UCLA

Classroom practices in regards to technology use may have a significant impact (positive or negative) on the effectiveness of a curriculum. This paper looks at temporal frequency of technology use in the context of a high school statistics curriculum, and generates exploratory clusters of that usage with multivariate regression trees. It examines both Euclidean distance and two versions of Kullback-Leibler divergence, ultimately discovering that Euclidean clusters are more robust to outliers and have lower cross-validated error.