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.
The aim of this work is to adapt and test, in a Brazilian public school, the ACE model proposed by Borkulo for evaluating student performance as a teaching-learning process based on computational modeling systems. The ACE model is based on different types of reasoning involving three dimensions. In addition to adapting the model and introducing innovative methodological procedures and instruments for collecting and analyzing data, our main results showed that the ACE model is superior than written tests for discriminating students on the top and bottom of the scale of scientific reasoning abilities, while both instruments are equivalent for evaluating students in the middle of the scale.
Text mining has been used for various purposes, such as document classification and extraction of domain-specific information from text. In this paper we present a study in which text mining methodology and algorithms were properly employed for academic dishonesty (cheating) detection and evaluation on open-ended college exams, based on document classification techniques. Firstly, we propose two classification models for cheating detection by using a decision tree supervised algorithm. Then, both classifiers are compared against the result produced by a domain expert. The results point out that one of the classifiers achieved an excellent quality in detecting and evaluating cheating in exams, making possible its use in real school and college environments.