Computerized Adaptive Testing (CAT) is now widely used. However, inserting new items into the question bank of a CAT requires a great effort that makes impractical the wide application of CAT in classroom teaching. One solution would be to use the tacit knowledge of the teachers or experts for a pre-classification and calibrate during the execution of tests with these items. Thus, this research consists of a comparative case study between a Stratified Adaptive Test (SAT), based on the tacit knowledge of a teacher, and a CAT based on Item Response Theory (IRT). The tests were applied in seven Computer Networks courses. The results indicate that levels of anxiety expressed in the use of the SAT were better than those using the CAT, in addition to being simpler to implement. In this way, it is recommended the implementation of a SAT, where the strata are initially based on the tacit knowledge of the teacher and later, as a result of an IRT calibration.
Although widely used, the SCORM metadata model for content aggregation is difficult to be used by educators, content developers and instructional designers. Particularly, the identification of contents related with each other, in large repositories, and their aggregation using metadata as defined in SCORM, has been demanding efforts of computer science researchers in pursuit of the automation of this process. Previous approaches have extended or altered the metadata defined by SCORM standard. In this paper, we present experimental results on our proposed methodology which employs ontologies, automatic annotation of metadata, information retrieval and text mining to recommend and aggregate related content, using the relation metadata category as defined by SCORM. We developed a computer system prototype which applies the proposed methodology on a sample of learning objects generating results to evaluate its efficacy. The results demonstrate that the proposed method is feasible and effective to produce the expected results.