1. What Matters in Student Rating of Instructor Teaching (SRI)?
Decision Trees (CHAID)

2. Student Evaluation of Teaching Form

3. Decision Tree Analysis

4. What Matters in Student Rating of Instructor Teaching?

5. Expected Grade versus Overall Instructor Rating

6. Competitive Positioning
Cluster Analysis (K-Means)

7. Variables Used in the Analysis
1. Books per Faculty 2. Articles per Faculty 3. Citations per Faculty 4. Awards per Faculty 5. Grant Dollars per Faculty (federal) 6. Grants per Faculty 7. Conference Proceedings per Faculty

8. Data Analytics Lifecycle

9. Anomaly Detection
Anomaly detection models are used to identify outliers, or unusual cases, in the data. Unlike other modeling methods that store rules about unusual cases, anomaly detection models store information on what normal behavior looks like. Anomaly detection is an exploratory method designed for quick detection of unusual cases or records that should be candidates for further analysis.
For example, the algorithm might lump records into three distinct clusters and flag those that fall far from the center of any one cluster.
Source: SPSS, 2014

10. Application of Analytics in Institutional Research
Cafeteria meal planning
Student housing planning
Identify high risk students
Estimate/predict alumni contributions
Predict new student application rate
Course planning
Academic scheduling
Identify student preferences for clubs and social organizations
Faculty teaching load estimation
Predict alumni donations
Predict potential demand for library resources
Categorize your students
Classification/Segmentation
Predict students retention/Alumni donations
Neural Nets/Regression
Group similar students
Clustering
We can use Classification techniques to categorize students into different groups: Examples: Survey data analysis such as CSEQ, NSSE, Assessment, Opinion Surveys. Ex writing skills
Prediction techniques can be used to predict students success: Six years graduation rate or one year retention, predict potential alumni donations/donors
Segmentation/Cluster analysis can be used to group similar students: Cafeteria mean plans, survey data analysis
Association techniques can be used to identify courses that are taken together, useful for course scheduling.
Sequence analysis or generators can be used to find patterns and trends overtime: transcript analysis for student learning and post graduation outcomes, course taking pattern analysis for course scheduling and faculty load estimation, trend analysis of library usage by students and faculty for structuring library services and hours.
Identify courses that are taken together
Association
Find patterns and trends over time
Sequence
Source: Thulasi Kumar, 2004