

Analytics assists academic institutions in course schedule building by providing data on students’ need for courses through a variety of analysis methodologies. The following analysis types are used by Analytics to estimate demand. Using weighting applied by the institution, the analysis engine evaluates the data using these techniques and presents the results in order of highest impact changes to lowest.
Baseline Analysis This analysis type assesses student demand for courses by comparing enrollments for course offerings in the analysis term to those in the last selected “like” term.
Strengths:
•Relatively quick and easy to calculate
•Grounded in fact
•Relies on data from a recent, similar term
•Maintains existing practices and culture
•Minimizes politically challenging changes
Weaknesses:
•Does not reflect pent up demand
•Does not reflect changes to student population
•Demand is skewed by existing practices and culture
•Analysis is limited to data from a single term
**Emphasizing this analysis type will produce few suggestions for changes. Only very large deviations in seats offered versus projected need will be highlighted.
DEFINITIONS:
Pent up demand is the demand for a course that may not be evident without reviewing student program rules. For example, if 150 nursing majors need to take a required biology course and the institution offers 100 seats per term, 50 students are prevented from moving effectively on their degree paths.
Seats offered is the total of the maximum enrollment values on each section for a particular course for the analysis term.
Projected need is the seats needed to meet demand as determined by each analysis type. The overall projected need is the weighted average of the analysis type(s) used to calculate demand.
Baseline Analysis Process:
1.Import section data from "last like" term (most recent term that is deemed to be similar to the analysis term, e.g., Fall 2009 is the last like term for the analysis term of Fall 2010)
2.Summarize totals for census (if available) or actual enrollments and seats offered by course 
Historical Trend Analysis This analysis type assesses student demand for courses by comparing enrollments for course offerings in the analysis term to those in multiple selected “like” terms. This provides nonstudentspecific quantitative trending information from historical demand. In other words, how many students took x courses in x terms?
Historical Trend Analysis can also provide information on student demographic subpopulation trends. Drilldown details can help validate the quantitative trending by showing historical justification for the data, including specific student enrollment information. This component looks at student availability based on what was taken in the past or what is gathered from the student profile. It can be used to infer intent and highlight subjective course selection trends, including favorite instructors, subjects, courses, etc.
Strengths:
•Data is based on multiple academic terms
•Grounded in fact
•Relies on data from terms that are similar to analysis term
•Reflects enrollment trends
Weaknesses:
•Does not reflect pent up demand
•Only reflects past changes in student population
•Demand is skewed by existing practices and culture
**Emphasizing historical trend analysis will provide feedback to follow previous scheduling tendencies while allowing for growth based upon trends. Historical trend analysis is most useful for elective, continuing education, and freshmen course offerings. This analysis type is also very useful in determining oversupply for courses.
Historical Trend Analysis Process:
1.Import section data from multiple "like" terms (recent terms that are deemed to be similar to the analysis term, e.g., Fall 2006, Fall 2007, Fall 2008 and Fall 2009 are like terms for the analysis term of Fall 2010)
2.Summarize totals for census (if available) or actual enrollments and seats offered by course and term
3.Calculate trend of enrollments and seats offered by course 
Program Analysis Program Analysis looks at individual, active students’ academic career progress to determine those courses that students are eligible to take in the analysis term and that will satisfy currently unmet program rules. Course demand is assessed by inferring the probability of students taking these courses.
Program Analysis requires access to your degree audit system to capture the various rules and requirements of your programs. Using this information, Program Analysis provides an objective statistical summary of demand by program fulfillment. Looking at studentbystudent program requirements and degree progress, Program Analysis helps to determine the likelihood that a student will take a specific course, highlights courses that are in the critical path to graduation, and identifies students atrisk for not being able to get needed courses. (Note that for program electives, likelihood is simply based on historical demand, as many combinations are possible to satisfy requirements.) DEFINITIONS:
Strengths:
•Assesses each actual student’s course needs in his/her academic program
•Reflects pent up demand
•Reflects changes to student population
•Objective and not skewed by existing practices and culture
Weaknesses:
•Difficult to calculate
•Requires assumptions regarding data sample of analysis students
•Assigns equal value to all courses in a list when student behavior may indicate that certain choices are much more popular
•Potential to support significant changes to schedules that may be disruptive culturally and politically
** An emphasis on this analysis may suggest a great deal of change because it only considers student needs based on degree audit rules and not on historical offerings or course selection tendencies.
