Course Scheduling Optimization
Executive Summary &
Recommendations
Spring & Summer 2025 Terms | February 2026
Purpose & Scope
This analysis examines 1,660 course sections across the Spring 2025 (877 sections) and Summer 2025 (783 sections) terms to identify systemic inefficiencies in enrollment management, faculty deployment, room utilization, and time-slot distribution. The goal is to provide data-driven recommendations that reduce waste, improve student access, and lay the groundwork for transitioning from a fragmented, spreadsheet-based scheduling process to a centralized, role-based platform.
The recommendations below are grounded in quantitative analysis of the SP-SU25 Export data and are organized into a phased implementation roadmap spanning 12 months.
Source: Section counts derived from SP-SU25 Export.xlsx — Spring 2025 sheet (877 data rows) and Summer 2025 sheet (783 data rows). Total = 1,660 sections across both terms.
Key Performance Snapshot
Total Sections Offered
Average Utilization Rate
Low Enrollment Sections (<5)
Over-Capacity Sections (>100%)
Empty Sections (0 enrollment)
Faculty with Zero Hours
TBD/TBA Instructor Assignments
Evening Time Slot Concentration
Friday Utilization
FT/PT Faculty Ratio
Performance Snapshot Table
Red = Critical | Yellow = Needs Attention | Blue = Informational
| Metric | Spring 2025 | Summer 2025 | Status |
|---|---|---|---|
| Total Sections | 877 | 783 | Info |
| Average Utilization | 66.7% | 65.2% | Critical |
| Low Enrollment (<5 students) | 21.4% | 15.7% | Critical |
| Over-Capacity Sections | 99 | 71 | Attention |
| Empty Sections (0 enrolled) | 53 | 0 | Attention |
| Faculty with Zero Hours | 43.8% | 67.7% | Critical |
| TBD/Unassigned Faculty | 114 | 131 | Critical |
| Evening Concentration | 38.3% | 59.6% | Critical |
| Friday Utilization | 5.8% | 0.3% | Critical |
| FT:PT Faculty Ratio | 1:25 | 1:30 | Attention |
Critical Findings & Reasoning
1. Severe Time-Slot Concentration
HIGHNearly half of all course sections (48.4% in Spring, 59.6% in Summer) are compressed into a single evening time block, while Friday utilization sits at just 5.8% in Spring and a near-zero 0.3% in Summer. This concentration creates an artificial bottleneck: rooms are at maximum demand during peak hours, students face scheduling conflicts that limit course selection, and faculty workloads cluster into narrow windows.
Why this matters: Redistributing even 15–20% of evening sections into underutilized daytime and Friday slots would immediately relieve room pressure, reduce student conflicts, and create a more balanced weekly schedule without requiring any new resources.
Source: SP-SU25 Export.xlsx — "Time" column and "Day" column
2. Low-Enrollment Section Proliferation
HIGHBetween 16% and 21% of all sections carry fewer than five enrolled students, and Spring 2025 includes 53 sections with zero enrollment. Meanwhile, the overall average utilization rate of 66–67% indicates significant unused seat capacity across the institution.
Why this matters: Establishing minimum enrollment thresholds (e.g., 8–10 students per section) and consolidating low-enrollment sections would directly improve per-section economics, free up faculty capacity for high-demand courses, and reduce overall scheduling complexity. The 109 courses shared across 3+ programs represent immediate consolidation opportunities.
Source: SP-SU25 Export.xlsx — "Total Enrollment" column
3. Unbalanced Faculty Workload Distribution
HIGHThe full-time to part-time faculty ratio is heavily skewed at 1:25 in Spring and 1:30 in Summer, meaning the institution relies almost entirely on adjunct faculty for instruction. Compounding this, 43.8% of Spring faculty and 67.7% of Summer faculty have zero recorded instructional hours, while 114–131 sections remain assigned to "TBD."
