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Honolulu Community College

November, 2015

Laura Kelley, Senior ConsultantAd Astra Information Systems

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Ad Astra Information Systems™, L.L.C.

Ad Astra’s interest in academic space begin in the 1950’s when the founder’s father, John Shaver, was

introduced to a Ford Foundation project at Stanford University that cemented the firm’s future. Shaver decided

to morph his architectural firm from a general design practice to one specializing in higher-education facilities.

He enthusiastically joined the project and helped shape its contribution to the industry: a framework that quickly

became and remains the standard by which space utilization is assessed and facilities’ master plans are

developed.

When Tom Shaver launched Ad Astra in 1996, it was known that space management was both critically

important and incredibly complex. Measuring space utilization wasn’t enough. First, and most obvious, was that

management didn’t improve utilization; it simply confirmed that need to improve. Second, space was the only

part of the equation. Scheduling must be embraced as a way to allocate not only space, but also faculty; to

deliver instruction, and to enable students to graduate on time.

Ad Astra has collaborated with more than 800 higher education campuses and many state systems that

prioritize the stewardship of instructional resources and improved student outcomes.

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10% of course schedules could feasibly be

“recaptured” through management, for an annual

impact of $14.7B.

58% of institutions core operating costs are for

academic resources.

This savings opportunity is more than 10% of the

$143B paid by students annually in tuition and fees.

Savings opportunity is more than 23% of the $63B of

state appropriations, and 10% of $138B in federal

appropriations.

58%

$14.7B

10%

10%

ACADEMIC RESOURCES ARE CRUCIAL$147BACADEMIC

RESOURCES

ANNUAL SPEND

The Chronicle of Higher Education Almanac (2013-14)

© 2015 AD ASTRA INFORMATION SYSTEMS™, L.L.C. 3

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FOUR YEAR PRIVATE

COMMUNITY COLLEGE

FOUR YEAR PUBLIC

© 2015 AD ASTRA INFORMATION SYSTEMS™, L.L.C.

HIGHER EDUCATION SCHEDULING INDEX130 PEER INSTITUTIONS

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Strategic Scheduling CheckUp Goals

• Alignment to HCC’s strategic plan• Promote Learning and Teaching for Student Success

• Visibility into academic space utilization and management opportunities• Fall 2015 data used

• Address current, reported scheduling challenges• Classroom utilization

• Meeting Pattern Utilization

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Objectives

• Understand course offering inefficiencies directly impacting budgets and capacity

• Understand course offering warning signals that potentially impact student access to required courses and graduation

• Solution framework to leverage data from the SIS in future terms

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Project Overview

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Typical Schedule Building Process

Typical Goal: Completion vs. Improvement

Course offerings are based on a historical schedule, typically a roll-forward of a “like” term

Departments refine offerings in silos. Distinct processes and

decision makers, limited collaboration and decision-

support tools

SIS is updatedRoom assignments are made/refined

“Final” schedule is posted Changes still occur after registration or even after

classes start

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THE SCHEDULE

IT IS COMPLEX!

© 2015 AD ASTRA INFORMATION SYSTEMS™, L.L.C.

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Course Offerings Analysis

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17

70%FILLED

24

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Course Offering Summary – Fall 2015

Average Enrollment / Average Enrollment Capacity

HCC Mean2-year Public Institutions

MeanMin Max

17 / 24 23/ 29 18/25 10/ 16 41 / 53

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Course Offering Summary – Fall 2015 Average Enrollment / Average Enrollment Capacity

Measurement Percent Number Courses Percentile

Enrollment Ratio (85% Target)

70%Avg. Enroll

/Avg. Enroll Cap17 / 24

13th

Percentile

Overloaded Course Ratio (<10% Goal)

16% 52 of 33080th

Percentile

Balanced Course Ratio (>60% Goal)

32% 106 of 33050th

Percentile

Underutilized(<30% Goal)

52% 172 of 33014th

Percentile

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Course Offering Analysis – Fall 2016

Measurement Percent Number Percentile

Reduction Candidates 8% 43 of 549 sections 59th Percentile

Elimination Candidates 7% 39 of 549 sections 39th Percentile

Addition Candidates 12% 68 of 549 sections 83rd Percentile

Addition Candidates (Fall 2016 Only)

0% 0 of 549 sections Lowest Measured

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10%90th Percentile

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Course Offering Analysis by Level