Program Analysis Process:
1.Import section data from the analysis term, multiple like terms, and “prior” terms (terms in which students typically participate in prior to the analysis and like terms, e.g. Spring 2010 is the prior term for Fall 2010)
2.Identify “active analysis students” progressing from the prior term to the analysis term (students that have not graduated and are active based on userdefined fields)
3.Generate “simulated students” for the analysis term (students who are not currently enrolled but are expected, such as new and transfer students by program, major, and level
4.Import and apply program rules for the progressing analysis students
5.Import academic history, test codes, and other student specific data for progressing analysis students
6.Apply academic history for progressing analysis students from completed degree audits
7.Assign program rules and academic history to simulated students from a representative of progressing analysis students
8.Identify unmet program rules for each analysis student
9.Identify ‘helpful courses’ for each analysis student (courses that can satisfy unmet program rules and that the analysis student is eligible to take in the analysis term)
10.Assign an estimated credit hour load to all analysis students
11.Calculate the probability that each analysis student will take each helpful course in the analysis term by distributing the estimated credit hour load in a way that prioritizes courses with the highest requirement rating (an absolute requirement has requirement rating of 100%). Note: If an analysis student has registered for the analysis term, Platinum replaces any helpful courses and their probabilities with the actual registered courses.
12.Summarize probabilities to assign a calculated total projected need by course. 
Predictive Program Analysis The traditional program analysis results are objective in the sense that each course in a particular rule will be given equal weight in determining the statistical likelihood of meeting a particular requirement. In reality, some courses in rules are much more popular than others. For example, if an institution requires ENGL 100 or 101 or 102 for all students, traditional program analysis will give student a 33% likelihood of taking each one of those courses. In reality, the institution offers many more sections of ENGL 100 so the majority of the students use that course to fulfill the requirement. Predictive program analysis is a way to combine the value of program analysis with the value of historical trend analysis to determine a more realistic projected demand for courses for certain institutions.
Strengths:
•Assesses each student’s course needs in his or her academic program
•Reflects pent up demand
•Reflects changes to student population
•Accounts for historical tendencies including subjective course preferences for students and offering preferences for institutions
•Potentially less disruptive than traditional program analysis in that the offering tendencies are reflected
Weaknesses:
•Difficult to calculate
•Requires assumptions regarding data sample of analysis students
•Potentially perpetuates offering tendencies that are restrictive to students
•Does not account for individual students’ subjective course preferences
** Emphasizing the predictive program analysis type allows an institution to blend the change suggestions provided by traditional program analysis with the historical offerings for a schedule that may better align with student need and institutional comfort. 
Student Survey Analysis (coming soon) 
Student Survey Analysis The Student Academic Planner Analysis assesses course demand through a direct online survey of individual students on their desired/needed courses and availability for an upcoming academic term. The combined survey data feeds into the Platinum Analytics analysis run to provide key insights from the student perspective. DEFINITION:
The Student Academic Planner is a tool that allows students to provide insight into their intent:
•Do they plan to graduate, transfer, complete a few courses, etc.?
•Do they plan to take courses full time or part time?
•Do they plan to enroll in all semesters including summer school?
•Do they have specific day/time preferences in completing courses?
It also leverages program analysis to guide students in creating comprehensive academic career plans that can provide key information to students, advisors, and administration. The tool supplements degree audit tools in providing the same information in a more userfriendly format allowing students to provide terms they plan to take courses and which courses they would like to take including requirements and electives. For students that complete this process, there is a clear answer to the complex questions of what to take, in what order, and over how many terms.
As a result of the planner, institutions get demand data on both program requirements and desired electives in addition to elusive information on students’ academic career goals. This intent can be used to measure student success against student goals on a studentbystudent basis.
Strengths:
•Facilitates direct course needs and desires through student input
•Assesses each student’s course needs in his or her academic program
•Reflects pent up demand
•Reflects changes to student population
•Objective and not skewed by existing practices and culture
Weaknesses:
•Requires business process change to gather data
•Requires manipulations to results if data sample does not include all analysis students
•Potentially promotes subjective desires versus objective needs
•High potential to support significant changes to schedules that may be disruptive culturally and politically
** The Student Academic Planner Analysis can be used to emphasize student input in the overall process. This analysis is highly recommended. 