In orthogonal range reporting we are to preprocess
Abstract. The mode of a multiset of labels, is a label that occurs at least as often as any other label. The input to the range mode problem is an array A of size n. A range query [i, j] must return the mode of the subarray. We prove that any data structure that uses S memory cells of w bits needs Ω( log n log(Sw/n) ) time to answer a range mode query. Secondly, we consider the related range k-frequency problem. The input to this problem is an array A of size n, and a query [i, j] must return whether there exists a label that occurs precisely k times in the subarray. We show that for any constant k > 1, this problem is equivalent to 2D orthogonal rectangle stabbing, and that for k = 1 this is no harder than four-sided 3D orthogonal range emptiness. Finally, we consider approximate range mode queries. A c-approximate range mode query must return a label that occurs at least 1/c times that of the mode. We describe a linear space data structure that supports 3-approximate range mode queries in constant time, and a data structure that uses O( n ε ) space and supports (1 + ε)-approximation queries in O(log 1 ε ) time.
Predicting the success of students participating in introductory programming courses has been an active research area for more than 25 years. Until recently, no variables or tests have had any significant predictive power. However, Dehnadi and Bornat claim to have found a simple test for programming aptitude to cleanly separate programming sheep from non-programming goats. We briefly present their theory and test instrument.We have repeated their test in our local context in order to verify and perhaps generalise their findings, but we could not show that the test predicts students' success in our introductory programming course.Based on this failure of the test instrument, we discuss various explanations for our differing results and suggest a research method from which it may be possible to generalise local results in this area. Furthermore, we discuss and criticize Dehnadi and Bornat's programming aptitude test and devise alternative test instruments.
Predicting the success of students participating in introductory programming courses has been an active research area for more than 25 years. Until recently, no variables or tests have had any significant predictive power. However, Dehnadi and Bornat claim to have found a simple test for programming aptitude to cleanly separate programming sheep from non-programming goats. We briefly present their theory and test instrument.We have repeated their test in our local context in order to verify and perhaps generalise their findings, but we could not show that the test predicts students' success in our introductory programming course.Based on this failure of the test instrument, we discuss various explanations for our differing results and suggest a research method from which it may be possible to generalise local results in this area. Furthermore, we discuss and criticize Dehnadi and Bornat's programming aptitude test and devise alternative test instruments.
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