As an educational researcher by profession and education, a degree of intersectionality exists in my considerations surrounding work, and learning- both of which take place in a school in my case, though my specific venue is the City University of New York. The focus on data utilization and accountability has a long and politicized history. Recent innovations in data collection, curation and access have helped balloon educational data into yet another juggernaut that faculty and staff must battle. Once largely immune to standardized assessments, higher education is now confronted with similar scrutiny as primary and secondary education, especially in that which is publicly funded. The use of standardized testing to assess student learning and even predict students’ futures as scholars and workers dates back to the start of compulsory education in the early twentieth century, when administrative progressives promised that civil unrest and other conflicts and contradictions surrounding capitalism could be mitigated, if not obviated, by the use of a tracking system, that cordoned students into career-guided learning paths based on their test-determined aptitude (Tyack, 1974).
To that end, a decade-long educational journey in K-12 ends at work or detours into higher education, for specialization and heightened acculturation, before returning to the road to work. While there is no No-Child-Left-Behind-style set of incremental test-score gains tied to higher education funding, a college’s funding is tied to its enrollment which is a function of its perceived utility in setting the stage for successful adulthood. The marriage of business and higher education was strengthened by the wave of disinvestment that accompanied the 1970s fiscal crises, and the fiscal austerity that followed (Fabricant & Brier, 2016) .
In these first decades of the twenty-first century, technology is forcing a standard of transparency on the practices of colleges which were heretofore hidden behind the mystique of academic expertise. This tension between the niche-guru status of a traditional academic and the desire for interdisciplinary studies voiced by students plays out in the projection of medieval guild style academic programs (complete with robes and rituals) onto these ersatz new fields. Rather than offering a complex and interrelated venue to demonstrate analytical and synthetic prowess, students are made to focus on a more diverse, but rigid, canon that restricts the bounds of intellectual integration (Ferguson, 2012). Thus, academia displays none of the dexterity and flexibility interdisciplinary studies require, this rigidity is also apparent in its newer role as a conduit to gainful employment.
Academe has positioned itself as the force that will bridge the gap between itself and industry. It has also stationed itself as the gatekeeper of access to salaried positions, which are growing scarcer across all industries. With these dual roles and in the service of so many masters, higher education habitually sins against its own. While the humanities and social sciences have been gutted by many public colleges and universities under the aegis of creating “work ready” graduates; the leaders of these same schools also bristle at the existence of proprietary schools, naming them mere “diploma mills.” But academia is always behind, resistant to change and only willing to adopt new strategies that can bolster the repetition or the reputation of existing epistemologies (Ferguson, 2012).
In Marc Bousquet’s 2008 monograph this role is delved into the quandary of the modern university. Academia sets the standards that the rest of the world imitates, he argues that the casualization of labor, and the perpetual withholding of status positions is at the center of the university’s business model. Bousquet’s sentiment echoes Ferguson, the university is able to incorporate its contradictions and turn the potential for dereliction into a nurturing element. The closer higher education’s practices and priorities grow to capitalism the finer this trait will be honed. Capitalism and higher education are two rare specimen that can subsume its paradoxes through creative destruction and even benefit from the insuring turmoil, and reordering.
Tension between administrators and faculty have long played out in a simplistic “us vs. them” dichotomy that serves to diminish the potential for consensus and collective action. Insouciant faculty are complicit in the exploitation of their colleagues, as this benefits them by diminishing their introductory and intermediate course load, and making substitutions easier. While other (usually more junior) faculty object within the limits of their own vulnerability, the use of graduate students to bolster the faculty ranks, and the subsequent retention of post-doctoral faculty long enough for their part-time and meagerly compensated employment to be counted as post-graduation employment outcome success is highlighted in How the University Works (Bousquet, Nelson, & Nelson, 2008) .
Within this historicized but contemporary context come the rationale for this project: the track from higher education to employment is a central issue in the twenty-first century. Whether at an online proprietary school, a community college, or an elite university where one end up after graduation is a primary consideration. Higher education has become synonymous with job-preparation, a fact that is richly exploited in the use of part-time labor at colleges. The conflation of credentials with learning is a necessity of universal education. Not until recently did the assessment of learning outcomes enter higher education. Licensure programs must align to industry standards. Beyond the roles such as doctor, lawyer, nurse etc. that require a specific skill set, and a demonstrable repository of supporting knowledge (Baynes, 2006); higher education has taken over the job preparation and training duties that once represented the investment a firm was willing to make in taking on a junior colleague regardless of their prior trade or training. More broadly still academic fields must now demonstrate that they pass on vaguely defined skills such as “critical thinking.” Little is more contentious and consequential as the alignment of learning to employment outcomes. Unfortunately, academia has, for many onlookers, lost its hallowed veneer. With tuition costs inflating rapidly consumers are suspicious of the distance between the marketing and reality of the value of a college degree (Supiano, 2018).
