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In the wave of higher education digital transformation, more and more universities have built data centers and deployed various business systems. However, problems like scattered data, system fragmentation, and difficult queries still trouble faculty and staff. Academic affairs teachers wanting to query "course enrollment distribution by major this semester" need to log into the academic system, export Excel, and manually count; the research department wanting to analyze "research funding trends by college in the past three years" need to submit requirements to the IT center and wait weeks for reports.
This article systematically sorts out core scenarios for university data analysis and explores how to use AI-driven natural language query tools to improve management efficiency, truly achieving "data-driven decision-making" smart campus.
University data is scattered across multiple systems:
The same concept is defined differently across systems:
Problem: Statistics from three systems don't match, causing decision confusion.
University data involves multiple departments with complex permission management:
University data query needs are temporary and diverse:
Status:
Impact:
Status:
Impact:
Common Problems:
Impact:
Core Objective: Optimize teaching resource allocation and improve teaching quality
Key Questions:
Analysis Metrics:
Natural Language Query Examples (using AskTable):
"Top 10 courses by enrollment this semester"
"Enrollment distribution by grade for Computer Science College"
"Which courses have enrollment under 20?"
"Compare this semester's and last semester's enrollment changes"
Decision Applications:
Key Questions:
Analysis Metrics:
Natural Language Query Examples:
"Average GPA by major this semester"
"Top 10 courses by failure rate"
"Grade distribution for Advanced Mathematics"
"What are the grade differences for the same course taught by Teacher Zhang and Teacher Li?"
Early Warning Mechanism:
Identify academic warning students:
- GPA < 2.0 for two consecutive semesters
- Cumulative failed credits > 10
- Failed > 3 required courses
Trigger warning:
- Automatically notify counselors
- Send academic guidance suggestions
- Arrange academic assistance
Key Questions:
Analysis Metrics:
Natural Language Query Examples:
"Employment rates by major for Class of 2025"
"Trends in further education rates for Computer Science major over the past three years"
"Which majors have the highest relevant employment rates?"
"Four-year graduation rate forecast for Class of 2020"
Enrollment Promotion Applications:
Highlight extraction:
- Majors with employment rates exceeding 95%
- Majors with further education rates exceeding 50%
- Majors with top average salaries
Used in enrollment brochures, promotional materials, and enrollment consultations
Core Objective: Improve research output and optimize research resource allocation
Key Questions:
Analysis Metrics:
Natural Language Query Examples:
"SCI paper publications by college in 2025"
"Which papers have impact factors greater than 10?"
"Trends in patent grant quantities over the past three years"
"CCF A-class conference papers from Computer Science College"
Research Evaluation Applications:
Discipline evaluation preparation:
- Representative papers from the past five years
- High-level research achievement statistics
- Summary of various research awards
- Industry-university-research cooperation cases
Generate evaluation materials, saving manual organization time
Key Questions:
Analysis Metrics:
Natural Language Query Examples:
"Research funding received by college this year"
"Projects with fund utilization rate below 30%"
"Projects expiring soon but not completed"
"Proportion of horizontal vs vertical project funding"
Fund Early Warning:
Warning rules:
- Project expiring soon (<3 months) but fund utilization < 60%
- Abnormal fund expenditure (monthly expenditure exceeds average by 200%)
- Long-term zero expenditure projects (>6 months without expenditure records)
Automatically notify project leaders and research administration
Key Questions:
Analysis Metrics:
Natural Language Query Examples:
"Research teams with most papers published in the past three years"
"Which cooperation units does Professor Zhang's team have?"
"Proportion of papers from cross-college cooperation"
"Average papers per young faculty member within three years of hire"
Core Objective: Precise student profiling and personalized services
Data Dimensions:
Application Scenarios:
Scenario 1: Precise Subsidies
Identify struggling students:
- Monthly average canteen consumption < 300 yuan
- Consumption times concentrated in off-peak hours (cheaper food)
- Almost no entertainment consumption (movies, milk tea, etc.)
