times, 1 (1): 1-3.
Bowman, N.A., Miller, A., Woosley, S., Maxwell, N.P., & Kolze, M. (2019). Understanding the link between non-cognitive attributes and college retention. Research in Higher Education, 60(2), 135-152.
Cameron, J. & Pierce, W.D. (1994). Reinforcement, reward, and intrinsic motivation: A meta-analysis. Review of Educational Research, 64, 363-423.
Cameron, J. & Pierce, W.D. (1996). The debate about rewards and intrinsic motivation: Protests and accusations do not alter the results. Review of Educational Research, 66, 39-51.
Cho, Y.J., Harrist, S., Steele, M. & Murn, L.T. (2015) College Student Motivation to Lead in Relation to Basic Psychological Need Satisfaction and Leadership Self-Efficacy. Journal of College Student Development, 56 (1), 32-44.
Cunningham, C. A. (2011). Using Learner Controlled Progress-Based Rewards to Promote Motivation and Achievement of At-Risk Students in Managed Online Learning Environments. Review of Educational Research, 66, 1-4.
Deci, E.L. (1972). The effects of contingent and non-contingent rewards and controls on intrinsic motivation. Organizational Behavior and Human Performance, 8, 217-229.
Gneezy, U., Meier, S., Rey-Biel, P. (2011). When and why incentives (Don’t) work to modify behavior. Journal of Economic Perspectives, 25 (4): 191-210.
Heller, D.E., & Rogers, K.R. (2003). Merit scholarships and incentives for academic performance. Paper presented at the 20th Annual NASSGAP/NCHELP Student Financial Aid Research Network Conference.
Hu, S. (1984). Scholarship Awards, Student Engagement, and Leadership Capacity of High-Achieving Low-Income Students of Color. Journal of Higher Education, 82 (5), 511-534.
Kremer, M., Miguel, E., & Thornton, R. (2009). Incentives to learn. The Review of Economics and Statistics, 91 (3), 437-456.
Lepper, M.R., & Greene, D. (1988). Motivational considerations in the study of instruction. Cognition and Instruction, 5, 289-309.
Levitt, S.D., List, J.A., Neckermann, S., & Sadoff, S. (2011). The impact of short-term incentives on student performance. University of Chicago Working Paper.
MacDonald, H., Malatest, R., Assels, R., et al. (2009). Final impacts report: Foundations for a successful project. Report to the Canadian Millennium Scholarship Foundation. Ottawa: Canada Millennium Scholarship Foundation.
Rosenbaum, P., & Rubin, D. (1983). The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 70(1), 41-55. doi:10.2307/2335942
Stover, J.B., Hoffman, A.F., Iglesia, G., & Liporace, M.M. (2014). Predicting academic achievement: The role of motivation and learning strategies. Problems of Psychology in the 21st Century, 8 (1), 71-84.
Tomlinson, T. (1992). Hard work and high expectations: Motivating student to learn. Issues in Education, April 1992, 1-35.
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