Given these circumstances, the promotional content offered by schools would likely not hold up to the scrutiny and validation inherent to a peer-review process for academic publication. Regardless of ilk or point-of-view, educators are newly developing a relationship with data, especially in its current repletion and ubiquity (Attaran, Stark, & Stotler, 2018) . The duality of higher education’s role as a gatekeeper to and consumer of post-baccalaureate labor indicates that data must be collected from a distance, and with a more aggregable lens. Faculty and administrators make use of the data and analysis, guided by the need to make crucial decisions and policies. The accountability measures imposed on public K-12 schools are quickly climbing up to the level of higher education (Anagnostopoulos, Rutledge, & Jacobsen, 2013) . Humanities and social sciences have been sacrificed at the altar of employment, there has been some investigation of whether these fields are providing students with skills needed in the workforce. There is some discontinuity in both the concept and validation of the concept of the utility of a discipline. Employers and policy makers also express unspecific perceptions of many disciplines, indicating a need to make a more substantive evaluation of how content and prospects align (Edmond, 2014).
The concern with academia’s alignment with industry vis-à-vis employment, is one of the more public of its promises made to stakeholders under the spell of the neoliberal zeitgeist (Bousquet et al., 2008; Fabricant & Brier, 2016) . The National Center for Education Statistics (NCES) is a rare public resource that collects educational outcomes and feedback from across the country. Under the umbrella of the Institute of Education Sciences the data collected by the NCES is available to all, but provides educators a unique opportunity for data-driven insight (Office of Educational Research and Improvement, U.S. Department of Education, n.d.). Faculty creating or retooling curricula and administrators making policy and funding decisions can gain focus on the broader scope of issues with which their institution is grappling via the statistics made available. With this utility in mind, and the issue of the alignment of higher education to employment as a focus the recently published High School Longitudinal Study (HSLS) 2016 update can offer that aggregate perspective (this data will be discussed in detail in the methods section.) This study tracked a cohort of students from around the country through periodic surveys, for those at the forefront of the group post-higher education work outcomes are now available. Big data can engage campus stakeholder in a variety of discussions, yet there are issues with the use of these unwieldy, and complex datasets (Daniel, 2015). First among them is the complexity for even experienced researchers to determine which portions of the data are relevant to the institution’s questions. Secondly, the limitations of big data’s ability to explain or predict case-specific nuances must be fully understood by those hoping to engage with it. Thirdly, stakeholders must be educated around the concept of big data as an opportunity to glean insights from the interplay of key variables, that the relevance of the information is significant but not personal (Daniel, 2017).
Specifically, this historically and critically motivated literature review serves to contextualize need for actual data to hopefully refocused the discussion on what has been historically observed and to remove this issue from the grasp of speculation and suspicion. The aim is not to color the perceptions of the reader, other than to bolster their skepticism to full health. As mentioned above there are some issues with data literacy. The administration of education, whether at the K-12 level, or in postsecondary education requires that decisions be made and policies enacted that can significantly impact the process and outcomes of the learning environment (Fabricant & Brier, 2016) . While policymakers are not directly involved in the mechanics of education as a transfer of information; they can and do act in a broad and deep-reaching capacity that can alter the interpersonal dynamics of the classroom as well as the structure and mission of an institution, and an entire educational system (Kaestle, 1983).
The interest of all well-intentioned policymakers is to use the best data available in decision-making. This data is often incomplete, or not ideally related to the policy issue at hand. This information and its attached analysis come from sources other than the policymakers who must rely on researchers to provide an accurate, verified and applicable assessment of an issue area. At stake in the deliberations that rely on antecedent studies are the educational prospects of students, the freedom and curricular modes of teachers, and the impressions that parents and taxpayers have of the policy decisions undertaken and their outcomes. In recognition of preexisting complexity of their roles, and the responsibility of being an educational researcher, I will create data explorers that produce interactive and approachable aggregations and extracts of the enormous HSLS data. This will be guided towards elucidating the specific issue of how higher education experience (major, GPA, courses taken etc. (please see methods section)) correlates with employment outcomes.
Hopefully, the lack of progress in leveraging available data in the ways that benefit students, staff and faculty by compressing the time taken to gain insight and understand how they can facilitate the transition to employment (Attaran et al., 2018). This project hopes to offer insight based on real outcomes, a rarity in higher education. Even as corporations retool their customer relationship management platforms for use in colleges and promise to predict student success (“How Predictive Analytics is Affecting Higher Education,” 2017) but do not engineer backwards from the post-higher education outcome. This is what I seek to do via an approachable and accessible data explorer and the HSLS data. Stakeholders will experience a richer palette from which to validate their perceptions, and ground theories and changes. The HSLS data explorer I propose will provide an opportunity to delve into an aggregation of real outcomes from the past decade. This will provide a hypothesis free tool to look at the continuity of student experience from high school, through the experience of college and/or work, and even to the frontier of post-baccalaureate work/education divide. Thanks to data, technology and processing power we can explore real life outcomes instead of speculating.