Persistence: Sustaining, Systematizing, Institutionalizing »
Use Data to Guide your Work
Data analysis can help stakeholders understand the problem, illuminate issues that are not fully understood, mobilize proponents, and neutralize skeptics who deny a problem exists. Data can also lead to ongoing refinement of project goals. Data and accomplishments from other institutions can form an important component of lessons learned, but should be understood within the local institutional context and culture. As projects move forward into implementation, data collection serves as an important tool to monitor and assess progress.
Articulate Perceived Problems and Student Learning Goals
To guide the collection of data, it is important to first articulate the problems, limitations, or inadequacies of current practices in light of aspirations for student learning.
The courses developed with HHMI funding were designed to increase student engagement in research. We learned that students were unable to enroll in several of the offered courses because their academic schedule is too constrained. An analysis of department majors showed that many students only had three hours of free electives available to them over their entire college career. As a result of this analysis, the entire biology curriculum was revised to allow for more student enrollment flexibility. The new schedule eliminates most required biology courses, converting them all to free electives, and allows the student to get departmental credit for research or for related classes taken outside the department. This allows students to craft an interdisciplinary major and explore their own areas of interest.
Analyze Existing Institutional Data and Collect Targeted New Data
Institutional data may already exist that is helpful to understanding the issue at hand. For example, the Grinnell College folks found that high school performance and standardized exam scores did not predict difficulties with introductory math and science courses, but environmental factors such as being a first-generation college student or a domestic student of color, or graduating from high schools where fewer than half the students go on to enroll in college, were predictors of poor performance. This data was readily available within the various offices of the institution. In other instances, data collection strategies can be developed specific to the particular project.
The use of data to inform, sustain, and manage our programs focused on inclusive excellence ensures that we are effectively serving our students while simultaneously offering evidence of success to our faculty, administration, and funders.
Faculty and administrators at Barnard regularly monitor the choice of majors and academic performance of students from underrepresented groups as well as those who arrive at Barnard from under-resourced high schools. Statistical analyses of four cohorts of recent graduates explore the effects of academic preparation, standardized test scores, race/ethnicity, and family economic circumstances on persistence and performance in science majors. The insights from the results of these analyses are allowing Barnard faculty to develop curricular and programmatic offerings and interventions designed to overcome the barriers to student success.
The Grinnell Science Project started with the anecdotal observation that African American students had lower success rates in introductory math and science courses. Analysis of admissions data and grades revealed that other domestic students of color, first-generation college students, and students graduating from colleges where fewer than half went on to enroll in college suffered a similar fate, but that standard predictors of college grades—such as standardized exam scores and high school grades—did not correlate with grades in introductory math and science courses. This led us to create a program more focused upon building community and dealing with the context for study of science and less academic remediation.
Review External Data to Evaluate Lessons Learned
Data collected relative to accomplishments at other institutions or from national agencies can be an important component when analyzing lessons learned. In some cases, comparisons to the home institution can be readily made, while in other instances data translation is more nuanced.Identify What Success Looks Like and Establish a Way to Monitor/Assess Progress
Clearly identifying goals and descriptions of likely successes allows you to collect formative data and monitor progress as you proceed, This can inform needs to change approaches, directions, etc. It is also essential to convincing students, faculty, and institutional leadership of the compelling case for institutionalizing the changes.
Given the observed challenge that the Grinnell Science Project aimed to deal with was lower grades from the groups "at risk" in introductory math and science courses and lower persistence in graduating with science majors, our explicit goals were to halve the grade deficit of the target population (as compared to other students) and to double the number of domestic students of color graduating with science and math majors.