AACRAO: Undergraduate Class & Academic Program Demand Practices

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AACRAO and Coursedog collaborated to survey 330+ institutions on the state of projecting course and academic program demand in higher ed. This report presents the survey results and includes findings such as to what extent data is used to inform academic offerings, types of data used, and more.

  • The percentage of institutions that use data to inform course and program offerings
  • Barriers to performing demand analysis
  • Links between academic demand analysis and institutional initiatives


The July 2022 60-Second Survey invited undergraduate-serving members to share how, if at all, their institution uses tools and data to project class and/or academic-program demand at the undergraduate level. There were 3311 responses representing undergraduate-serving institutions or systems of higher education in six countries (Appendix A). One of the more curious items to come from deploying this survey was a discussion about the definition of the word course in the context of higher education.

Often in higher education, we use the terms course and class interchangeably when talking about the practice of creating a schedule2 with specific days, times, delivery modalities, and locations for courses/classes. In addition, however, there are at least two other common working definitions of the word course. The first applies to catalog-level information and the second to a set of classes or a program of study used to obtain a credential. In this survey, all instances of the use of the word course were intended to mean class as defined below.

Working Definitions

To aid in a common understanding of the data from this survey, the following working definitions were used to interpret the data and shape this report. These definitions will also carry forward to the upcoming benchmarking survey on class scheduling practices and evolve as needed based on feedback.

  • Program: This is an interchangeable term with the following: major, program of study, program area, or course of study. It is intended to represent a distinct set of courses (using the definition of course below) and includes practica and internships that a student must complete to obtain the educational credential sought.
  • Course: The details reside at the catalog level and include information such as title, level, description, course learning outcomes/performance objectives, pre and co-requisites, type, etc.
  • Class: A course from the catalog that has been built into a schedule and available for registration for a particular term. A course may be offered as a class several times a term. Section: Some institutions use the word section in their schedule to differentiate more than one instance of the same class in the schedule offered at the same time. Others use section as a stand-alone term meaning the same as the definition of class above.

1 312 represent U.S. institutions. This number is representative of degree-granting undergraduate-serving institutions in the United States at the 90% C.I. and 5% margin of error. Respondents were not required to answer all of the questions, and some chose to skip some questions.

2 The class schedule may be identified as a class timetable at some institutions.

Key Data

  • Projecting class and/or academic-program demand: There is no statistical relationship between institutional characteristics (size, type and control) and whether an institution conducts class and/or academic-program-demand analyses.
    • 27% do not conduct class or academic-program-demand analyses (n = 89).
    • 38% conduct class and academic-program-demand analyses (n = 127).
    • 28% conduct only class-demand analyses (n = 92).
    • 7% conduct only academic-program-demand analyses (n = 23).
  • Projecting class demand: Among those who project class demand (n=219)3:
    • 90% reported using registration data from a previous term as a source of data for projecting class demand.
    • 58% responded “somewhat” to the question, “To what extent does your institution have access to data needed to project class demand?”
    • In response to the question, “Why is using data to forecast class academic program demand important to your institution?”
      • 86% selected the response choice, “More accurately predict the resources (e.g., instructors, physical space) needed to offer the necessary classes.”
      • 88% selected the response choice, “Ensure students can access the classes they need and keep them on track for timely completion.”
    • 45% reported that lacking the appropriate tools to analyze data prevents their institution from using data to forecast class demand more accurately.
    • 37% reported using a purpose-built software solution to help project class demand (Appendix B).
  • Projecting academic-program demand: Among those who project academic-program demand (n=150)4:
    • 88% reported using the number of students currently enrolled in a program as a source of data for projecting academic-program demand.
    • 60% responded “somewhat” to the question, “To what extent does your institution have access to data needed to project academic-program demand?”
    • In response to the question, “Why is using data to forecast class academic program demand important to your institution?”
      • 84% selected the response choice, “More accurately predict the resources (e.g., instructors, physical space) needed to offer the necessary classes.”
      • 83% selected the response choice, “Ensure students can access the classes they need and keep them on track for timely completion.”
    • 44% reported that lacking the appropriate tools to analyze data prevents their institution from using data to forecast class demand more accurately.
    • 37% reported using a purpose-built software solution to help project academic program demand (Appendix B).
  • Barriers to projecting class or academic-program demand: Among those who do not conduct any analyses of class or academic-program demand (n=89):
    • 54% reported that lacking the appropriate tools to analyze the data prevents their institution from using data to forecast class demand more accurately.
    • 45% reported not having the appropriate tools to analyze the data.
    • 27% reported that “Institutional leaders don’t feel data are necessary for building the class schedule.”
  • Differences between undergraduate and graduate practices: Among those who use data to project undergraduate-class and/or academic-program demand and who also enroll graduate and/or professional students:
    • 40% reported that the graduate and/or professional practices differ from undergraduate practices.
    • 42% reported the practices are the same.
    • 18% either did not know or were unsure if the practices were the same or different.

