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

This study investigates the factors that impact student ratings of instructor teaching using decision trees and cluster analysis. The analysis explores variables such as books, articles, citations, awards, grant dollars, grants, and conference proceedings. The results provide insights into the expected grade versus overall instructor rating and competitive positioning.

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

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  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 • Course planning • Academic scheduling • 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 Identify courses that are taken together Association Find patterns and trends over time Sequence Source: Thulasi Kumar, 2004

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