Two of the most intensive areas of focus in higher education today involve the strategic use of resources and the intentional commitment to student success.  Striking a balance between effective stewardship and increased student service is creating challenges for most institutions in this changing world of higher education.  Fortunately technology is available to enable campuses to determine how students can improve their paths to graduation while bringing about efficiency in use of both faculty and facility resources.

 

Ad Astra’s Platinum Analytics is a patented tool that analyzes historical enrollment patterns, student academic history, and degree audit system rules to forecast the number of seats and sections a campus should be offering for upcoming terms.  Platinum Analytics enables institutions to utilize their own student and course offering data to determine how to provide a course schedule that will meet student need, thereby improving retention and graduation rates and overall student success.

Common Practice

Frequently institutions roll their class schedules forward from previous “like” terms. Although historical demand analysis may be performed using quantitative demand for courses in the last “like” term, sufficient information is often unavailable to inform schedule changes that benefit students. Many institutions simply rely on anecdotal information or faculty preferences to drive the schedule development and refinement process. These methods frequently lead to increased challenges for students who need appropriate schedules to complete their programs, and for campus administration who are seeking ways to use their resources efficiently.

DEFINITION:  “Like” terms are historical terms against which the analytics tool will compare the analysis term (i.e, when an upcoming fall term is being analyzed, it will be compared with 3-5 previous fall terms).  “Like” terms are used for historical analysis and elements of program analysis.

Best Practice

Having a better understanding of student demand for courses can turn schedule-building into a student service operation and a resource management opportunity. Adjusting course offerings and meeting times to reflect student need will help reduce empty seats and costs associated with part-time instruction, heating and cooling, security, janitorial services and other expensive resources. The goal is to maximize the value of each dollar spent on the academic operation, while allowing students the opportunity for on-time program completion.

Evidence-based Decision Making

Data provided by the Platinum Analytics analysis enables institutions to make adjustments to a roll-forward schedule that can positively impact students’ ability to graduate on time, and ensure more efficient use of available resources.  Performing an analysis run in advance of the schedule development period will allow for earlier and improved planning by academic departments and administrators.   Reevaluating the sections and seats per course that are needed each term helps to free under-utilized space that can be used for higher-demand courses. This reallocation of resources not only addresses space bottlenecks, but also allows current students the opportunity to graduate sooner while making room for growing enrollments.

 

Examples of high impact schedule changes include:

 

•Adding a course offering so seniors can graduate on time

 

•Removing an unneeded course offering to free up faculty resources to teach an important undersupplied course

 

•Adding a course offering of an undersupplied course in non-primetime to best utilize classroom space and maximize enrollment ratios

 

•Changing an offering time to correspond with the availability of the students who need it most

 

•Changing an offering time to reduce conflicts between other required courses that students need to take in a given term

The Analytics Process

Platinum Analytics currently uses three types of data to predict student demand for courses and forecast the number of students who have a likelihood of registering for a course in the upcoming (analysis) term.

 

Steps in the Analytics process include:

 

1.Build course sections for an upcoming term (roll forward or new) in your student information system

 

2.Import data including sections, students, and degree audit information

 

3.Perform historical and program analysis

 

4.Analyze proposed offerings with Platinum Analytics data

 

5.Review results and determine high impact changes that may require schedule adjustment

 

6.Produce final schedule or repeat process during the scheduling cycle

Historical Analysis Types

Baseline Analysis assesses student demand for courses by comparing enrollments for course offerings in the analysis term to those in the selected last “like” term.

 

Historical Trend Analysis assesses student demand for courses by comparing enrollments for course offerings in the analysis term to those in multiple selected “like” terms.  A mathematical trend is performed on the data points to determine seats needed.

Student Analysis Types

Program Analysis looks at individual, active students’ academic history/career progress to determine those courses that students are eligible to take in the analysis term that will satisfy currently unmet program requirements. Course demand is assessed by inferring the probability of students taking these courses.

 

Student Survey Analysis (not yet available) assesses course demand through a direct online survey of individual students on their desired/needed courses and availability for an upcoming academic term.

 

The institution will weigh the importance of each of the analysis types during the analysis run setup. The tool then suggests high-impact schedule refinements using an interactive web-based reporting interface. This data, displayed in order of highest impact changes to lowest, can be further investigated by drill-down to find additional detailed information. Based on this feedback, changes can be implemented and tested to model their impact before creating and publishing a new schedule. The analysis, testing and change process is a cycle that can be repeated as necessary until the desired results are achieved.

High-Impact Recommendations

Data returned from the analysis run will recommend changes that involve course offerings and course meeting times.

 

Course Offering Change Recommendations

 

Under-supply

 

Under-supply is reported when the number of students needing the course exceeds available seats in the roll-forward schedule. The following may be considered when determining if changes should be made:

 

•How many students are impacted?

•Is the course a degree requirement for these students?

•Are these students near the end of the program?

 

Over-supply

 

Over-supply occurs when the number of students needing the course is less than available seats in the roll-forward schedule. The following may be considered when determining if changes should be made:

 

•How many offerings should be reviewed to determine true need?

•Is the instructor a full faculty member or a part time instructor?

•Could the instructor be used to teach an undersupplied course?

•Do these offerings occur during prime time hours or in highly sought after (bottleneck) rooms?

 

Time Change Recommendations

(available in a future release)

 

Wrong Time of Week

 

Wrong time of week occurs when courses are scheduled during times when the students who need them are not available.  An example of this occurs when all of the students needing the course are evening students and all of the sections are scheduled during daytime hours. Prioritizing these disconnects involves the same factors as under-supply, since the net effect is the same - students can’t get courses they need.

 

Student Conflict

 

Student conflict occurs when students have multiple required courses offered at the same time. If those sections have always been in conflict in roll-forward schedules, this problem may go undetected. Prioritizing these disconnects, like wrong time of week, involves the same factors as under-supply.