3This number includes “yes” to “both class demand and academic program demand” as well as “yes” to “class demand”
4This number includes “yes” to “both class demand and academic program demand” as well as “yes” to “academic program demand”

Data are displayed in the aggregate in the figures on the following pages. If you have any questions or would like to see the data disaggregated by institutional characteristics, please contact Wendy Kilgore, AACRAO Director of Research, at wendyk@aacrao.org.

Undergraduate-Class Demand

Other undergraduate-class-demand-data sources listed by respondents5:

  • A variety of "pieces" of data are used, but there is no overall "demand" tool or formula being used
  • Academic department information
  • By cohort in sequential programs
  • Class bidding patterns
  • Currently working on getting this data out of the student planner, but not yet there
  • Enrollment advisory board
  • GEN ED Seats
  • High school subject GPA
  • Labor statistics on # unfilled jobs and projections
  • Number of First Year and Transfer admits
  • Number of projected admissions for first-year class seats
  • Our colleges may use other data
  • Program review
  • Programs/classes needed for promotion
  • Student interest as indicated on FTIAC application
  • Student opinion
  • Student surveys on class instructional modality preferences
  • Student-specific factors such as placement levels
  • We use a pre-survey for demand in classes we plan to offer and adjust based on student responses
  • Withdrawn and repeat demand

5 These responses have been lightly edited for grammar and to remove any identification of a specific institution.

Undergraduate-Academic-Program Demand

Other academic program demand data sources listed by respondents6:

  • Enrollment in feeder classes for specific programs/majors
  • In addition to institutional data, outside consultants provide market analysis.
  • Labor statistics
  • National industry demand data
  • Number of students enrolled compared with labor status on number of unfilled jobs

6 These responses have been lightly edited for grammar and to remove any identification of a specific institution.

Undergraduate-Class-Demand and/or Undergraduate-Academic-Program Demand

Other factors preventing using data to more accurately forecast undergraduate-class demand and/or academic-program demand among those who currently conduct analyses7:

  • Academic leaders want to be in control of what classes/sections are offered
  • Academic leadership that will enforce the predicted results; still too much catering to faculty needs rather than student/curriculum
  • Adjusting and maintaining academic plans in the degree audit system is regarded as cumbersome and time intensive. There is not as much adoption as we would like.
  • Advisors are not required to build plans for students
  • Budget
  • Budget - we have the interest just not the capacity or tools
  • Budgetary constraints
  • Capacity concerns are not an overall issue with many programs.
  • Changes in demographics each year due to enrolment needs at the micro-managed level
  • Collective Bargaining
  • Complete understanding of the necessity to create complete educational plans for students
  • Complex program requirements
  • Creating and continually updating student plans is not sustainable. There does not appear to be a degree planning tool that is robust enough to dynamically adjust degree plans when students need adjustments to them.
  • Deans and others creating the schedule might have the data but may not be using the data to make informed decisions or may be opting to use their own historical thoughts on enrollment to drive decision-making.
  • Decentralized
  • Departments disinterested in using data
  • Difference of opinion on the meaning of data
  • Encouraging Deans and Department Chairs to effectively use available information
  • Faculty preference in what classes are offered and when
  • Forecasts are only estimates. Accuracy depends very much on students' plans/actions, which are subject to changes and various other factors.
  • Implementation of Plan tool. Waiting on SIS provider.
  • Institutional commitment to allow any of our students to take any of our majors means we have to make room when predictive models fail
  • Issues with class sequencing
  • Just implementing Ad Astra and managing the culture change associated with it
  • Lack of faculty trust in degree planning data
  • Lack of focus on compliance with institutional goals
  • Lack of IT resources to implement a software solution; Institution eliminated the systems positions in the Registrar's Office so we cannot implement without IT
  • Not all students submit academic plans
  • Not everyone uses the data. For class demand, some departments use it while others do not
  • Organizational structure makes no sense. Deans are not incentivized to meet class demand.
  • Poor class scheduling practices
  • Reluctance to depart from old ways of doing things/maintaining status quo
  • SIS software constraints, budgetary constraints for purchasing a new software
  • Still learning new tools
  • Time
  • We do not currently collect information about students’ future demands/needs/plans.
  • Willingness of colleges or departments to change offerings

