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, the academic units (e.g., those who manage the academic mission) , and the institutional leaders (e.g., those who make policy and resource decisions that impact how teaching 209
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Examining these interactions policies, practices, and and student infrastructure learning. limitations is critical to supporting a t it u t i o n l C o n t e x Ins t: Instructors Cognitive Campus Sociocultural Leaders Academic Units s In ra re u f str d uc ce tu r P ro e, P o l icie s, a n d FIGURE 9-1 Institutional context impacts interactions and student learning.
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The concept of continuous improvement, originally developed in manufacturing, can usefully be applied to this type of STEM education reform (Singh & Singh, 2015; Temponi, 2005)
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, the Bayview Alliance, the Center for the Integration of Research, Teaching and Learning (see Chapter 8) , the network of STEM education centers, the Partnership for Undergraduate Life Sciences Education, and the Association for Undergraduate Education at Research Universities, all of which have been players in the effort to improve undergraduate STEM education (Austin et al., 2024)
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TABLE 9-1 Types of Organizational Change by Scope Type of Change Scope (e.g., depth, magnitude) Example First-Order Targets, or is meant to be compatible Implementing a recruitment Change with, existing structures, and thus does effort to diversify the STEM not seek to change those structures or faculty and student body on a their foundations.
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Any effort to make STEM more diverse, inclusive, and equitable demands the deepest and most difficult kind of change: third-order change. This approach requires new or revised policies and practices, long-term reallocation of resources, and ongoing learning and reflection to shift mental models about how academic units, colleges, universities, and/or the profession should work.
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. CREATE STUDENT-READY INSTITUTIONS The experience of teaching and mentoring undergraduate students in STEM varies by institutional context, mission, and perhaps, most importantly, across levels of student preparedness to learn the course material.
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The primary purpose of the workshop was to determine approaches to align financial decisions with inclusive instructional practices and student success metrics. Discussion focused on how data can be used to drive informed decision making that supports institutional improvement, including balancing instructional costs with financial realities, supporting systemic change for increased access and equity, and developing 3 More information about FAITE is available at https://ascnhighered.org/ASCN/cost_benefit.
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and typically require a larger proportional investment in terms of instructional spaces, resources, learning materials, and preparation of instructors and teaching assistants, all of which are reflected in the financial data and are a key factor in student-centered financial planning.4 These are just a sampling of the issues where combining various forms of data, including financial, can help inform institutional decision making about instruction and the extent to which equitable and effective teaching is prioritized. Institutional leaders often have to make difficult decisions about competing priorities and finite resources.
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One example of a policy that directly affects student success is transfer articulation (see Chapter 7)
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In addition, realistic access to professional learning requires that institutional leaders provide realistic compensation for the time required for everyone who teaches to do that learning and subsequently apply it to their courses. A practice that spans an entire institution -- both across disciplines and vertically through levels of leadership -- is creating true access to professional learning and development (PLD)
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This is clearly a complex subject that interacts with many other components of the larger system, and one that does not always get prioritized by the academic unit or the institution. Reflect on the Role of Grades Letter grades are considered a staple of the educational system, but the history of the system is more complex than usually recognized today (Durm, 1993; Schneider & Hutt, 2013)
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; and/or racial/ethnic minoritized status. Furthermore, even with all the variations in grading approaches -- which arise for a range of factors, from different instructors teaching the same course to inconsistencies between courses in an academic unit, and from differences
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Additional research on more holistic approaches to evaluating the capabilities of STEM students is needed, with special attention to emphasis on approaches that value the ability to learn and overcome struggle. ANALYZE INSTITUTIONAL DATA This section focuses on how data can be leveraged to define goals, measure progress, and guide movement toward equitable and effective STEM teaching at the institutional level.
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Both quantitative and qualitative data can be used for strategic planning, program evaluation, and instructors' annual reviews. Quantitative data may comprise the student outcomes that programs often use to gauge students' progress, including course pass/fail/withdrawal rates, Fall-to-Fall retention, enrollment numbers, and student responses to class evaluations, among others.
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Each of these areas can be considered at the class, academic unit, or institutional level. Here we provide examples at various levels as all are relevant to the thinking of institutional leaders using data to drive change.
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. how data have been used by an institution to inform redesign of courses in ways that promote higher student success.
