Abdelbaset Almasri

Aspect-Oriented Software Development (AOSD) is a paradigm aiming to solve problems of object-oriented programming (OOP). With normal OOP it’s often unlikely to accomplish fine system modularity due to crosscutting concerns being scattered and tangled throughout the system. AOSD resolves this problem by its capability to crosscut the regular code and as a consequence transfer the crosscutting concerns to a single model called aspect. This thesis describes an experiment on industrial application wherein the effectiveness of aspect-oriented techniques is explained in migration the OOP application into aspects. The experiment goals at first to identify the crosscutting concerns in source code of the industrial application and transform these concerns to a functionally equivalent aspect-oriented version. In addition to presenting experiences gained through the experiment, the thesis aims to provide practical guidance of aspect solutions in a real application.

In this paper, a prototype of a Decision Support System (DSS) is proposed for providing the knowledge for optimizing the newly adopted e-learning education strategy in educational institutions. If an educational institution adopted e-learning as a new strategy, it should undertake a preliminary evaluation to determine the percentage of success and areas of weakness of this strategy. If this evaluation is done manually, it would not be an easy task to do and would not provide knowledge about all pitfall symptoms. The proposed DSS is based on exploration (mining) of knowledge from large amounts of data yielded from the operating the institution to its business. This knowledge can be used to guide and optimize any new business strategy implemented by the institution. The proposed DSS involves Database engine, Data Mining engine and Artificial Intelligence engine. All these engines work together in order to extract the knowledge necessary to improve the effectiveness of any strategy, including e-learning.

Educational Data Mining (EDM) is a new paradigm aiming to mine and extract knowledge necessary to optimize the effectiveness of teaching process. With normal educational system work it’s often unlikely to accomplish fine system optimizing due to large amount of data being collected and tangled throughout the system. EDM resolves this problem by its capability to mine and explore these raw data and as a consequence of extracting knowledge. This paper describes several experiments on real educational data wherein the effectiveness of Data Mining is explained in migration the educational data into knowledge. The experiments goal at first to identify important factors of teacher behaviors influencing student satisfaction. In addition to presenting experiences gained through the experiments, the paper aims to provide practical guidance of Data Mining solutions in a real application.