7 These responses have been lightly edited for grammar and to remove any identification of a specific institution.

Other factors preventing using data to more accurately forecast undergraduate-class-demand and/or academic-program demand among those who currently do not conduct analyses8:

  • Advisors actively encourage students NOT to plan for future semesters, thus thwarting our data gathering
  • All of our programs are profession-specific, and that industry is what drives curriculum and program changes.
  • Budgetary Concerns
  • College offers one nursing curriculum that all students must complete for the degree. More applicants than seats available.
  • Cost
  • Decentralized nature of scheduling and enrolment activities
  • Dispersed data sources
  • It doesn't really matter what the data says if you don't have the ability to hire additional faculty.
  • Just implemented a new SIS, which will assist in this type of data collection.
  • Lack of leadership making a final decision
  • Lack of software to collect the data
  • Not required - very small school
  • Our BA & MA programs have prescribed classes that are based on curricula and integrated curricula.
  • Outreach to students to teach them how to use it
  • We are getting there as an institution that will be receptive to using this data due to decreased student numbers.
  • We do the best we can with limited resources and staffing capacity
  • We guarantee specific programs regardless of the number of students in the program, so often classes are offered regardless of the demand.
  • We have one program for all students
  • We implemented a software solution, but after working with the academic units on employing it, they simply wouldn't adapt to it. We ended our software contract.
  • We just implemented software and will begin using Fall semester
  • We offer only specific classes
  • We've never done it before, and it wasn't on anyone's radar to do.

8 These responses have been lightly edited for grammar and to remove any identification of a specific institution.

Appendix A: Institutional Characteristics

Country Count
Bulgaria 1
Canada 15
Cote d'lvoire 1
Singapore 1
United Arab Emirates 1
United States 312
Grand Total 331

Size Count
Under 1,000 52
1,000 - 2,499 1587
2,500 - 4,999 60
5,000 - 9,999 42
10,000 - 19,999 44
20,000+ 45
Not Applicable 1
Grand Total 331

Size Count
Public 156
  Lower Division Only
  Undergraduate, graduate, and/or professional
Private, not-for-profit 168
  Lower Division Only
  Undergraduate, graduate, and/or professional
Private, proprietary 7
  Lower Division Only
  Undergraduate, graduate, and/or professional
Grand Total 331

Appendix B: List of Class and/or Academic-Program-Demand-Analysis-Software Solutions Used9