Enrollment ratio varies by course level

LevelBaselineSections

Enrollment RatioAverage

EnrollmentAverage

Enrollment CapSections per

Course

000 Level 122 79% 19 24 2

100 Level 319 70% 17 25 2

200 Level 114 59% 13 22 1

300 Level 4 83% 17 21 1

Totals 559 70% 17 24 2

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Course Offering Analysis by Level

There is a high number of reduction candidates in the 100 level

LevelFall 2016 Sections

Addition Candidates

Addition Candidates, Fall 2016 Only

Reduction Candidates

Elimination Candidates

000 Level 122 26 0 6 8

100 Level 319 23 0 30 15

200 Level 114 17 0 7 16

300 Level 4 2 0 0 0

Totals 559 68 0 43 39

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Course Offering Analysis By Average Sections per Course

Enrollment ratio increases with the section per course count:

Average Sections per Course

Courses Average Enrollment Enrollment Ratio Balanced Course Ratio

1 255 14 63% 27%

2 38 16 70% 42%

3 to 5 24 18 71% 46%

6 to 10 9 22 81% 67%

11 + 4 21 79% 100%

Totals 330 17 70% 32%

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Course Offering by Enrollment Ratio Tier

Enrollment Ratio Courses % of Total Average Enrollment Average Enrollment Cap

1-19% 7 2% 2 24

20-49% 84 25% 8 24

50-69% 81 25% 13 23

70-95% (Balanced) 106 32% 20 25

> 95% (Overloaded) 52 16% 24 23

Totals 330 100% 17 24

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Course Offerings – Financial Information

Methodology:

• Instructors were classified as Full time (F) or Lecturer (L)

• This can be refined by classification later if desired

• Full time - $3411 per credit hour (average rate based on

salary/benefits)

• Lecturer - $1717 per credit hour (average rate)

• Tuition was set for $126 per credit hour

• No student data so we cannot account for in-state/out-of-state

• Section revenue = Section Enrollment*$126*Credit Hours

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Example: ENG 100*

Fall 2015 NumberPercentage/

AverageTuition

RevenueInstructional

CostMargin

Sections Offered

36 $281,232 $302,322 -$21,090

Full Time Instructors

23 64%$7,379 avgper section

$10,233 per section

Lecturers 13 36%$8,578 avgper section

$5,151 per section

Fall 2015 Enrollment

744 21

Fall 2015 Max Enroll

911 25

* Changes in developmental education will mean changes for ENG 100 in Fall 2016. This example illustrates how schedule alignment can benefit the institution, but there will be adjustments for Fall 2016.

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Example: ENG 100*

Fall 2016 NumberPercentage/

AverageTuition

RevenueInstructional

CostAnticipated

Margin

Sections Offered

33 $298,242 $286,869 $11,373

Full Time Instructors

23 70%$10,233 per

section

Lecturers 10 30%$5,151 per

section

Fall 2016 Projected Enrollment

789 24

Fall 2016 Max Enroll

836 25

* Changes in developmental education will mean changes for ENG 100 in Fall 2016. This example illustrates how schedule alignment can benefit the institution, but there will be adjustments for Fall 2016.

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Course Offering Opportunities

• Improved graduation rates from additional seats offered in “gateway” courses (focus on required courses that were overloaded in the past)

• Reduction of inefficiency/expense from reduction and elimination candidates (82 total candidates; 15% of all sections)

• Increased scheduling flexibility and capacity• Identification of underutilized faculty capacity by increasing average

enrollment from 17 to 20 (22% capacity increase to 85% enrollment ratio target)

• Reallocation of faculty, moving reduction candidates to additions

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Course Offering Analysis Dashboards

0%

20%

40%

60%

80%

100%

Goal: Increase Enrollment Ratio

Enrollment Ratio

HCC

Mean

Goal

0%

5%

10%

15%

20%

25%

30%

Goal: Decrease Addition Candidates

Addition Candidates

HCC

Mean

Goal

0%

10%

20%

30%

40%

50%

60%

70%

Goal: Increase Balanced Course Ratio

Balanced Course Ratio

HCC

Mean

Goal

0%

2%

4%

6%

8%

10%

12%

Goal: Decrease Reduction Candidates

Reduction Candidates

HCC

Mean

Goal

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Capacity analysis

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Space Bottleneck ConceptAverage utilization does not reflect capacity or inform space management.

