The student credit hour was invented as a tool for smoothing transitions from K–12 into higher education and was reinforced by foundations wanting to encourage business models—including competition and unit‐cost analysis—in higher education.
As institutions seek to shift into more advanced analytics and data‐based decision‐support, many institutional research offices face the challenge of meeting the office's current demands while taking on more intricate and specialized work to support decision‐making. Given the great need organizations have for information that supports real‐time strategic decision‐making, institutions must advance beyond traditional static data reporting offices to modern offices with regular predictive analytics use. The authors believe that institutional research offices should actively engage in contemporary analytical approaches and provide leadership in this area. The following chapter focuses on (a) why higher education should embrace analytics, (b) discusses areas where analytical advancements have occurred, (c) discusses areas where analytical growth is lacking, and (d) provides guidance on addressing cultural changes concerning institutional data use, policies, and practices.
The federal government has collected data from colleges and universities for more than a century. However, how and what data are collected has evolved over time, as has the purposes for collecting those data. The most systematic collection of data happens through the Integrated Postsecondary Education Data System (IPEDS), which is required to be reported by any institution that awards federal student aid. While initially collected to describe the state of postsecondary education in the United States to policymakers, a shift in recent decades has focused on making data available to students and parents to compare institutions. Now, the reauthorization of the Higher Education Act of 1965 is overdue and one proposed change-moving from an institution-level to a student-level data collection-would transform IPEDS. We trace the beginnings of federal data collection from institutions to its current state and discuss how the data collection has evolved over time.
This chapter describes the results of an institutional survey designed to learn how different colleges and universities apply the credit hourwhether they use it to measure credits to graduation, how they define it, and how they determine the number of credits to award for any course. We also did a survey of credits in relation to time in class-that is, the extent to which these institutions continue to use time as one of the primary bases for awarding credits.Working with the Office of Institutional Research at the University of Delaware (UD/OIR), we invited about seventy-five institutions to participate in a survey of their policies and practices with regard to awarding the credit hour. The institutions were drawn from participants in the past six years of a survey administered by the University of Delaware on instructional costs.Institutions were identified by the UD/OIR based on their participation in the instructional cost survey and their reputation for having "good" data management. Participation was voluntary, and the anonymity of the participating institutions preserved. The survey consisted of two parts: one, a request for information about their written policies and procedures for assigning credits to classroom time, and two, an analysis of course data files to evaluate the relation of classroom time to credits awarded. Of the seventy-five institutions invited, fifty-five agreed to participate in providing a course data file, as follows:• Forty-two public and thirteen private institutions • Nineteen research or doctoral institutions
One of the most important and most enduring ways in which classification systems are used is for governmental, system, and institutional policy development. Classification systems are involved in allocating funds, comparing institutional inputs and outputs, and making decisions about faculty, students, and staff. At the national level, establishing a meaningful classification system for two-year institutions that is supportive of and necessary to policy development and research is limited by the extent to which appropriate data for the universe of two-year institutions are collected.In attempting to construct a national system of classification for twoyear institutions, the sources of data are regrettably limited. The most comprehensive of these data sources is the Integrated Postsecondary Education Data System (IPEDS). IPEDS is the core postsecondary education data collection program in the U.S. Department of Education's National Center for Education Statistics (NCES). It encompasses all institutions and educational organizations whose primary purpose is to provide postsecondary education. Data are collected from more than ten thousand postsecondary accredited and nonaccredited institutions, including baccalaureate and higher degree-granting institutions, two-year award institutions, and less-than-twoyear institutions. The IPEDS system is built around a series of interrelated surveys that collect institution-level data on enrollment, program completion, faculty, staff, finances, and academic libraries.In conducting the analysis for this chapter, we have chosen to take an explicitly empirical approach to classification of two-year institutions
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