  • Ad Astra
  • Ad Astra
  • Ad Astra
  • Ad Astra
  • Ad Astra
  • Ad Astra
  • Ad Astra
  • Ad Astra Analytics, EMSI Career data, internal dashboards, and queries
  • Ad Astra Predict and Align
  • Ad Astra Predict/Align/Monitor
  • Argos, Access
  • Astra Analytics
  • Astra Schedule
  • Banner, Argos, Data warehouse, degree works, college scheduler
  • Banner, PowerBI, Excel
  • Burning Glass
  • Civitas
  • Coursedog
  • Courseleaf, Degreeworks
  • Courseleaf, SIS
  • Colleague
  • Colleague
  • Colleague
  • Colleague; Student Planning
  • CollegeNet 25Live, Ellucian Colleague data
  • Data warehouse, Excel, Tableau, Vera Predict
  • Degree Audit
  • Degree Audit from Colleague
  • Degree Planner and in-house Data Dashboards pulling from our Data Warehouse
  • Degree Works
  • Degree Works and soon Courseleaf CLSS
  • Degree Works reporting is used to determine class demand based on audits. This is just in its infancy.
  • Degree Works SEP
  • Degree Works Student Educational Planner
  • Degree Works, Ad Astra
  • EAB
  • EAB Analytics; Coursedog; reporting from SIS
  • EAB APS data, Emsi, EduNav
  • EAB Navigate, Colleague, Coursedog
  • EAB. Colleague Advise
  • EAB's Academic Performance Solutions (class Planning Optimization, Dept & College Analytics, Program Analytics)
  • Ellucian banner, power BI, Cognos reporting tools
  • Ellucian Colleague Student Planning
  • Ellucian Colleague's Student Planning (just implemented, however)
  • Ellucian provided Student Planning module
  • Ellucian's Self Service: Student Planning
  • Empower Student Information System
  • Event Management System (EMS)
  • Excel, Access, Smart Planner (Degree planning tool)
  • Gray & Associates product
  • Greys for program demand
  • Homegrown
  • Homegrown application
  • Homegrown data warehouse
  • Homegrown spreadsheets that live off Cognos reports from SIS
  • Informer/Colleague
  • Internal data warehouse; SIS; SAS
  • Jenzabar
  • Jenzabar
  • Jenzabar J1 Degree Audit, past enrollment, enrollment in majors by class year, etc.
  • Jenzabar One
  • Jenzabar, InfoMaker
  • Locally built applications - salesforce - UAchieve
  • OASIS, Canvas
  • PeopleSoft, DegreeWorks, Schedule Builder
  • Power BI, Banner
  • Smartplanner
  • Stellic (currently implementing)
  • Student Planning (Ellucian Colleague)
  • Student Planning (Ellucian)
  • Tableau
  • Tableau, Argos
  • This is a custom solution that is part of our custom SIS
  • Visual analytics
  • We own the CollegeSource planner but are not using it efficiently

Appendix C: Other Comments About Undergraduate-Class and/or Academic-Program-Demand Analysis10

