Platinum 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

## Historical Analysis

This analysis type projects demand for courses in the analysis term by conducting a linear trend of enrollments over up to five past "like" terms. This provides non-student-specific quantitative trending information from historical demand. In other words, how many students took x courses in x terms?

### 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

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## 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 student-by-student 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 at-risk 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

**Critical Path: ** is a series of dependant (pre-requisite) courses that a student must take in order by a specific term to graduate on time.

**Requirement Rating:** the likelihood that a student needs to take a particular course at some point during the completion of his/her program. This value could also be described as the pure probability and is used in the calculation of an adjusted probability.

**Probability:** the likelihood that a student will take a particular course during the analysis term. This adjusted probability is derived using the Requirement Rating, as noted above, and a suggested student credit load. Program Analysis applies the suggested student credit load to the courses that are required in some way by the student, and he or she is eligible to register for them. The sum of the Probabilities for all students for each course is the global "result" or projected need of the program analysis type.

### Strengths

- Assesses each actual student’s course needs in his/her academic program
- Reflects pent up demand
- Reflects changes to the student population
- Objective and not skewed by existing practices and culture

### Weaknesses

- Difficult to calculate
- Requires assumptions regarding data sample of analysis students
- Assigns an 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

**Program Analysis Process**

- 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.)

- Identify “active analysis students” progressing from the prior term to the analysis term (students that have not graduated and are active based on user-defined fields.)

- Generate “simulated students” for the analysis term (students who are not currently enrolled but are expected, such as new and transfer students by the program, major, and level.)

- Import and apply program rules for the progressing analysis students.

- Import academic history, test codes, and other student-specific data for progressing analysis students.

See Student Progress Analysis for step 6-9

- Apply academic history for progressing analysis students from completed degree audits.

- Assign program rules and academic history to simulated students from a representative of progressing analysis students.

- Identify unmet program rules for each analysis student.

- 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.)

- Assign an estimated credit hour load to all analysis students.

- 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 a requirement rating of 100%). If an analysis student has registered for the analysis term, Platinum replaces any helpful courses and their probabilities with actual registered courses.

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 students 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 the 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

## Planner Analysis

While degree audit data has been an excellent source to determine student demand, the amount of choice inherent in degree audit inhibits the ability to predict the set of ‘next up’ courses for a student. As institutions implement student-facing planning systems and pathways (term by term maps of requirements), this data can be used to narrow the focus of what institutions would ideally like for students to take. This analysis type layers data based on the sources that are available. If planner data (student has indicated his/her choices) exists, then it is used first. Otherwise, student academic history is matched to the pathway to determine which courses the student needs to take next.

### Strengths

- Assesses each student’s course needs in his or her academic program
- Reflects pent up demand
- Reflects changes to the student population
- Prioritizes students’ actual desires in the schedule OR prioritizes the ideal path the institution would like the students to take

### Weaknesses

- Requires student planner or pathways from the institution
- May require significant institutional change if the institution’s historical offerings do not match the way pathways were designed

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