Abstract-Learning Object Technology is a diverse and contentious area, which is constantly evolving, and will inevitably play a major role in shaping the future of both teaching and learning. Learning Objects are small chunk of materials which acts as basic building blocks of this technology enhanced learning and education.Learning Objects are hosted by various repositories available online so that different users can use them in multiple contexts as per their requirements. The major bottleneck for end users is finding an appropriate learning object in terms of content quality and usage. Theorist and researchers have advocated various approaches for evaluating learning objects in form of evaluation tools and metrics, but all these approaches are either qualitative based on human review or not supported by empirical evidence. The main objective of this paper is to study the impact of current evaluation tools and metrics on quality of learning objects and propose a new quantitative system LOQES that automatically evaluates the learning object in terms of defined parameters so as to give assurance regarding quality and value.

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An efficient cluster-based cab recommender system (CBCRS) provides solo cab drivers with recommendations about the next pickup location having high passenger finding potential at the shortest distance. To recommend the cab drivers with the next passenger location, it becomes imperative to cluster the global positioning system (GPS) coordinates of various pick-up locations of the geographic region as that of the cab. Clustering is the unsupervised data science that groups similar objects into a cluster. Therefore, the objectives of the research paper are fourfold: Firstly, the research paper identifies various clustering techniques to cluster GPS coordinates. Secondly, to design and develop an efficient algorithm to cluster GPS coordinates for CBCRS. Thirdly, the research paper evaluates the proposed algorithm using standard datasets over silhouette coefficient and Calinski-Harabasz index. Finally, the paper concludes and analyses the results of the proposed algorithm to find out the most optimal clustering technique for clustering GPS coordinates assisting cab recommender system.