Geospatial data analysis can be improved by using data-driven algorithms and techniques from the machine learning field. The aim of our research is to discover interrelationships among topographical data to support the decision-making process. In this paper, we extracted topographical geospatial data from digital elevation model (DEM) raster images, and we discovered hidden patterns among this data based on the K-means clustering algorithm, to uncover relationships and find clusters of elevation values for the area of Jordan. We introduce a method for querying and clustering geospatial data and we built an interactive map accordingly. The method discovers hidden patterns and uncovers relationships in given large datasets. We demonstrate the applicability of the method using the Jordan map and we report on geospatial data analysis and retrieval improvements. The results show that the optimal decision is in favor of four clusters (classes). The first class includes the high elevation values, the second class includes the very low elevation values, the third class includes the medium-high elevation values, and the fourth class includes the very high elevation values.

In this paper, we suggest an e-learning framework for producing electronic courses. The conceptual model, the use case diagram and the class diagram for the suggested framework are provided. In order to implement the framework, the MOODIE platform is used. The MOODIE platform provides the premier integrated, robust, scalable, flexible and secure services. Because the MOODIE is a free source platform, it can be customized according to the user requirements. In fact, implementing the framework using MOODIE platform will ensure high reliability and availability to the most demanding of user communities, and providing superior risk management, return on investment and the financial flexibility of modular enhance. One of the major outputs of this work is a set of two electronic courses (database and software engineering for undergraduate students) that can be utilized by the University of Jordan and any other interested parties.