- Applied for student loans
Measures:
- Proactively contact students to understand actual situations
- Provide scholarships and work-study opportunities
- Mental health care, avoid inferiority complex
Scenario 2: Academic Warnings
Identify students with academic difficulties:
- Continuously declining GPA
- Gradually increasing failed courses
- Declining library borrowing
- Irregular作息 (frequently returning to dorm late)
Measures:
- Counselor interviews
- Arrange academic tutoring
- Psychological counseling (check for psychological issues)
Natural Language Query Examples:
"How many students have monthly consumption below 300 yuan?"
"List of students with GPA below 2.5 and more than 3 failed courses"
"Proportion of students with zero library borrowing this semester"
"Characteristics of students frequently returning late (after 23:00)"
Key Questions:
Evaluation Standards (National Scholarship Example):
Hard requirements:
- GPA ranking: Top 10% of major
- No failed course records
- No disciplinary actions
Bonus points:
- Competition awards (National +10, Provincial +5)
- Paper publications (SCI +15, Core journals +10)
- Social practice (Excellent volunteer +5)
Comprehensive ranking: GPA weight 70% + Bonus points 30%
Natural Language Query Examples:
"List of students meeting national scholarship criteria"
"Coverage rate of grants by college"
"Further education rates of students who received national scholarships"
"Usage of various bonus points in scholarship evaluation"
Key Questions:
Risk Identification:
Abnormal behavioral characteristics:
- Long-term no dormitory leaving (no one-card consumption records)
- Frequent late-night entries/exits (irregular schedules)
- Sudden changes in consumption behavior (e.g., sudden large or zero consumption)
- Cliff-like decline in academic performance
- Significantly reduced social activities
Automatically trigger warnings, notify counselors and psychological counseling center
Note:
Core Objective: Optimize logistics resource allocation and improve service quality
Key Questions:
Analysis Metrics:
Natural Language Query Examples:
"Dining人次 for each canteen this week"
"Top 10 dishes in Canteen 1"
"Dining flow at each time point during lunch (11:30-13:00)"
"Compare dining counts on weekends vs weekdays"
Operations Optimization:
Strategy 1: Dish Adjustment
- Phase out low-sales dishes
- Increase supply of popular dishes
- Adjust menu according to season
Strategy 2: Staggered Dining
- Analyze peak hours
- Launch staggered discounts (e.g., 10% off 11:00-11:30)
- Relieve queuing pressure
Strategy 3: Reduce Waste
- Predict daily dining numbers
- Optimize food material procurement
- Reduce leftover food
Key Questions:
Analysis Metrics:
Natural Language Query Examples:
"Occupancy rate by dormitory building"
"Dormitories using high-power electrical appliances this month"
"Work orders with repair response time exceeding 24 hours"
"Dormitories with abnormal utility fees (exceeding average by 200%)"
Intelligent Early Warning:
Electrical Safety Warning:
- Sudden surge in dormitory electricity consumption (possible high-power appliance use)
- Continuous high-power consumption late at night (23:00-6:00)
Equipment Maintenance Warning:
- Abnormally high repair frequency for certain buildings (possible equipment aging)
- High failure rate for specific equipment (e.g., air conditioners)
Automatically notify dormitory management and maintenance departments
Key Questions:
Analysis Metrics:
Natural Language Query Examples:
"Top 10 classrooms with lowest utilization this semester"
"Failure rate ranking of laboratory equipment"
"Changes in multimedia equipment maintenance costs over the past three years"
"Which equipment has been in use for over 10 years?"
Problems with Traditional Mode:
AskTable Solutions:
Problem: Technical and business personnel have different understandings of data
Solution: Configure business semantic layer in AskTable
Indicator Definition:
- Name: Four-Year Graduation Rate
Definition: Proportion of students obtaining degrees within four years of enrollment
Calculation: |
SELECT
COUNT(CASE WHEN graduation_year - enrollment_year <= 4 THEN 1 END) * 1.0 /
COUNT(*) as graduation_rate
FROM students
WHERE enrollment_year = :year
Synonyms: [On-time Graduation Rate, Normal Graduation Rate]
- Name: Further Education Rate
Definition: Proportion of graduates continuing education (graduate school, abroad)
Calculation: |
SELECT
COUNT(CASE WHEN further_study = true THEN 1 END) * 1.0 /
COUNT(*) as further_study_rate
FROM graduates
WHERE graduation_year = :year
Synonyms: [升学率, 读研率]
Dimension Definition:
- Name: College
Field: college_name
Possible Values: [Computer Science College, Economics College, Management College, ...]