Room Type Campus APrimetime Utilization

Campus BPrimetime Utilization

Classrooms (2) 50% 50%

Science Lab (1) 50% 10%

Tech Auditorium (1) 50% 90%

Average Utilization 50% 50%

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Capacity Management Findings 1

56%

30%

49%

27%

62%

34%

56%

29%

0% 50% 100%

28-hour prime week (8am – 3pm M-R)

67.5 hour standard week (6:30am – 8pm

M-F)

AVERAGE UTILIZATION

Classroom (80) Shop (14) Computer Lab (10) All Rooms (104)

• Classroom utilization during the standard week is Moderately Low

2nd Percentile

• Classroom Prime Ratio (percentage of all usage in primetime) is High

5th Percentile* – 80% Mean is 59%

* Even spread would be 41% (28 of 67.5 hours)

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Capacity Management Findings 2

• Classroom utilization during the 28 hour primetime is higher in larger spaces

SEATS ROOMS PRIME ROOM HOURS PRIME UTILIZATION PRIME RATIO0 - 15 6 42 25% 74%16 - 25 29 458 56% 83%26 - 50 45 757 60% 79%

Total 80 1,258 56% 80%

• HCC is in the 10th percentile for Classroom primetime utilization

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Capacity Management Findings 2

• Computer Lab prime ratio is higher in the 16-25 size category

SEATS ROOMS PRIME ROOM HOURS PRIME UTILIZATION PRIME RATIO16 - 25 7 102 52% 77%26 - 50 3 36 43% 68%

Total 10 138 49% 75%

• Shop utilization is highest in the 26-50 size category during the 28 hour prime time

SEATS ROOMS PRIME ROOM HOURS PRIME UTILIZATION PRIME RATIO0 - 15 1 12 43% 50%16 - 25 9 128 51% 74%26 - 50 4 102 91% 80%

Total 14 242 62% 75%

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Capacity Management Findings 3

• Non-Section utilization in Classroom space is very low overall*

SEATS ROOMSSTANDARD

ROOM HOURSSTANDARD

UTILIZATIONPRIME ROOM

HOURSPRIME

UTILIZATIONPRIME RATIO

0 - 15 2 12 9% 0 0% 0%16 - 25 5 17 5% 0 0% 2%26 - 50 17 46 4% 8 2% 17%

Total 24 75 5% 8 1% 11%

*Meetings, Events, Etc.

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Capacity Management Findings 4

• Classroom utilization varies by departmental priority

• Top ten departments by Primetime UtilizationDEPARTMENT ROOMS OVERALL UTILIZATION PRIME UTILIZATION PRIME RATIOLANA 3 35% 78% 93%AERO 3 49% 78% 65%MATH 4 33% 75% 93%HUM/SS 3 38% 73% 80%ART 1 36% 71% 83%BABRP 1 36% 71% 83%SCI 3 33% 69% 88%PHYS 2 43% 65% 63%OESM 1 44% 64% 60%HESER 1 50% 59% 49%

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Capacity Management Findings 4 cont.• Bottom fifteen departments by Primetime Utilization

DEPARTMENT ROOMS OVERALL UTILIZATION PRIME UTILIZATION PRIME RATIONO PRIORITY 30 28% 58% 87%BIOL 1 29% 54% 77%CHEM 2 27% 52% 81%MELE 2 23% 52% 94%NSCI 1 22% 50% 93%EIMT 2 22% 49% 91%FIRE 1 19% 46% 100%METCH 2 26% 46% 72%COSM 5 27% 45% 70%ECE 2 31% 40% 54%BLPR 1 18% 39% 92%UC 1 16% 38% 100%CA 4 20% 32% 66%FT 3 20% 31% 65%CENT 1 9% 21% 100%

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18

77%

27

25

Classroom Space

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19

67%

25

23

Computer Lab Space

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20

100%

24

25

Shop Space

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Capacity Management Findings 5

• Seat fill enroll ratios in Classroom space is higher in smaller rooms

• Classroom Seat Fill (Enroll) ratio comparison: 67th Percentile

• Classroom Seat Fill (Cap) ratio comparison: 94th Percentile

SEATS ROOMSAVERAGECAPACITY

AVERAGE ENROLL

FILL (ENROLL)AVERAGE

ENROLL CAPFILL (CAP)

0 - 15 6 13 9 72% 18 141%16 - 25 29 22 19 86% 25 111%26 - 50 45 32 18 57% 27 84%Total 80 27 18 66% 25 94%

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Capacity Management Findings 5