Why this matters: Without early TBD resolution and workload balancing, course assignments remain uncertain late into the scheduling cycle, leading to last-minute adjunct hires, student-facing schedule changes, and an inability to guarantee instructional quality. Establishing faculty workload bands (e.g., 60–90 hours) and resolving TBD assignments 6+ weeks before term start would stabilize the process.
Source: Master Schedule 2025.xlsx — Faculty sheets and SP-SU25 Export.xlsx — "Instructor" column
4. Room Utilization Imbalance
MEDIUMRoom utilization is highly uneven: the top three rooms account for 59.6% of all scheduled sections while other spaces sit idle. Combined with 568 sections missing capacity data entirely, the institution lacks the information needed to make informed room assignment decisions. Over-capacity sections (99 in Spring, 71 in Summer) also signal a mismatch between assigned rooms and actual demand.
Why this matters: A capacity-aware room assignment system—starting with a complete capacity audit—would eliminate over-booking, distribute sections across available space, and reveal whether the institution has a genuine room shortage or simply a room assignment problem.
Source: SP-SU25 Export.xlsx — "Room" and "Capacity" columns
5. Data Quality & Process Gaps
MEDIUMThe current scheduling workflow relies on interconnected spreadsheets and email-based approvals, leading to systemic data quality issues: 568 of 866 Summer sections lack capacity values, 58.3% of Summer faculty records are missing Employee IDs, and the Master Schedule contains formula reference errors (#REF!).
Why this matters: No optimization strategy can succeed on unreliable data. A data cleanup sprint—filling missing capacity values, resolving Employee IDs, and fixing formula errors—is a prerequisite for every other recommendation. Long-term, migrating to a centralized platform with role-based access and automated validation would prevent these issues from recurring.
Source: SP-SU25 Export.xlsx and Master Schedule 2025.xlsx — multiple sheets
Recommended Action Plan
The following phased roadmap sequences actions to deliver quick wins first, then addresses structural issues, and finally builds toward long-term systemic transformation. Each recommendation is directly tied to the findings above.
Phase 1: Quick Wins
0–3 Months
| Action | Rationale | Expected Impact |
|---|---|---|
| Set enrollment floors (8–10 students minimum) | Consolidating the 16–21% of sections with <5 students reduces waste and frees faculty time | Eliminate 130–185 underperforming sections; improve avg utilization to 75%+ |
| Redistribute 15–20% of evening sections | With 48–60% of sections in one evening block, shifting courses to daytime/Friday slots relieves peak pressure | Reduce room conflicts, improve student access to preferred times |
| Activate Friday scheduling | Friday utilization at 0.1–5.8% represents ~20% of the week going unused | Add capacity equivalent to opening a new location without capital investment |
| Resolve TBD assignments 6+ weeks pre-term | 114–131 unassigned sections create cascading delays | Stabilize faculty plans, reduce last-minute adjunct scrambles |
| Complete data cleanup sprint | 568 missing capacities and 58% missing Employee IDs undermine all planning | Enable accurate utilization tracking and room optimization |
Phase 2: Structural Improvements
3–6 Months
| Action | Rationale | Expected Impact |
|---|---|---|
| Implement demand-based scheduling model | Current scheduling does not account for historical enrollment patterns | Right-size section counts per course based on demonstrated demand |
| Rebalance faculty workload bands (60–90 hrs) | Hour allocations range from 52–127 with no standard; 67.7% have zero hours | More equitable distribution, fewer overloaded or idle instructors |
| Optimize cross-program section sharing | 109 courses serve 3+ programs; separate sections inflate section counts | Reduce duplication by 15–20% through strategic stacking |
| Deploy capacity-aware room assignment | Top 3 rooms hold 59.6% of sections; others sit empty | Distribute sections evenly and eliminate over-capacity bookings |
| Develop multi-location strategy | 25 locations scheduled without coordination | Reduce location-specific bottlenecks; optimize hybrid/online balance |
