What Matters in Student Rating of Instructor Teaching (SRI)?

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Presentation transcript:

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

Student Evaluation of Teaching Form

Decision Tree Analysis

What Matters in Student Rating of Instructor Teaching?

Expected Grade versus Overall Instructor Rating

Competitive Positioning Cluster Analysis (K-Means)

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

Data Analytics Lifecycle

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

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