• Seat fill enroll ratios in Computer Lab space is consistent across room sizes

SEATS ROOMSAVERAGECAPACITY

AVERAGE ENROLL

FILL (ENROLL)AVERAGE

ENROLL CAPFILL (CAP)

16 - 25 7 24 19 76% 21 88%26 - 50 3 27 21 77% 27 99%Total 10 25 19 77% 23 92%

• Seat fill enroll ratios in Shop space is high across all room size categories

SEATS ROOMSAVERAGECAPACITY

AVERAGE ENROLL

FILL (ENROLL)AVERAGE

ENROLL CAPFILL (CAP)

0 - 15 1 14 14 100% 24 171%16 - 25 9 24 19 80% 25 104%26 - 50 4 28 24 85% 27 97%Total 14 24 20 82% 25 105%

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The Importance of On-Grid Scheduling

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Capacity Management Findings 6

On-grid Primetime Meeting Pattern usage in Classrooms varies by Pattern

MEETING PATTERN TOTAL USAGETOTAL

UTILIZATIONOFF-GRID USAGE

OFF-GRID UTILIZATION

MW 8:30am - 9:45am 162 67% 87 36%MW 10:00 am - 11:15am 207 85% 117 48%MW 11:30am - 12:45pm 201 83% 102 42%MW 1:00pm - 2:15pm 159 65% 114 47%MW 2:30pm - 3:45pm 66 27% 66 27%TR 8:30am - 9:45am 153 63% 72 30%TR 10:00am - 11:15am 210 86% 108 44%TR 11:30am - 12:45pm 159 65% 90 37%TR 1:00pm - 2:15pm 141 58% 87 36%TR 2:30pm - 3:45pm 93 38% 69 28%Total 1,551 64% 912 38%

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Capacity Management Findings 6 Summary

• There is Very High off-grid meeting pattern usage in Classroom space during primetime meeting patterns:

59% (18th Percentile) of usage for all Classroom space

• There is Moderately High off-grid “waste factor”258 hours or 16% of all Classroom space capacity is wasted (41st Percentile)

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Capacity Management Opportunity

• Limit Off-Grid scheduling of Classroom space during primetime from 59% to 20% and waste from 17% to 5%― Result: Eliminate waste inherent in off-grid scheduling (12% capacity

increase)

― Graph category label: “Meeting Patterns”

• Reduce primetime scheduling in Classroom space to 65%― Result: Move activities from Classroom space to reduce prime ratios of 80%

(19% capacity increase)

― Graph category label: “Prime Ratio”

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Capacity Management Opportunity, cont.

• Increase average enrollments through course offering management to get to 85% enrollment ratio target― Result: Increase average enrollment from 17 to 20 (22% capacity increase

to 85% enrollment ratio target)

― Graph category label: “Enrollment Ratio”

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Capacity Management Dashboards – Main Campus

0%

10%

20%

30%

40%

50%

60%

Goal: Increase Space Utilization

Space Utilization

HCC

Mean

Goal

4,500

4,700

4,900

5,100

5,300

5,500

5,700

2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Enrollment Capacity

Meeting Patterns

Existing Capacity

Prime Ratio

Enrollment Ratio

Enrollment

0%

20%

40%

60%

80%

100%

Goal: Decrease Prime Ratio

Prime Ratio

HCC

Mean

Goal

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Strategic Options for Evaluation

Course Offering Efficiency Strategies:

• Evaluate Elimination Candidates for degree requirement impact

• Select Reduction and Elimination Candidates to remove from the Fall schedule (82 total candidates; 15% of all sections)

Course Offering Student Success Strategies:

• Evaluate Addition Candidates for degree requirement impact

• Consider implementing and using Platinum Analytics (uncover key Addition Candidates to improve student completion rates)

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Strategic Options for Evaluation, cont.

Capacity Bottlenecks Strategies:• Meeting Patterns (12% potential capacity)

• Prime Ratio (19% potential capacity)

• Enrollment Ratio (22% potential capacity)

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Next Steps

• Develop a schedule review team and process― Senior leadership and academic department representation― Focused on leveraging and sharing schedule analysis

• Develop data-driven scheduling policies― Course offering efficiency and effectiveness― Meeting pattern and room assignment efficiency

• Integrate other academic planning processes (curriculum planning, academic space planning, student success initiatives, etc.)

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Questions?Laura Kelley, Senior Consultant lkelley@aais.com (913)652-4153