Based on approximately 25,000 Student Course Evaluation Questionnaires (SCEQ) covering 16 semesters from Spring 2005 through Fall 2012 and publicly available at the University of Maryland, Baltimore County’s website http://www.umbc.edu/oir/sceq/index.html, we analyze the effect of class level and discipline on student responses. We compare the results obtained and conclusions drown with those already reported in the literature.

Monitoring data streams in a distributed system has attracted considerable interest in recent years. The task of feature selection (e.g., by monitoring the information gain of various features) requires a very high communication overhead when addressed using straightforward centralized algorithms. While most of the existing algorithms deal with monitoring simple aggregated values such as frequency of occurrence of stream items, motivated by recent contributions based on geometric ideas we present an alternative approach. The proposed approach enables monitoring values of an arbitrary threshold function over distributed data streams through constraints applied separately on each stream. We report numerical experiments on a real-world data that detect instances where communication between nodes is required, and compare the approach and the results to those recently reported in the literature.

The k-means algorithm with cosine similarity, also known as the spherical k-means algorithm, is a popular method for clustering document collections. However spherical k-means can often yield qualitatively poor results, especially when cluster sizes are small, say 25-30 documents per cluster, where it tends to get stuck at a local maximum far away from the optimal solution. In this paper, we present a local search procedure, which we call 'first-variation" that refines a given clustering by incrementally moving data points between clusters, thus achieving a higher objective function value. An enhancement of first variation allows a chain of such moves in a Kernighan-Lin fashion and leads to a better local maximum. Combining the enhanced first-variation with spherical k-means yields a powerful "ping-pong" strategy that often qualitatively improves k-means clustering and is computationally efficient. We present several experimental results to highlight the improvement achieved by our proposed algorithm in clustering high-dimensional and sparse text data.