In the last few years, the need for an automated way to assess people has increased quickly because of the growing request from both private and public structures. Many Learning Management Systems (LMS) have been developed in order to automatize the learning and assessment process. In most of the cases these systems don't allow a quality content evaluation and an efficient ability estimation. In this paper we analyze the features of the Proportional Odd Model (POM), belonging to the Item Response Theory. The POM enables to translate an automatic test deliver in an efficient way in order to get an evaluation through either a summative or a formative way. The data have been collected in some undergraduate courses of Bologna University, by using test delivering and by authoring system developed in ASP and Java, respectively.
Abstract:The experience from years of development and use, the advance of technology, and the development of authoring tools for questions and tests has resulted in a sophisticated, computer based assessment system. However, there is still a lot of room for further development. Some of the current ideas for development are discussed in the remainder of this work. A primary aim of assessment, both formative and summative, is provide the necessary information to improve future educational experiences because it provides feedback on whether the course and learning objectives have been achieved to satisfactory level. Yet, it is important that the assessment data be accurate and relevant to effectively make informed decisions about the curriculum. Moreover, formative assessment can also be used to help bridge the gap between assessment and learning. This may be achieved particularly where assessment strategies are combined with useful feedback, and integrated within the learning process. The answers to the described objectives are enhanced if we could integrate adaptive testing techniques; accurate and fitted assessment data may improve both the curriculum and the student ability level. The idea behind a computerized adaptive testing (CAT) is quite forward: to apply to each examinee only those items useful to know his proficiency level. As a consequence of this, CAT is more efficient than conventional (i.e., fixeditem) tests. It provides more precise measurements for same-length tests or shorter tests for same-precision measurements.
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