Loss of connectivity property is a common problem presented in binary map resizing operation. There are several algorithms to make the enlargement or reduction of digital images. However, when these techniques are applied in a binary geographic map image, in order to obtain a scaled map, these algorithms do not preserve the connectivity property. When a filled algorithm is required for coloring each item of a geographic map (e.g. its political division) the connectivity property is fundamental.In this paper, we propose a resizing algorithm for processing binary geographic maps. The proposed algorithm preserves the connectivity property in the maps when an enlargement or reduction operation is applied.
To derive a latent trait (for instance ability) in a computer adaptive testing (CAT) framework, the obtained results from a model must have a direct relationship to the examinees’ response to a set of items presented. The set of items is previously calibrated to decide which item to present to the examinee in the next evaluation question. Some useful models are more naturally based on conditional probability in order to involve previously obtained hits/misses. In this paper, we integrate an experimental part, obtaining the information related to the examinee’s academic performance, with a theoretical contribution of maximum entropy. Some academic performance index functions are built to support the experimental part and then explain under what conditions one can use constrained prior distributions. Additionally, we highlight that heuristic prior distributions might not properly work in all likely cases, and when to use personalized prior distributions instead. Finally, the inclusion of the performance index functions, arising from current experimental studies and historical records, are integrated into a theoretical part based on entropy maximization and its relationship with a CAT process.
Computer Adaptive Testing (CAT) is an example of a Computer Based Test (CBT) and is one of the main trending topics in the area of knowledge testing and, more recently, in e-learning or in Intelligent Tutoring Systems scenarios. The Item Response Theory (IRT) defines the theoretical basis of a CAT implementation, which assumes the existence of a repository of properly calibrated items that is used during the testing process of a particular examinee. The calibration and adaptation are based on an Item Characteristic Curve (ICC) related to an specific model, being Rasch's models the most widely used. CAT systems require high computational cost to implement the calibration and evaluation processes and the amount of concurrent users at a time could be large enough. Thus, the platform must support high concurrency and availability to perform a desired level of functionality. Technological tendencies in computing offer each time better platforms to develop and manage big collections of data for its processing and relevant information extraction. This paper presents a perspective of using new technologies in CAT as an alternative of implementation. Particularly, the use of a cloud computing platform as current alternative for online CAT systems using the capabilities of multicore processing and big amount of RAM that offers the cloud, to resolve the proper mathematical equations related to psychometric models and the operations described in their algorithms in a real evaluation scheme.
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