- Name: Major
Field: major_name
Hierarchy: College > Major
Permission Configuration:
- Role: College Leader
Rule: Can only view this college's data
Implementation: WHERE college_id = :user_college_id
- Role: Academic Affairs
Rule: Can view data across the university
Implementation: No restrictions
Scenario 1: Academic Affairs Teacher
Q: "Course enrollment by major this semester"
AskTable:
- Understand "this semester" → Current semester code
- Understand "major" → major_name
- Understand "enrollment count" → COUNT(DISTINCT student_id)
- Generate SQL and execute
- Return results and bar chart
Follow-up: "Only for Computer Science College"
AskTable:
- Understand context (continuing previous query)
- Add filter: college_name = 'Computer Science College'
- Return updated results
Scenario 2: Research Administration Teacher
Q: "SCI paper publications by college in the past three years"
AskTable:
- Understand "past three years" → 2023-2025
- Understand "by college" → GROUP BY college_name
- Understand "SCI papers" → Query semantic layer definition
- Generate SQL, containing complex JOINs and aggregations
- Return results and line chart
Follow-up: "Compare with the previous three years"
AskTable:
- Understand need to compare two time periods
- Generate query containing year-over-year growth rates
- Return comparison results
Scenario 3: College Leader
Q: "Average GPA for undergraduates in our college"
AskTable:
- Identify user role: Computer Science College Dean
- Automatically add permission filter: college_id = 1
- Understand "average GPA" → Query semantic layer definition
- Return result: 3.42
Follow-up: "How does it compare with the university average?"
AskTable:
- Query university average GPA: 3.35
- Generate comparison: "Your college average GPA is 3.42, higher than university average of 3.35"
Row-Level Permission Examples:
Permission Rules:
- Role: Counselor
Description: Can only view student data for classes they manage
Implementation:
students table:
Filter: class_id IN (:user_class_ids)
grades table:
Filter: student_id IN (SELECT id FROM students WHERE class_id IN (:user_class_ids))
Column-Level Permission Examples:
Field Permissions:
- Field: students.id_card_number
Visible Roles: [School Leaders, Student Affairs]
Other Roles: Masked display (110***********123)
- Field: students.phone
Visible Roles: [Counselors, Student Affairs, School Leaders]
Other Roles: Masked display (138****5678)
Query Auditing:
Recorded content:
- Who queried what data at what time
- The SQL statement of the query
- Amount of data returned
- Whether sensitive data access was triggered
Auditing analysis:
- Identify abnormal query behaviors (e.g., bulk export of student data)
- Whether permission configuration is reasonable
- Data access frequency statistics
Background:
Implementation Process:
Phase 1 (Weeks 1-2):
Phase 2 (Weeks 3-4):
Phase 3 (Months 2-3):
Effects:
Efficiency Improvement:
Application Scenarios:
User Feedback:
"Before, I had to find the IT center to get data. Now I just ask AskTable directly and get results in seconds." — Academic Affairs Teacher
"Preparing discipline evaluation materials used to take 2 weeks to organize various data. Now it takes 3 days." — Research Administration Teacher
Don't connect all systems at once, recommended to advance in phases:
Phase 1 (1 month): Core systems
Phase 2 (2-3 months): Extended systems
Phase 3 (3-6 months): Full coverage
Data Quality Management:
Data Standardization:
Metadata Management:
Training and Promotion:
Data-Driven Decision-Making:
Incentive Mechanisms:
Student Privacy Protection:
Data Access Auditing:
Compliance:
University digital transformation is not just about building information systems, but more importantly, letting data truly serve teaching, research, management, and decision-making.
Core Challenges:
Solutions:
Value Embodiment:
In smart campus construction, letting every faculty and staff easily obtain, understand, and use data—this is true "data-driven" approach.
Learn More:
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