  • Can't wait until we can prioritize getting resources to be able to get software and staff support specifically for this. The day is coming. There is growing consensus that our current hard-scrabble methods aren't sufficient in their consistency or accuracy, and growing awareness that this is a solvable problem with the right resource investment.
  • Class demand analysis is a function of academic affairs and is not conducted. Program demand analysis is a function of enrollment management and is prioritized.
  • Class scheduling is faculty centric. Faculty preferences for what they teach, when they teach, and enrollment caps on classes is the primary driver. Academic Department Chairs and Associate Deans then have to NEGOTIATE with faculty for any changes.
  • Complicated process to gather necessary information and difficult to mandate change on the faculty level.
  • Data and market analysis it a great tool to determine if an institution is meeting the needs of the state or region within the state it serves. We should be utilizing it.
  • Departments and colleges also do their own analysis.
  • Easier to predict the major-level classes versus the other degree requirements (gen ed interest, electives)
  • For community colleges with open admission, and delayed enrollment cycles many students register very late in cycle and time to respond is limited ...additionally many students drop classes early in the semester
  • Institutional culture around what classes to offer and how many seats is often based on preference and historic scheduling. Data is often provided but not used as expected to plan section offerings in all faculties. Some use data really well while others do not.
  • It is left to departments to assess their own demand and adjust accordingly. Conversations are just beginning on our campus to consider a more strategic approach.
  • It's very difficult to forecast accurately. In any case, supply may not be able to adjust to demand quickly/effectively.
  • Lack of budget funding to implement software applications. Lack of applications that integrate with J1.
  • No money to buy software to help with this endeavor.
  • Our institution does not use class/academic program demand analyses.
  • Our university is highly decentralized, [with] each School and College doing their own class and program demand analysis.
  • Programming at our institution is lock-step for students and/or cohort-based modeled class offerings. We use degree audit for one program, but it is cohort-based; therefore, class program demand is based on the requisite knowledge/skills of the cohort against the degree audit of their program.
  • Serious discussions are now happening about program and class demand. We are a faculty-led institution, and usually they are not receptive to decreased class offerings. However, that is slowly changing because of decreased student enrollment across the nation.
  • Since graduate and professional programs are typically much more prescribed, this analysis tends to be much simpler and more expressive than for undergraduate first-degree programs.
  • Since we are just now beginning the process to use software to help determine demand, I am unsure if our practices will be different between undergraduate and graduate programs
  • Sometimes teaching obligations such as contract agreements are considered over student demand to run a class.
  • The Academic Departments and Dean's Office provide a great deal of input, both quantitative and qualitative, to the Provost's Office to determine class and program demand. Also, our Office of Institutional Research provides data to support these explanations/analyses. Data query software includes CROA and Informer.
  • This is something we are putting energy and resources toward right now. I would consider our institution to be in the toddler stage. We've been focused on classes for a few years, but now bringing that attention to new degree development and determine where resources can be best utilized.
  • This single issue has been the most frustrating in my career. A lack of data out of degree audit, the unwillingness of the institution to require a planner, and the lack of collegiality of all deans to meet class demand is beyond frustrating and is the single biggest issue of student frustration.
  • U-grad is more complicated, per the length of the programs and the gen ed classes used by all. Grad level is a more specific set of classes (and fewer of them), so it is easier to see the flow of students through those grad programs.
  • We anticipate purchasing and implementing software to help us gather demand data (as determined by student academic plans) this FY.
  • We are a health science center, and our programs are very profession specific. Those professions and programmatic accrediting bodies dictate class and academic program demands.
  • We are currently engaged in a project to better understand our data needs in this area.
  • We are currently exploring technology solutions to help understand the student demand (planned classes) data component. Currently, we can leverage data in our systems to determine how many students need a particular required class but not data about which term they intend to take the class or which electives they would like to enroll in.
  • We are currently implementing a degree audit for this fall and have hopes that we will have more and better data to analyze.
  • We are currently implementing Stellic to assist with this, but we don't have any results yet.
  • We are currently in process of purchasing and implementing new tools to help us to gather data for more effective analysis and projections.
  • We are going live with Coursedog for the Spring 2023 class offering and hope in the future we will be able to gain analytics through Coursedog to help with class projection.
  • We are launching an RFP later this year to implement tools to aid in this important work.
  • We have a home-grown class planning program that is helpful to an extent. We spent a bit of time exploring the Student Educational Planner in DegreeWorks this spring and found it to be too advisor time-intensive with a high potential for error. We are doing a bit of testing with CAP in DegreeWorks, but we haven't gotten far enough into testing to determine if this is a viable option yet.
  • We have recently set up reporting capability from academic plans from our degree tracking software, so once we have students completing academic plans with some critical mass, we should be able to start forecasting class demand with this new forward-looking data versus always looking backwards.
  • We have the tools available, [but] there is a reluctance to change at the institution. The administration does not want to force faculty to change their methods.
  • We offer the same classes term over term so the only need is for section size management.
  • We recently started using a new registration product that will better predict student class demand. Changes are coming, but unsure what they will look like in relation to this questionnaire.
  • We tend to be behind the curve - it's always reactionary instead of proactive.
  • We've been having this conversation frequently. Currently, there are staff building models based on past enrollment, but nothing in place.
  • We've just started using Student Planning for Ellucian Colleague (Educational Planning Tool), which will provide us with additional data.
  • While my institution thinks that getting and using the data would be extremely beneficial, there is no fiscal way to add another intricate planning process. If we added something like this, we would not have the staff to accommodate the time needed to use the software correctly, and our institution is NOT willing to add extra staff, only add more to multiple people's plates.
  • While using a solution to predict demand, it is up to each department/program to create their schedule of classes. This distributed responsibility has not led to a demand for such a solution.

10 These responses have been lightly edited for grammar and to remove any identification of a specific institution.

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