Phase 3: Systemic Transformation
6–12 Months
| Action | Rationale | Expected Impact |
|---|---|---|
| Deploy centralized scheduling platform | Spreadsheet-based workflow creates access conflicts, version issues, and data loss | Single source of truth with role-based permissions and audit trail |
| Build automated monitoring dashboards | No real-time visibility into utilization, enrollment trends, or capacity | Proactive alerts for low enrollment, over-capacity, and TBD assignments |
| Develop predictive enrollment model | Scheduling decisions are reactive rather than data-informed | Forecast demand by program, modality, and term to optimize section counts |
| Create centralized faculty database | Faculty data scattered across multiple sheets with inconsistent records | Unified profiles with credentials, availability, workload history, and preferences |
| Implement integrated approval workflow | Email-based approvals create bottlenecks and lack accountability | Structured routing with role-based sign-off, automated escalation, and tracking |
Data Sources & References
| Data Point | Source File | Sheet(s) | Method |
|---|---|---|---|
| Total Sections (877 / 783) | SP-SU25 Export.xlsx | Spring 2025, Summer 2025 | Row count of each sheet (excluding header) |
| Average Utilization (66.7% / 65.2%) | SP-SU25 Export.xlsx | Spring 2025, Summer 2025 | mean(Total Enrollment ÷ Capacity) per term |
| Low Enrollment <5 (21.4% / 15.7%) | SP-SU25 Export.xlsx | Spring 2025, Summer 2025 | Total Enrollment column: count where value < 5 ÷ total rows |
| Empty Sections (53 / 0) | SP-SU25 Export.xlsx | Spring 2025, Summer 2025 | Total Enrollment column: count where value = 0 |
| Over-Capacity (99 / 71) | SP-SU25 Export.xlsx | Spring 2025, Summer 2025 | Sections where Total Enrollment > Capacity |
| Evening Concentration (48.4% / 59.6%) | SP-SU25 Export.xlsx | Spring 2025, Summer 2025 | Time column: evening time-block sections ÷ total |
| Friday Utilization (5.8% / 0.3%) | SP-SU25 Export.xlsx | Spring 2025, Summer 2025 | Day column: Friday sections ÷ total sections |
| FT:PT Ratio (1:25 / 1:30) | Master Schedule 2025.xlsx | Spring/Summer Faculty | Faculty type designation (full-time vs. part-time counts) |
| Zero Hours (43.8% / 67.7%) | Master Schedule 2025.xlsx | Spring/Summer Faculty | Hour Allocations column: count where value = 0 ÷ total |
| 109 Shared Courses | Master Schedule 2025.xlsx | Courses by Program | Courses appearing in 3+ program columns |
| Missing Employee IDs (58.3%) | Master Schedule 2025.xlsx | Summer 2025 Faculty | Employee ID column: count of blank values ÷ total |
SP-SU25 Export.xlsx
Clean registrar export containing Spring 2025 (932 rows × 29 columns) and Summer 2025 (896 rows × 25 columns) scheduling data.
Master Schedule 2025.xlsx
20-sheet master workbook containing faculty rosters, courses-by-program mapping, and data validation figures.
Course_Scheduling_Optimization_Report.xlsx
Generated analysis workbook containing detailed findings across 7 sheets and 670+ flagged items.
Conclusion
The data tells a clear story: the current scheduling process has significant capacity that is either misallocated or untracked. With average utilization at 66%, a fifth of sections serving fewer than five students, and an entire weekday (Friday) going virtually unused, the institution has substantial room to improve outcomes without adding resources.
The Phase 1 quick wins alone—enrollment floors, evening redistribution, Friday activation, TBD resolution, and data cleanup—can be implemented within a single scheduling cycle and would meaningfully improve utilization, faculty stability, and student access. Phases 2 and 3 then build on that clean foundation to create a scheduling operation that is data-driven, collaborative, and scalable.
Critically, the long-term vision of a centralized platform addresses the root cause of the challenges identified: a manual, spreadsheet-based workflow that lacks version control, role-based access, and real-time visibility. The platform options outlined in the optimization report—from custom-built solutions to enterprise academic scheduling software—should be evaluated against the institution's budget, IT capacity, and timeline.