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the California State University Equity Dashboards and Community College dashboards,7 and (d) the Sloan Equity & Inclusion in STEM Introductory Courses Stem Equity Learning Community project8 and resulting course equity reports and communication approach (Carlson, 2022; Meraz, 2022, Rehrey et al., 2019)
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The intermediate form of categorical data (e.g., gender, Pell eligibility,9 race/ethnicity, major, letter grade, enrollment type, prior schooling, veteran, etc.) is based on generally constructed definitions and is usually collected through questionnaires or derived based on quantitative data (Pell status)
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First-generation attendance, Pell eligibility, gender, and other categorical variables can and often do change definition over time so some students may fit one or more of these categories in any given academic year but not in the next. In general, quantitative data hold a privileged position in STEM conversations likely due to the similarity in numerical analysis approaches applied in STEM research.
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Local efforts focused on equity in introductory STEM courses have developed in a variety of institutions including those associated with the Association of American Universities (AAU) STEM initiative,14 NSF Improving Undergraduate STEM Education grants, HHMI Inclusive Excellence 10 More information about the University of California Undergraduate Experience Survey is available at https://www.ucop.edu/institutional-research-academic-planning/services/surveyservices/UCUES.html 11 More information about the National Survey of Student Engagement is available at https://nsse.indiana.edu/ 12 More information about the Community College Survey of Student Engagement is avail able at https://www.ccsse.org/ 13 More information about the surveys conducted by the Higher Education Research Insti tute is available at https://heri.ucla.edu/overview-of-surveys/ 14 More information about AAU's STEM initiative is available at https://www.aau.edu/ education-community-impact/undergraduate-education/undergraduate-stem-education-initiative
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. Box 9-2 shows an example of an equity dashboard that provides data on academic units that provide courses for students who are majoring in another field.
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Next, users can look at disaggregated data focused on course performance at multiple levels (e.g., division, major, lower or upper division)
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. BOX 9-3 Equity Dashboards at University of Nebraska-Lincoln At the University of Nebraska-Lincoln, every instructor can access course specific dashboards through a customized link embedded in the course software.
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Box 9-4 shows a third example of equity dashboards that demonstrates how a tool can provide customized results for each instructor within a 24-institution system, the California State University system. It shows instructors disaggregated course outcomes along with any equity indicators observed in their data.
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. FIGURE 9-4-1 Disaggregated data on DFW course grades.
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For example, predictive analytics could be used to identify students needing additional support or to label or exclude students. Equity gaps in student performance could be used to identify instructors needing additional support or professional learning opportunities, or to judge and punish.
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The tool was designed with the intention of providing information that might inspire empathy and motivate course change efforts that could improve overall student learning and equity. All users only have access to the courses they have taught.
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Here we illustrate some of the interconnections across academic units that help explain why course-level data might be of interest to institutional leaders (Figure 9-2)
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NOTE: Data can beData collectedcan be collected and analyzed and at the level analyzed of individual at the instructors, at alevel courseof individual level, instructors, as connected between at a course courses level, in a sequence, as connected within between an academic program, courses how courses in abetween connect sequence, within programs anaggregated as well as academic program, at how courses the level of related connect academic units between and/or the programs, institution. as well Source: Committee as aggregated Generated.
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such as sense of belonging and departmental climate can be part of a campus-wide data collection effort that is distributed back to departments and instructors and teaching and learning centers can help gather and interpret qualitative data that can help an instructor make instructional corrections when they still have the chance to impact student outcomes. In all these instances, departmental and institutional leaders can create the expectations and infrastructure for these forms of data to be normalized throughout academic units and the institution.
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VALUE AND REWARD TEACHING Recognizing and rewarding faculty for implementing equitable teaching practices is essential to achieving equitable and effective undergraduate STEM education. The institutional reward system can support and encourage such teaching practices at the classroom and departmental levels by valuing teaching activities and rewarding equitable and effective teaching.
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For example, there is a significant amount of work that takes place before a course begins, such as planning a syllabus to include selected source materials, creating assessments and learning progressions, preparing course materials, and engaging in professional development. While students will see the finished products of these efforts, the reasoning behind them may not be evident to a student; yet the approach to these planning steps may indeed distinguish higher and lower quality teaching.
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While the actions related to implementing a holistic teaching evaluation approach will take place at the department level of an institution, the work that will be required to formulate and use these practices will need support from institutional leaders. In fact, reform of faculty evaluation in ways that
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can analyze and reform policies and practices so that the institutional reward system for faculty, instructors, and academic unit leaders is aligned with the goal of equitable and effective teaching and all stakeholders are supported in change efforts. Conclusion 9.2: Institutional change is an ongoing process of continu ous improvement that can include (a)
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