Subjective Norms and Academic Dishonesty : A Decision Tree Algorithm Analysis

Academic dishonesty, ethical norms and learning

Subjective probability over a subjective decision tree

Since Savage’s seminal work, a state space has been assumed as a primitive and as a complete description of the world. However, the decision maker may have subjective states in her mind, which are distinct from the primitive states, but relevant for her decisions. Dekel, Lipman and Rustichini (2001) derive a unique subjective state space from preference. In a dynamic setting, a state space S an...

Investigating Academic Dishonesty with Concrete Measures

Our recent research revealed two predictors of multiple-choice exam cheating: subclinical psychopathy and poor verbal ability. However, the extent to which individual difference variables predict plagiarism on term papers remains untested. Such research is now possible using the widely used plagiarism detector called Turn-It-In. We collected such objective measures of both plagiarism and multip...

ROC Analysis of a Linguistic Decision Tree Merging Algorithm

ROC analysis is an important tool for evaluation and comparison of classifiers in imprecise environments (i.e., class distribution and cost parameters are unknown). Area Under the Curve of ROC (AUC) is increasingly being recognized as a better measure for evaluating algorithm performance than accuracy. A bigger AUC value implies a better ranking performance for a classifier. Linguistic decision...

Policy Decision Tree for Academic Digital Collections

We present the results of a questionnaire survey for the access and reproduction policies of 67 digital collections in 34 libraries (national, academic, public, special etc) from 13 countries. We examine and analyze the above policies in relation to specific factors, such as, the acquisition method, copyright ownership, library type (national, academic, etc.), content creation (digitized, born-...