This paper describes a system's model for Augmented Reality Learning in which a traditional book is converted to an interactive book using Glyphs (TAGs) and multimedia. The interactive book can be used by a child, parent, or by a teacher to make learning an enjoyable experience. As the child goes through the contents of the book, illustrations and images come to live, thus enforcing the learning and comprehension of concepts in an interactive and fun way. To make a printed book interactive, special TAGs (Glyphs) are inserted in the required places within the book, ready to be read by the webcam and then converted to video, 2-D or 3-D images, audio and explanation text. An actual example (Sandy Starfish) is presented to illustrate the architecture and the implementation of the Augmented Reality learning system and to explain the steps and procedure used to transform a textbook to an interactive one.

Associative Classification (AC) classifiers are of substantial interest due to their ability to be utilised for mining vast sets of rules. However, researchers over the decades have shown that a large number of these mined rules are trivial, irrelevant, redundant, and sometimes harmful, as they can cause decision-making bias. Accordingly, in our paper, we address these challenges and propose a new novel AC approach based on the RIPPER algorithm, which we refer to as ACRIPPER. Our new approach combines the strength of the RIPPER algorithm with the classical AC method, in order to achieve: (1) a reduction in the number of rules being mined, especially those rules that are largely insignificant; (2) a high level of integration among the confidence and support of the rules on one hand and the class imbalance level in the prediction phase on the other hand. Our experimental results, using 20 different well-known datasets, reveal that the proposed ACRIPPER significantly outperforms the well-known rule-based algorithms RIPPER and J48. Moreover, ACRIPPER significantly outperforms the current AC-based algorithms CBA, CMAR, ECBA, FACA, and ACPRISM. Finally, ACRIPPER is found to achieve the best average and ranking on the accuracy measure.

Abstract. The paper is an attempted to discuss one of the must important components in the architecture of personalized teaching systems -the testing system. One of the aims of the paper is to propose a definition of cognitive state, which can be used in designing personalized teaching systems.